fix: keep TRUST_REMOTE_CODE=True for built-in models

The constant is the default for built-in models with known model cards,
which are trusted. Custom models added via API already default to
trust_remote_code=False in ModelCard.fetch_from_hf(). The CLI flag
overrides custom models only.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

.cuda_typings/openai_harmony/__init__.pyi

@@ -1,20 +0,0 @@
-from enum import Enum
-
-class HarmonyEncodingName(Enum):
-    HARMONY_GPT_OSS = ...
-
-class HarmonyEncoding: ...
-class HarmonyError(Exception): ...
-
-class Role(Enum):
-    ASSISTANT = ...
-
-class StreamableParser:
-    last_content_delta: str
-    current_channel: str | None
-    current_recipient: str | None
-
-    def __init__(self, encoding: HarmonyEncoding, role: Role = ...) -> None: ...
-    def process(self, token_id: int) -> None: ...
-
-def load_harmony_encoding(name: HarmonyEncodingName) -> HarmonyEncoding: ...

.cuda_typings/pynvml/__init__.pyi

@@ -1,17 +0,0 @@
-class NvmlMemoryInfo:
-    used: int
-    total: int
-    free: int
-
-class NvmlUtilizationRates:
-    gpu: int
-    memory: int
-
-def nvmlInit() -> None: ...
-def nvmlShutdown() -> None: ...
-def nvmlDeviceGetCount() -> int: ...
-def nvmlDeviceGetHandleByIndex(index: int) -> object: ...
-def nvmlDeviceGetUtilizationRates(handle: object) -> NvmlUtilizationRates: ...
-def nvmlDeviceGetTemperature(handle: object, sensor_type: int) -> int: ...
-def nvmlDeviceGetPowerUsage(handle: object) -> int: ...
-def nvmlDeviceGetMemoryInfo(handle: object) -> NvmlMemoryInfo: ...

.cuda_typings/torch/__init__.pyi

@@ -1,61 +0,0 @@
-from typing import Any, Sequence
-
-from torch import backends as backends
-from torch import cuda as cuda
-from torch import distributed as distributed
-
-__version__: str
-
-class version:
-    cuda: str
-
-class dtype: ...
-
-bfloat16: dtype
-float16: dtype
-float32: dtype
-int8: dtype
-int32: dtype
-int64: dtype
-long: dtype
-float8_e4m3fn: dtype
-
-class Tensor:
-    shape: Sequence[int]
-    dtype: dtype
-    def __getitem__(self, key: Any) -> Tensor: ...
-    def __setitem__(self, key: Any, value: Any) -> None: ...
-    def to(self, *args: Any, **kwargs: Any) -> Tensor: ...
-    def cpu(self) -> Tensor: ...
-    def detach(self) -> Tensor: ...
-    def clone(self) -> Tensor: ...
-    def flatten(self, start_dim: int = 0, end_dim: int = -1) -> Tensor: ...
-    def view(self, *shape: Any) -> Tensor: ...
-    def squeeze(self, dim: int = ...) -> Tensor: ...
-    def unsqueeze(self, dim: int) -> Tensor: ...
-    def permute(self, *dims: int) -> Tensor: ...
-    def float(self) -> Tensor: ...
-    def numpy(self) -> Any: ...
-    def numel(self) -> int: ...
-    def nelement(self) -> int: ...
-    @property
-    def is_cuda(self) -> bool: ...
-    @property
-    def device(self) -> device: ...
-    def __len__(self) -> int: ...
-    def data_ptr(self) -> int: ...
-    def tolist(self) -> Any: ...
-    def abs(self) -> Tensor: ...
-    def max(self) -> Tensor: ...
-    def mean(self) -> Tensor: ...
-    def sum(self, dim: int = ...) -> Tensor: ...
-    def item(self) -> float: ...
-
-def tensor(data: Any, dtype: dtype | None = None, device: Any = None) -> Tensor: ...
-def zeros(*size: Any, dtype: dtype | None = None, device: Any = None) -> Tensor: ...
-def empty(*size: Any, dtype: dtype | None = None, device: Any = None) -> Tensor: ...
-def from_numpy(ndarray: Any) -> Tensor: ...
-def inference_mode() -> Any: ...
-
-class device:
-    def __init__(self, type: str, index: int = ...) -> None: ...

.cuda_typings/torch/backends/__init__.pyi

@@ -1 +0,0 @@
-from torch.backends import cuda as cuda

.cuda_typings/torch/backends/cuda/__init__.pyi

@@ -1 +0,0 @@
-def is_built() -> bool: ...

.cuda_typings/torch/cuda/__init__.pyi

@@ -1,10 +0,0 @@
-class _DeviceProperties:
-    total_memory: int
-
-def is_available() -> bool: ...
-def get_device_name(device: int) -> str: ...
-def get_device_properties(device: int) -> _DeviceProperties: ...
-def empty_cache() -> None: ...
-def mem_get_info() -> tuple[int, int]: ...
-def synchronize() -> None: ...
-def max_memory_allocated() -> int: ...

.cuda_typings/torch/distributed/__init__.pyi

@@ -1,2 +0,0 @@
-def is_initialized() -> bool: ...
-def destroy_process_group() -> None: ...

.cuda_typings/vllm/__init__.pyi

@@ -1 +0,0 @@
-__version__: str

.cuda_typings/vllm/config.pyi

@@ -1,2 +0,0 @@
-class ModelConfig:
-    max_model_len: int

.cuda_typings/vllm/engine/arg_utils.pyi

@@ -1,18 +0,0 @@
-from dataclasses import dataclass
-
-@dataclass
-class EngineArgs:
-    model: str = ...
-    served_model_name: str | list[str] | None = ...
-    tokenizer: str | None = ...
-    trust_remote_code: bool = ...
-    dtype: str = ...
-    seed: int = ...
-    max_model_len: int | None = ...
-    gpu_memory_utilization: float = ...
-    enforce_eager: bool = ...
-    tensor_parallel_size: int = ...
-    pipeline_parallel_size: int = ...
-    quantization: str | None = ...
-    load_format: str = ...
-    enable_sleep_mode: bool = ...

.cuda_typings/vllm/outputs.pyi

@@ -1,17 +0,0 @@
-class CompletionOutput:
-    index: int
-    text: str
-    token_ids: list[int]
-    cumulative_logprob: float | None
-    logprobs: object | None
-    finish_reason: str | None
-    stop_reason: int | str | None
-
-    def finished(self) -> bool: ...
-
-class RequestOutput:
-    request_id: str
-    prompt: str | None
-    prompt_token_ids: list[int] | None
-    outputs: list[CompletionOutput]
-    finished: bool

.cuda_typings/vllm/sampling_params.pyi

@@ -1,11 +0,0 @@
-class SamplingParams:
-    n: int
-    temperature: float
-    top_p: float
-    top_k: int
-    min_p: float
-    seed: int | None
-    stop: str | list[str] | None
-    max_tokens: int | None
-    logprobs: int | None
-    repetition_penalty: float

.cuda_typings/vllm/tokenizers/__init__.pyi

@@ -1,3 +0,0 @@
-from vllm.tokenizers.protocol import TokenizerLike
-
-__all__ = ["TokenizerLike"]

.cuda_typings/vllm/tokenizers/protocol.pyi

@@ -1,15 +0,0 @@
-from typing import Protocol
-
-class TokenizerLike(Protocol):
-    @property
-    def eos_token_id(self) -> int: ...
-    @property
-    def vocab_size(self) -> int: ...
-    def encode(self, text: str, add_special_tokens: bool = ...) -> list[int]: ...
-    def decode(self, ids: list[int] | int, skip_special_tokens: bool = ...) -> str: ...
-    def apply_chat_template(
-        self,
-        messages: list[dict[str, str]],
-        tools: list[dict[str, object]] | None = ...,
-        **kwargs: object,
-    ) -> str | list[int]: ...

.cuda_typings/vllm/v1/__init__.pyi

@@ -1 +0,0 @@
-

.cuda_typings/vllm/v1/core/__init__.pyi

@@ -1 +0,0 @@
-

.cuda_typings/vllm/v1/core/kv_cache_manager.pyi

@@ -1,24 +0,0 @@
-from collections.abc import Sequence
-
-from vllm.v1.core.kv_cache_utils import BlockPool, KVCacheBlock
-from vllm.v1.kv_cache_interface import KVCacheConfig
-
-class KVCacheBlocks:
-    blocks: tuple[Sequence[KVCacheBlock], ...]
-    def __init__(self, blocks: tuple[Sequence[KVCacheBlock], ...]) -> None: ...
-    def get_block_ids(self) -> tuple[list[int], ...]: ...
-
-class KVCacheManager:
-    block_pool: BlockPool
-    kv_cache_config: KVCacheConfig
-    enable_caching: bool
-    num_kv_cache_groups: int
-    coordinator: object
-    def __init__(self, *args: object, **kwargs: object) -> None: ...
-    def allocate_slots(
-        self, request: object, num_new_tokens: int, *args: object, **kwargs: object
-    ) -> KVCacheBlocks | None: ...
-    def get_computed_blocks(self, request: object) -> tuple[KVCacheBlocks, int]: ...
-    def create_kv_cache_blocks(
-        self, blocks: tuple[list[KVCacheBlock], ...]
-    ) -> KVCacheBlocks: ...

.cuda_typings/vllm/v1/core/kv_cache_utils.pyi

@@ -1,16 +0,0 @@
-class KVCacheBlock:
-    block_id: int
-    ref_cnt: int
-    def __init__(self, block_id: int) -> None: ...
-
-class FreeKVCacheBlockQueue:
-    def append_n(self, blocks: list[KVCacheBlock]) -> None: ...
-    def popleft_n(self, n: int) -> list[KVCacheBlock]: ...
-
-class BlockPool:
-    blocks: list[KVCacheBlock]
-    free_block_queue: FreeKVCacheBlockQueue
-    num_gpu_blocks: int
-    enable_caching: bool
-    def get_num_free_blocks(self) -> int: ...
-    def get_new_blocks(self, num_blocks: int) -> list[KVCacheBlock]: ...

.cuda_typings/vllm/v1/engine/llm_engine.pyi

@@ -1,22 +0,0 @@
-from vllm.config import ModelConfig
-from vllm.engine.arg_utils import EngineArgs
-from vllm.outputs import RequestOutput
-from vllm.sampling_params import SamplingParams
-from vllm.tokenizers import TokenizerLike
-
-class LLMEngine:
-    tokenizer: TokenizerLike | None
-    model_config: ModelConfig
-
-    @classmethod
-    def from_engine_args(cls, engine_args: EngineArgs) -> LLMEngine: ...
-    def add_request(
-        self,
-        request_id: str,
-        prompt: str,
-        params: SamplingParams,
-        arrival_time: float | None = ...,
-    ) -> None: ...
-    def step(self) -> list[RequestOutput]: ...
-    def has_unfinished_requests(self) -> bool: ...
-    def get_tokenizer(self) -> TokenizerLike: ...

.cuda_typings/vllm/v1/kv_cache_interface.pyi

@@ -1,23 +0,0 @@
-from dataclasses import dataclass
-
-@dataclass
-class KVCacheSpec:
-    block_size: int
-    num_kv_heads: int
-    head_size: int
-
-@dataclass
-class KVCacheGroupSpec:
-    layer_names: list[str]
-    kv_cache_spec: KVCacheSpec
-
-@dataclass
-class KVCacheTensorSpec:
-    shared_by: list[str]
-    size: int
-
-@dataclass
-class KVCacheConfig:
-    num_blocks: int
-    kv_cache_groups: list[KVCacheGroupSpec]
-    kv_cache_tensors: list[KVCacheTensorSpec]

.cuda_typings/vllm/v1/request.pyi

@@ -1,6 +0,0 @@
-class Request:
-    request_id: str
-    prompt_token_ids: list[int] | None
-    num_prompt_tokens: int
-    num_computed_tokens: int
-    num_tokens: int

.cuda_typings/vllm/v1/worker/__init__.pyi

@@ -1 +0,0 @@
-

.cuda_typings/vllm/v1/worker/gpu_model_runner.pyi

@@ -1,24 +0,0 @@
-import torch
-
-class _CompilationConfig:
-    static_forward_context: dict[str, object]
-
-class _ModelConfig:
-    hf_config: object
-
-class GPUModelRunner:
-    kv_caches: list[torch.Tensor]
-    compilation_config: _CompilationConfig
-    model_config: _ModelConfig | None
-    def _allocate_kv_cache_tensors(
-        self, kv_cache_config: object
-    ) -> dict[str, torch.Tensor]: ...
-    def initialize_kv_cache_tensors(
-        self, kv_cache_config: object, kernel_block_sizes: list[int]
-    ) -> dict[str, torch.Tensor]: ...
-    def _reshape_kv_cache_tensors(
-        self,
-        kv_cache_config: object,
-        raw_tensors: dict[str, torch.Tensor],
-        kernel_block_sizes: list[int],
-    ) -> dict[str, torch.Tensor]: ...

.cuda_typings/vllm/v1/worker/gpu_worker.pyi

@@ -1,6 +0,0 @@
-from vllm.v1.worker.gpu_model_runner import GPUModelRunner
-
-class Worker:
-    model_runner: GPUModelRunner
-    def determine_available_memory(self) -> int: ...
-    def initialize_from_config(self, kv_cache_config: object) -> None: ...

.cuda_typings/vllm/v1/worker/utils.pyi

@@ -1 +0,0 @@
-def extract_layer_index(layer_name: str, num_attn_module: int) -> int: ...

.mlx_typings/mlx/nn/layers/convolution.pyi

@@ -32,7 +32,6 @@ class Conv1d(Module):
     """
 
     weight: mx.array
-    bias: mx.array | None
     groups: int
     def __init__(
         self,

.mlx_typings/mlx/nn/layers/linear.pyi

@@ -40,10 +40,6 @@ class Linear(Module):
         bias (bool, optional): If set to ``False`` then the layer will
           not use a bias. Default is ``True``.
     """
-
-    weight: mx.array
-    bias: mx.array | None
-
     def __init__(self, input_dims: int, output_dims: int, bias: bool = ...) -> None: ...
     def __call__(self, x: mx.array) -> mx.array: ...
     def to_quantized(

.mlx_typings/mlx/nn/layers/normalization.pyi

@@ -88,9 +88,6 @@ class RMSNorm(Module):
         dims (int): The feature dimension of the input to normalize over
         eps (float): A small additive constant for numerical stability
     """
-
-    weight: mx.array
-
     def __init__(self, dims: int, eps: float = ...) -> None: ...
     def __call__(self, x) -> mx.array: ...
 

.mlx_typings/mlx_lm/generate.pyi

@@ -73,11 +73,9 @@ class GenerationResponse:
     finish_reason: Optional[str] = ...
 
 def maybe_quantize_kv_cache(
-    prompt_cache: Any,
-    quantized_kv_start: int | None,
-    kv_group_size: int | None,
-    kv_bits: int | None,
-) -> None: ...
+    prompt_cache, quantized_kv_start, kv_group_size, kv_bits
+):  # -> None:
+    ...
 def generate_step(
     prompt: mx.array,
     model: nn.Module,
@@ -254,14 +252,7 @@ class BatchResponse:
 
     texts: List[str]
     stats: BatchStats
-    caches: Optional[List[List[Any]]]
 
-def _left_pad_prompts(prompts: Any, max_length: Optional[int] = ...) -> mx.array: ...
-def _right_pad_prompts(prompts: Any, max_length: Optional[int] = ...) -> mx.array: ...
-def _make_cache(
-    model: Any, left_padding: Any, max_kv_size: Optional[int]
-) -> List[Any]: ...
-def _merge_caches(caches: Any) -> List[Any]: ...
 @dataclass
 class Batch:
     uids: List[int]
@@ -270,71 +261,39 @@ class Batch:
     max_tokens: List[int]
     num_tokens: List[int]
     cache: List[Any]
-    samplers: List[Any]
-    logits_processors: List[Any]
-    tokens: List[mx.array]
-    def __len__(self) -> int: ...
-    def filter(self, keep_idx: List[int]) -> None: ...
-    def extend(self, other: "Batch") -> None: ...
-    def extract_cache(self, idx: int) -> List[Any]: ...
+    def __len__(self):  # -> int:
+        ...
+    def filter(self, keep_idx: List[int]):  # -> None:
+        ...
+    def extend(self, other):  # -> None:
+        ...
 
 class BatchGenerator:
-    model: Any
-    max_kv_size: Optional[int]
-    prefill_step_size: int
-    unprocessed_prompts: List[Any]
-    active_batch: Optional[Batch]
-    prompt_progress_callback: Callable[[List[Tuple[int, int, int]]], None]
-    _stats: BatchStats
-
     @dataclass
     class Response:
         uid: int
         token: int
         logprobs: mx.array
         finish_reason: Optional[str]
-        prompt_cache: Any
 
     def __init__(
         self,
-        model: Any,
+        model,
         max_tokens: int = ...,
-        stop_tokens: Optional[set[int]] = ...,
+        stop_tokens: Optional[set] = ...,
         sampler: Optional[Callable[[mx.array], mx.array]] = ...,
-        logits_processors: Optional[
-            List[Callable[[mx.array, mx.array], mx.array]]
-        ] = ...,
         completion_batch_size: int = ...,
         prefill_batch_size: int = ...,
         prefill_step_size: int = ...,
-        prompt_progress_callback: Optional[
-            Callable[[List[Tuple[int, int, int]]], None]
-        ] = ...,
-        max_kv_size: Optional[int] = ...,
     ) -> None: ...
-    def close(self) -> None: ...
     def insert(
-        self,
-        prompts: Any,
-        max_tokens: Union[List[int], int, None] = ...,
-        caches: Any = ...,
-        samplers: Optional[List[Any]] = ...,
-        logits_processors: Optional[List[Any]] = ...,
-    ) -> List[int]: ...
-    def remove(
-        self, uids: List[int], return_prompt_caches: bool = ...
-    ) -> Optional[dict[int, List[Any]]]: ...
-    def stats(self) -> BatchStats: ...
-    def next(self) -> List[Response]: ...
-    def _process_prompts(self, prompts: List[Any]) -> Batch: ...
-    def _step(
-        self,
-        input_tokens: mx.array,
-        prompt_cache: List[Any],
-        samplers: Optional[List[Any]],
-        logits_processors: Optional[List[Any]],
-        tokens: List[mx.array],
-    ) -> Tuple[mx.array, List[mx.array]]: ...
+        self, prompts, max_tokens: Union[List[int], int, None] = ...
+    ):  # -> list[Any]:
+        ...
+    def stats(self):  # -> BatchStats:
+        ...
+    def next(self):  # -> list[Any]:
+        ...
 
 def batch_generate(
     model,

.mlx_typings/mlx_lm/models/cache.pyi

@@ -16,7 +16,7 @@ class Cache(Protocol):
         self, keys: mx.array, values: mx.array
     ) -> tuple[mx.array, mx.array]: ...
     @property
-    def state(self) -> tuple[mx.array | None, mx.array | None]: ...
+    def state(self) -> tuple[mx.array, mx.array]: ...
     @state.setter
     def state(self, v) -> None: ...
 
@@ -92,14 +92,13 @@ class _BaseCache(Cache):
     values: mx.array
     offset: int
     @property
-    def state(self) -> tuple[mx.array | None, mx.array | None]: ...
+    def state(self) -> tuple[mx.array, mx.array]: ...
     @state.setter
     def state(self, v) -> None: ...
     @property
     def meta_state(self) -> Literal[""]: ...
     @meta_state.setter
     def meta_state(self, v) -> None: ...
-    def trim(self, n: int) -> int: ...
     def is_trimmable(self) -> Literal[False]: ...
     @classmethod
     def from_state(cls, state, meta_state) -> Self: ...
@@ -115,13 +114,15 @@ class ConcatenateKVCache(_BaseCache):
     def update_and_fetch(self, keys, values):  # -> tuple[Any | array, Any | array]:
         ...
     @property
-    def state(self) -> tuple[mx.array | None, mx.array | None]: ...
+    def state(self):  # -> tuple[Any | array | None, Any | array | None]:
+        ...
     @state.setter
     def state(self, v):  # -> None:
         ...
     def is_trimmable(self):  # -> Literal[True]:
         ...
-    def trim(self, n: int) -> int: ...
+    def trim(self, n):  # -> int:
+        ...
     def make_mask(self, *args, **kwargs):  # -> array | Literal['causal'] | None:
         ...
 
@@ -131,7 +132,10 @@ class QuantizedKVCache(_BaseCache):
     def update_and_fetch(self, keys, values):  # -> Any:
         ...
     @property
-    def state(self) -> tuple[mx.array | None, mx.array | None]: ...
+    def state(
+        self,
+    ):  # -> tuple[Any | tuple[array, array, array] | None, Any | tuple[array, array, array] | None] | Any:
+        ...
     @state.setter
     def state(self, v):  # -> None:
         ...
@@ -143,7 +147,8 @@ class QuantizedKVCache(_BaseCache):
         ...
     def is_trimmable(self):  # -> Literal[True]:
         ...
-    def trim(self, n: int) -> int: ...
+    def trim(self, n):  # -> int:
+        ...
     def make_mask(self, *args, **kwargs):  # -> array | Literal['causal'] | None:
         ...
 
@@ -155,30 +160,22 @@ class KVCache(_BaseCache):
     @property
     def state(
         self,
-    ) -> tuple[mx.array | None, mx.array | None]: ...
+    ) -> tuple[array, array]: ...
     @state.setter
     def state(self, v) -> None: ...
     def is_trimmable(self):  # -> Literal[True]:
         ...
-    def trim(self, n: int) -> int: ...
+    def trim(self, n):  # -> int:
+        ...
     def to_quantized(
         self, group_size: int = ..., bits: int = ...
     ) -> QuantizedKVCache: ...
-    def make_mask(
-        self, *args: Any, **kwargs: Any
-    ) -> mx.array | Literal["causal"] | None: ...
+    def make_mask(self, *args, **kwargs):  # -> array | Literal['causal'] | None:
+        ...
 
 class RotatingKVCache(_BaseCache):
     step = ...
-    keys: mx.array | None
-    values: mx.array | None
-    keep: int
-    max_size: int
-    _idx: int
     def __init__(self, max_size, keep=...) -> None: ...
-    def _trim(
-        self, trim_size: int, v: mx.array, append: mx.array | None = ...
-    ) -> mx.array: ...
     def update_and_fetch(
         self, keys, values
     ):  # -> tuple[array | Any, array | Any] | tuple[array | Any, array | Any | None]:
@@ -186,7 +183,8 @@ class RotatingKVCache(_BaseCache):
     @property
     def state(
         self,
-    ) -> tuple[mx.array | None, mx.array | None]: ...
+    ):  # -> tuple[Any | array, Any | array] | tuple[Any | array | None, Any | array | None]:
+        ...
     @state.setter
     def state(self, v):  # -> None:
         ...
@@ -198,7 +196,8 @@ class RotatingKVCache(_BaseCache):
         ...
     def is_trimmable(self):  # -> bool:
         ...
-    def trim(self, n: int) -> int: ...
+    def trim(self, n):  # -> int:
+        ...
     def to_quantized(
         self, group_size: int = ..., bits: int = ...
     ) -> QuantizedKVCache: ...
@@ -213,7 +212,8 @@ class ArraysCache(_BaseCache):
         ...
     def __getitem__(self, idx): ...
     @property
-    def state(self) -> tuple[mx.array | None, mx.array | None]: ...
+    def state(self):  # -> list[Any | array] | list[array]:
+        ...
     @state.setter
     def state(self, v):  # -> None:
         ...
@@ -227,7 +227,8 @@ class ArraysCache(_BaseCache):
         In-place extend this cache with the other cache.
         """
 
-    def make_mask(self, N: int) -> mx.array | None: ...
+    def make_mask(self, N: int):  # -> array | None:
+        ...
 
 class MambaCache(ArraysCache):
     def __init__(self, left_padding: Optional[List[int]] = ...) -> None: ...
@@ -238,7 +239,8 @@ class ChunkedKVCache(KVCache):
         ...
     def update_and_fetch(self, keys, values):  # -> tuple[array, array]:
         ...
-    def trim(self, n: int) -> int: ...
+    def trim(self, n):  # -> int:
+        ...
     @property
     def meta_state(self):  # -> tuple[str, ...]:
         ...
@@ -251,9 +253,10 @@ class CacheList(_BaseCache):
     def __getitem__(self, idx): ...
     def is_trimmable(self):  # -> bool:
         ...
-    def trim(self, n: int) -> int: ...
+    def trim(self, n): ...
     @property
-    def state(self) -> list[tuple[mx.array | None, mx.array | None]]: ...
+    def state(self):  # -> list[Any]:
+        ...
     @state.setter
     def state(self, v):  # -> None:
         ...

.mlx_typings/mlx_lm/models/nemotron_h.pyi

@@ -1,154 +0,0 @@
-from dataclasses import dataclass
-from typing import Any, List, Optional, Tuple
-
-import mlx.core as mx
-import mlx.nn as nn
-
-from .cache import ArraysCache, KVCache
-from .switch_layers import SwitchMLP
-
-@dataclass
-class ModelArgs:
-    model_type: str
-    vocab_size: int
-    hidden_size: int
-    intermediate_size: int
-    num_hidden_layers: int
-    max_position_embeddings: int
-    num_attention_heads: int
-    num_key_value_heads: int
-    attention_bias: bool
-    mamba_num_heads: int
-    mamba_head_dim: int
-    mamba_proj_bias: bool
-    ssm_state_size: int
-    conv_kernel: int
-    n_groups: int
-    mlp_bias: bool
-    layer_norm_epsilon: float
-    use_bias: bool
-    use_conv_bias: bool
-    hybrid_override_pattern: List[str]
-    head_dim: Optional[int]
-    moe_intermediate_size: Optional[int]
-    moe_shared_expert_intermediate_size: Optional[int]
-    n_group: Optional[int]
-    n_routed_experts: Optional[int]
-    n_shared_experts: Optional[int]
-    topk_group: Optional[int]
-    num_experts_per_tok: Optional[int]
-    norm_topk_prob: Optional[bool]
-    routed_scaling_factor: Optional[float]
-    time_step_limit: Optional[Tuple[float, float]]
-    time_step_min: Optional[float]
-    time_step_max: Optional[float]
-
-    @classmethod
-    def from_dict(cls, params: dict[str, Any]) -> ModelArgs: ...
-    def __post_init__(self) -> None: ...
-
-class NemotronHMamba2Mixer(nn.Module):
-    num_heads: int
-    hidden_size: int
-    ssm_state_size: int
-    conv_kernel_size: int
-    intermediate_size: int
-    n_groups: int
-    head_dim: int
-    conv_dim: int
-    conv1d: nn.Conv1d
-    in_proj: nn.Linear
-    dt_bias: mx.array
-    A_log: mx.array
-    D: mx.array
-    norm: nn.RMSNorm
-    heads_per_group: int
-    out_proj: nn.Linear
-
-    def __init__(self, args: ModelArgs) -> None: ...
-    def __call__(
-        self,
-        hidden_states: mx.array,
-        mask: Optional[mx.array],
-        cache: Optional[ArraysCache] = None,
-    ) -> mx.array: ...
-
-class NemotronHAttention(nn.Module):
-    hidden_size: int
-    num_heads: int
-    head_dim: int
-    num_key_value_heads: int
-    scale: float
-    q_proj: nn.Linear
-    k_proj: nn.Linear
-    v_proj: nn.Linear
-    o_proj: nn.Linear
-
-    def __init__(self, args: ModelArgs) -> None: ...
-    def __call__(
-        self,
-        x: mx.array,
-        mask: Optional[mx.array] = None,
-        cache: Optional[KVCache] = None,
-    ) -> mx.array: ...
-
-class NemotronHMLP(nn.Module):
-    up_proj: nn.Linear
-    down_proj: nn.Linear
-
-    def __init__(
-        self, args: ModelArgs, intermediate_size: Optional[int] = None
-    ) -> None: ...
-    def __call__(self, x: mx.array) -> mx.array: ...
-
-class NemotronHMoE(nn.Module):
-    num_experts_per_tok: int
-    switch_mlp: SwitchMLP
-    shared_experts: NemotronHMLP
-
-    def __init__(self, config: ModelArgs) -> None: ...
-    def __call__(self, x: mx.array) -> mx.array: ...
-
-class NemotronHBlock(nn.Module):
-    block_type: str
-    norm: nn.RMSNorm
-    mixer: NemotronHMamba2Mixer | NemotronHAttention | NemotronHMLP | NemotronHMoE
-
-    def __init__(self, args: ModelArgs, block_type: str) -> None: ...
-    def __call__(
-        self,
-        x: mx.array,
-        mask: Optional[mx.array] = None,
-        cache: Optional[Any] = None,
-    ) -> mx.array: ...
-
-class NemotronHModel(nn.Module):
-    embeddings: nn.Embedding
-    layers: list[NemotronHBlock]
-    norm_f: nn.RMSNorm
-    fa_idx: int
-    ssm_idx: int
-
-    def __init__(self, args: ModelArgs) -> None: ...
-    def __call__(
-        self,
-        inputs: mx.array,
-        cache: Optional[Any] = None,
-    ) -> mx.array: ...
-
-class Model(nn.Module):
-    args: ModelArgs
-    backbone: NemotronHModel
-    lm_head: nn.Linear
-    model_type: str
-
-    def __init__(self, args: ModelArgs) -> None: ...
-    def __call__(
-        self,
-        inputs: mx.array,
-        cache: Optional[Any] = None,
-    ) -> mx.array: ...
-    @property
-    def layers(self) -> list[NemotronHBlock]: ...
-    def make_cache(self) -> list[ArraysCache | KVCache]: ...
-    def sanitize(self, weights: dict[str, Any]) -> dict[str, Any]: ...

.mlx_typings/mlx_lm/models/qwen3_5.pyi

@@ -1,153 +0,0 @@
-from dataclasses import dataclass
-from typing import Any, Optional
-
-import mlx.core as mx
-import mlx.nn as nn
-
-from .cache import ArraysCache, KVCache
-from .qwen3_next import (
-    Qwen3NextAttention as Attention,
-    Qwen3NextMLP as MLP,
-    Qwen3NextRMSNormGated as RMSNormGated,
-    Qwen3NextSparseMoeBlock,
-)
-
-SparseMoeBlock = Qwen3NextSparseMoeBlock
-from .switch_layers import SwitchGLU
-
-@dataclass
-class TextModelArgs:
-    model_type: str
-    hidden_size: int
-    intermediate_size: int
-    num_hidden_layers: int
-    num_attention_heads: int
-    rms_norm_eps: float
-    vocab_size: int
-    num_key_value_heads: int
-    max_position_embeddings: int
-    linear_num_value_heads: int
-    linear_num_key_heads: int
-    linear_key_head_dim: int
-    linear_value_head_dim: int
-    linear_conv_kernel_dim: int
-    tie_word_embeddings: bool
-    attention_bias: bool
-    head_dim: Optional[int]
-    full_attention_interval: int
-    num_experts: int
-    num_experts_per_tok: int
-    decoder_sparse_step: int
-    shared_expert_intermediate_size: int
-    moe_intermediate_size: int
-    norm_topk_prob: bool
-    rope_parameters: Optional[dict[str, Any]]
-    partial_rotary_factor: float
-    rope_theta: float
-    rope_scaling: Optional[dict[str, Any]]
-
-    @classmethod
-    def from_dict(cls, params: dict[str, Any]) -> TextModelArgs: ...
-    def __post_init__(self) -> None: ...
-
-class GatedDeltaNet(nn.Module):
-    hidden_size: int
-    num_v_heads: int
-    num_k_heads: int
-    head_k_dim: int
-    head_v_dim: int
-    key_dim: int
-    value_dim: int
-    conv_kernel_size: int
-    conv_dim: int
-    conv1d: nn.Conv1d
-    in_proj_qkv: nn.Linear
-    in_proj_z: nn.Linear
-    in_proj_b: nn.Linear
-    in_proj_a: nn.Linear
-    dt_bias: mx.array
-    A_log: mx.array
-    norm: RMSNormGated
-    out_proj: nn.Linear
-
-    def __init__(self, config: TextModelArgs) -> None: ...
-    def __call__(
-        self,
-        inputs: mx.array,
-        mask: Optional[mx.array] = None,
-        cache: Optional[Any] = None,
-    ) -> mx.array: ...
-
-class DecoderLayer(nn.Module):
-    is_linear: bool
-    linear_attn: GatedDeltaNet
-    self_attn: Attention
-    input_layernorm: nn.RMSNorm
-    post_attention_layernorm: nn.RMSNorm
-    mlp: MLP | SparseMoeBlock
-
-    def __init__(self, args: TextModelArgs, layer_idx: int) -> None: ...
-    def __call__(
-        self,
-        x: mx.array,
-        mask: Optional[mx.array] = None,
-        cache: Optional[Any] = None,
-    ) -> mx.array: ...
-
-class Qwen3_5TextModel(nn.Module):
-    embed_tokens: nn.Embedding
-    layers: list[DecoderLayer]
-    norm: nn.RMSNorm
-    ssm_idx: int
-    fa_idx: int
-
-    def __init__(self, args: TextModelArgs) -> None: ...
-    def __call__(
-        self,
-        inputs: mx.array,
-        cache: Optional[Any] = None,
-        input_embeddings: Optional[mx.array] = None,
-    ) -> mx.array: ...
-
-class TextModel(nn.Module):
-    args: TextModelArgs
-    model_type: str
-    model: Qwen3_5TextModel
-    lm_head: nn.Linear
-
-    def __init__(self, args: TextModelArgs) -> None: ...
-    def __call__(
-        self,
-        inputs: mx.array,
-        cache: Optional[Any] = None,
-        input_embeddings: Optional[mx.array] = None,
-    ) -> mx.array: ...
-    @property
-    def layers(self) -> list[DecoderLayer]: ...
-    def make_cache(self) -> list[ArraysCache | KVCache]: ...
-    def sanitize(self, weights: dict[str, Any]) -> dict[str, Any]: ...
-
-@dataclass
-class ModelArgs:
-    model_type: str
-    text_config: dict[str, Any]
-
-    @classmethod
-    def from_dict(cls, params: dict[str, Any]) -> ModelArgs: ...
-
-class Model(nn.Module):
-    args: ModelArgs
-    model_type: str
-    language_model: TextModel
-
-    def __init__(self, args: ModelArgs) -> None: ...
-    def __call__(
-        self,
-        inputs: mx.array,
-        cache: Optional[Any] = None,
-        input_embeddings: Optional[mx.array] = None,
-    ) -> mx.array: ...
-    def sanitize(self, weights: dict[str, Any]) -> dict[str, Any]: ...
-    @property
-    def layers(self) -> list[DecoderLayer]: ...
-    def make_cache(self) -> list[ArraysCache | KVCache]: ...

.mlx_typings/mlx_lm/models/qwen3_5_moe.pyi

@@ -1,19 +0,0 @@
-from dataclasses import dataclass
-from typing import Any, Optional
-
-import mlx.core as mx
-import mlx.nn as nn
-
-from .cache import ArraysCache, KVCache
-from .qwen3_5 import DecoderLayer, Model as Qwen3_5Model, TextModel
-
-@dataclass
-class ModelArgs:
-    model_type: str
-    text_config: dict[str, Any]
-
-    @classmethod
-    def from_dict(cls, params: dict[str, Any]) -> ModelArgs: ...
-
-class Model(Qwen3_5Model):
-    def sanitize(self, weights: dict[str, Any]) -> dict[str, Any]: ...

.mlx_typings/mlx_lm/models/qwen3_next.pyi

@@ -5,18 +5,8 @@ from typing import Any, Optional
 import mlx.core as mx
 import mlx.nn as nn
 
-from .cache import ArraysCache, KVCache
 from .switch_layers import SwitchGLU
 
-class Qwen3NextRMSNormGated(nn.Module):
-    eps: float
-    weight: mx.array
-
-    def __init__(self, hidden_size: int, eps: float = ...) -> None: ...
-    def __call__(
-        self, hidden_states: mx.array, gate: mx.array | None = None
-    ) -> mx.array: ...
-
 class Qwen3NextMLP(nn.Module):
     gate_proj: nn.Linear
     down_proj: nn.Linear
@@ -100,8 +90,6 @@ class Qwen3NextModel(nn.Module):
     embed_tokens: nn.Embedding
     layers: list[Qwen3NextDecoderLayer]
     norm: nn.RMSNorm
-    ssm_idx: int
-    fa_idx: int
 
     def __init__(self, args: Any) -> None: ...
     def __call__(
@@ -124,4 +112,3 @@ class Model(nn.Module):
     def sanitize(self, weights: dict[str, Any]) -> dict[str, Any]: ...
     @property
     def layers(self) -> list[Qwen3NextDecoderLayer]: ...
-    def make_cache(self) -> list[ArraysCache | KVCache]: ...

.mlx_typings/mlx_lm/models/switch_layers.pyi

@@ -73,9 +73,6 @@ class SwitchGLU(nn.Module):
     def __call__(self, x, indices) -> mx.array: ...
 
 class SwitchMLP(nn.Module):
-    fc1: SwitchLinear
-    fc2: SwitchLinear
-
     def __init__(
         self,
         input_dims: int,

.mlx_typings/mlx_lm/sample_utils.pyi

@@ -48,7 +48,7 @@ def make_logits_processors(
     logit_bias: Optional[Dict[int, float]] = ...,
     repetition_penalty: Optional[float] = ...,
     repetition_context_size: Optional[int] = ...,
-) -> list[Callable[[mx.array, mx.array], mx.array]]:
+):  # -> list[Any]:
     """
     Make logits processors for use with ``generate_step``.
 

.mlx_typings/mlx_lm/tokenizer_utils.pyi

@@ -39,11 +39,11 @@ class StreamingDetokenizer:
     """
 
     __slots__ = ...
-    def reset(self) -> None: ...
-    def add_token(self, token: int) -> None: ...
-    def finalize(self) -> None: ...
+    def reset(self): ...
+    def add_token(self, token): ...
+    def finalize(self): ...
     @property
-    def last_segment(self) -> str:
+    def last_segment(self):
         """Return the last segment of readable text since last time this property was accessed."""
 
 class NaiveStreamingDetokenizer(StreamingDetokenizer):

CONTRIBUTING.md

@@ -39,119 +39,6 @@ Write pure functions where possible. When adding new code, prefer Rust unless th
 
 Run `nix fmt` to auto-format your code before submitting.
 
-## Model Cards
-
-EXO uses TOML-based model cards to define model metadata and capabilities. Model cards are stored in:
-- `resources/inference_model_cards/` for text generation models
-- `resources/image_model_cards/` for image generation models
-- `~/.exo/custom_model_cards/` for user-added custom models
-
-### Adding a Model Card
-
-To add a new model, create a TOML file with the following structure:
-
-```toml
-model_id = "mlx-community/Llama-3.2-1B-Instruct-4bit"
-n_layers = 16
-hidden_size = 2048
-supports_tensor = true
-tasks = ["TextGeneration"]
-family = "llama"
-quantization = "4bit"
-base_model = "Llama 3.2 1B"
-capabilities = ["text"]
-
-[storage_size]
-in_bytes = 729808896
-```
-
-### Required Fields
-
-- `model_id`: Hugging Face model identifier
-- `n_layers`: Number of transformer layers
-- `hidden_size`: Hidden dimension size
-- `supports_tensor`: Whether the model supports tensor parallelism
-- `tasks`: List of supported tasks (`TextGeneration`, `TextToImage`, `ImageToImage`)
-- `family`: Model family (e.g., "llama", "deepseek", "qwen")
-- `quantization`: Quantization level (e.g., "4bit", "8bit", "bf16")
-- `base_model`: Human-readable base model name
-- `capabilities`: List of capabilities (e.g., `["text"]`, `["text", "thinking"]`)
-
-### Optional Fields
-
-- `components`: For multi-component models (like image models with separate text encoders and transformers)
-- `uses_cfg`: Whether the model uses classifier-free guidance (for image models)
-- `trust_remote_code`: Whether to allow remote code execution (defaults to `false` for security)
-
-### Capabilities
-
-The `capabilities` field defines what the model can do:
-- `text`: Standard text generation
-- `thinking`: Model supports chain-of-thought reasoning
-- `thinking_toggle`: Thinking can be enabled/disabled via `enable_thinking` parameter
-- `image_edit`: Model supports image-to-image editing (FLUX.1-Kontext)
-
-### Security Note
-
-By default, `trust_remote_code` is set to `false` for security. Only enable it if the model explicitly requires remote code execution from the Hugging Face hub.
-
-## API Adapters
-
-EXO supports multiple API formats through an adapter pattern. Adapters convert API-specific request formats to the internal `TextGenerationTaskParams` format and convert internal token chunks back to API-specific responses.
-
-### Adapter Architecture
-
-All adapters live in `src/exo/master/adapters/` and follow the same pattern:
-
-1. Convert API-specific requests to `TextGenerationTaskParams`
-2. Handle both streaming and non-streaming response generation
-3. Convert internal `TokenChunk` objects to API-specific formats
-4. Manage error handling and edge cases
-
-### Existing Adapters
-
-- `chat_completions.py`: OpenAI Chat Completions API
-- `claude.py`: Anthropic Claude Messages API
-- `responses.py`: OpenAI Responses API
-- `ollama.py`: Ollama API (for OpenWebUI compatibility)
-
-### Adding a New API Adapter
-
-To add support for a new API format:
-
-1. Create a new adapter file in `src/exo/master/adapters/`
-2. Implement a request conversion function:
-   ```python
-   def your_api_request_to_text_generation(
-       request: YourAPIRequest,
-   ) -> TextGenerationTaskParams:
-       # Convert API request to internal format
-       pass
-   ```
-3. Implement streaming response generation:
-   ```python
-   async def generate_your_api_stream(
-       command_id: CommandId,
-       chunk_stream: AsyncGenerator[TokenChunk | ErrorChunk | ToolCallChunk, None],
-   ) -> AsyncGenerator[str, None]:
-       # Convert internal chunks to API-specific streaming format
-       pass
-   ```
-4. Implement non-streaming response collection:
-   ```python
-   async def collect_your_api_response(
-       command_id: CommandId,
-       chunk_stream: AsyncGenerator[TokenChunk | ErrorChunk | ToolCallChunk, None],
-   ) -> AsyncGenerator[str]:
-       # Collect all chunks and return single response
-       pass
-   ```
-5. Register the adapter endpoints in `src/exo/master/api.py`
-
-The adapter pattern keeps API-specific logic isolated from core inference systems. Internal systems (worker, runner, event sourcing) only see `TextGenerationTaskParams` and `TokenChunk` objects - no API-specific types cross the adapter boundary.
-
-For detailed API documentation, see [docs/api.md](docs/api.md).
-
 ## Testing
 
 EXO relies heavily on manual testing at this point in the project, but this is evolving. Before submitting a change, test both before and after to demonstrate how your change improves behavior. Do the best you can with the hardware you have available - if you need help testing, ask and we'll do our best to assist. Add automated tests where possible - we're actively working to substantially improve our automated testing story.

Cargo.lock

@@ -216,28 +216,6 @@ dependencies = [
  "windows-sys 0.61.2",
 ]
 
-[[package]]
-name = "async-stream"
-version = "0.3.6"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "0b5a71a6f37880a80d1d7f19efd781e4b5de42c88f0722cc13bcb6cc2cfe8476"
-dependencies = [
- "async-stream-impl",
- "futures-core",
- "pin-project-lite",
-]
-
-[[package]]
-name = "async-stream-impl"
-version = "0.3.6"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "c7c24de15d275a1ecfd47a380fb4d5ec9bfe0933f309ed5e705b775596a3574d"
-dependencies = [
- "proc-macro2",
- "quote",
- "syn 2.0.111",
-]
-
 [[package]]
 name = "async-trait"
 version = "0.1.89"
@@ -2781,7 +2759,6 @@ dependencies = [
 name = "networking"
 version = "0.0.1"
 dependencies = [
- "async-stream",
  "delegate",
  "either",
  "extend",
@@ -2790,7 +2767,6 @@ dependencies = [
  "keccak-const",
  "libp2p",
  "log",
- "pin-project",
  "tokio",
  "tracing-subscriber",
  "util",

Cargo.toml

@@ -1,6 +1,10 @@
 [workspace]
 resolver = "3"
-members = ["rust/networking", "rust/exo_pyo3_bindings", "rust/util"]
+members = [
+    "rust/networking",
+    "rust/exo_pyo3_bindings",
+    "rust/util",
+]
 
 [workspace.package]
 version = "0.0.1"
@@ -30,7 +34,6 @@ delegate = "0.13"
 keccak-const = "0.2"
 
 # Async dependencies
-async-stream = "0.3"
 tokio = "1.46"
 futures-lite = "2.6.1"
 futures-timer = "3.0"

README.md

@@ -26,8 +26,6 @@ exo connects all your devices into an AI cluster. Not only does exo enable runni
 - **Topology-Aware Auto Parallel**: exo figures out the best way to split your model across all available devices based on a realtime view of your device topology. It takes into account device resources and network latency/bandwidth between each link.
 - **Tensor Parallelism**: exo supports sharding models, for up to 1.8x speedup on 2 devices and 3.2x speedup on 4 devices.
 - **MLX Support**: exo uses [MLX](https://github.com/ml-explore/mlx) as an inference backend and [MLX distributed](https://ml-explore.github.io/mlx/build/html/usage/distributed.html) for distributed communication.
-- **Multiple API Compatibility**: Compatible with OpenAI Chat Completions API, Claude Messages API, OpenAI Responses API, and Ollama API - use your existing tools and clients.
-- **Custom Model Support**: Load custom models from HuggingFace hub to expand the range of available models.
 
 ## Dashboard
 
@@ -198,8 +196,6 @@ exo follows the [XDG Base Directory Specification](https://specifications.freede
 - **Configuration files**: `~/.config/exo/` (or `$XDG_CONFIG_HOME/exo/`)
 - **Data files**: `~/.local/share/exo/` (or `$XDG_DATA_HOME/exo/`)
 - **Cache files**: `~/.cache/exo/` (or `$XDG_CACHE_HOME/exo/`)
-- **Log files**: `~/.cache/exo/exo_log/` (with automatic log rotation)
-- **Custom model cards**: `~/.local/share/exo/custom_model_cards/`
 
 You can override these locations by setting the corresponding XDG environment variables.
 
@@ -279,47 +275,8 @@ After that, RDMA will be enabled in macOS and exo will take care of the rest.
 
 ---
 
-## Environment Variables
-
-exo supports several environment variables for configuration:
-
-| Variable | Description | Default |
-|----------|-------------|---------|
-| `EXO_MODELS_PATH` | Colon-separated paths to search for pre-downloaded models (e.g., on NFS mounts or shared storage) | None |
-| `EXO_MODELS_DIR` | Directory where exo downloads and stores models | `~/.local/share/exo/models` (Linux) or `~/.exo/models` (macOS) |
-| `EXO_OFFLINE` | Run without internet connection (uses only local models) | `false` |
-| `EXO_ENABLE_IMAGE_MODELS` | Enable image model support | `false` |
-| `EXO_LIBP2P_NAMESPACE` | Custom namespace for cluster isolation | None |
-| `EXO_FAST_SYNCH` | Control MLX_METAL_FAST_SYNCH behavior (for JACCL backend) | Auto |
-| `EXO_TRACING_ENABLED` | Enable distributed tracing for performance analysis | `false` |
-
-**Example usage:**
-
-```bash
-# Use pre-downloaded models from NFS mount
-EXO_MODELS_PATH=/mnt/nfs/models:/opt/ai-models uv run exo
-
-# Run in offline mode
-EXO_OFFLINE=true uv run exo
-
-# Enable image models
-EXO_ENABLE_IMAGE_MODELS=true uv run exo
-
-# Use custom namespace for cluster isolation
-EXO_LIBP2P_NAMESPACE=my-dev-cluster uv run exo
-```
-
----
-
 ### Using the API
 
-exo provides multiple API-compatible interfaces for maximum compatibility with existing tools:
-
-- **OpenAI Chat Completions API** - Compatible with OpenAI clients
-- **Claude Messages API** - Compatible with Anthropic's Claude format
-- **OpenAI Responses API** - Compatible with OpenAI's Responses format
-- **Ollama API** - Compatible with Ollama and tools like OpenWebUI
-
 If you prefer to interact with exo via the API, here is an example creating an instance of a small model (`mlx-community/Llama-3.2-1B-Instruct-4bit`), sending a chat completions request and deleting the instance.
 
 ---
@@ -409,85 +366,14 @@ When you're done, delete the instance by its ID (find it via `/state` or `/insta
 curl -X DELETE http://localhost:52415/instance/YOUR_INSTANCE_ID
 ```
 
-### Claude Messages API Compatibility
-
-Use the Claude Messages API format with the `/v1/messages` endpoint:
-
-```bash
-curl -N -X POST http://localhost:52415/v1/messages \
-  -H 'Content-Type: application/json' \
-  -d '{
-    "model": "mlx-community/Llama-3.2-1B-Instruct-4bit",
-    "messages": [
-      {"role": "user", "content": "Hello"}
-    ],
-    "max_tokens": 1024,
-    "stream": true
-  }'
-```
-
-### OpenAI Responses API Compatibility
-
-Use the OpenAI Responses API format with the `/v1/responses` endpoint:
-
-```bash
-curl -N -X POST http://localhost:52415/v1/responses \
-  -H 'Content-Type: application/json' \
-  -d '{
-    "model": "mlx-community/Llama-3.2-1B-Instruct-4bit",
-    "messages": [
-      {"role": "user", "content": "Hello"}
-    ],
-    "stream": true
-  }'
-```
-
-### Ollama API Compatibility
-
-exo supports Ollama API endpoints for compatibility with tools like OpenWebUI:
-
-```bash
-# Ollama chat
-curl -X POST http://localhost:52415/ollama/api/chat \
-  -H 'Content-Type: application/json' \
-  -d '{
-    "model": "mlx-community/Llama-3.2-1B-Instruct-4bit",
-    "messages": [
-      {"role": "user", "content": "Hello"}
-    ],
-    "stream": false
-  }'
-
-# List models (Ollama format)
-curl http://localhost:52415/ollama/api/tags
-```
-
-### Custom Model Loading from HuggingFace
-
-You can add custom models from the HuggingFace hub:
-
-```bash
-curl -X POST http://localhost:52415/models/add \
-  -H 'Content-Type: application/json' \
-  -d '{
-    "model_id": "mlx-community/my-custom-model"
-  }'
-```
-
-**Security Note:**
-
-Custom models requiring `trust_remote_code` in their configuration must be explicitly enabled (default is false) for security. Only enable this if you trust the model's remote code execution. Models are fetched from HuggingFace and stored locally as custom model cards.
-
 **Other useful API endpoints*:**
 
 - List all models: `curl http://localhost:52415/models`
-- List downloaded models only: `curl http://localhost:52415/models?status=downloaded`
-- Search HuggingFace: `curl "http://localhost:52415/models/search?query=llama&limit=10"`
 - Inspect instance IDs and deployment state: `curl http://localhost:52415/state`
 
 For further details, see:
 
-- API documentation in [docs/api.md](docs/api.md).
+- API basic documentation in [docs/api.md](docs/api.md).
 - API types and endpoints in [src/exo/master/api.py](src/exo/master/api.py).
 
 ---
@@ -546,4 +432,4 @@ On macOS, exo uses the GPU. On Linux, exo currently runs on CPU. We are working
 
 ## Contributing
 
-See [CONTRIBUTING.md](CONTRIBUTING.md) for guidelines on how to contribute to exo.
+See [CONTRIBUTING.md](CONTRIBUTING.md) for guidelines on how to contribute to exo.

app/EXO/EXO/ExoProcessController.swift

@@ -5,7 +5,6 @@ import Foundation
 private let customNamespaceKey = "EXOCustomNamespace"
 private let hfTokenKey = "EXOHFToken"
 private let enableImageModelsKey = "EXOEnableImageModels"
-private let offlineModeKey = "EXOOfflineMode"
 private let onboardingCompletedKey = "EXOOnboardingCompleted"
 
 @MainActor
@@ -61,14 +60,6 @@ final class ExoProcessController: ObservableObject {
             UserDefaults.standard.set(enableImageModels, forKey: enableImageModelsKey)
         }
     }
-    @Published var offlineMode: Bool = {
-        return UserDefaults.standard.bool(forKey: offlineModeKey)
-    }()
-    {
-        didSet {
-            UserDefaults.standard.set(offlineMode, forKey: offlineModeKey)
-        }
-    }
 
     /// Fires once when EXO transitions to `.running` for the very first time (fresh install).
     @Published private(set) var isFirstLaunchReady = false
@@ -276,9 +267,6 @@ final class ExoProcessController: ObservableObject {
         if enableImageModels {
             environment["EXO_ENABLE_IMAGE_MODELS"] = "true"
         }
-        if offlineMode {
-            environment["EXO_OFFLINE"] = "true"
-        }
 
         var paths: [String] = []
         if let existing = environment["PATH"], !existing.isEmpty {

app/EXO/EXO/Views/SettingsView.swift

@@ -13,7 +13,6 @@ struct SettingsView: View {
     @State private var pendingNamespace: String = ""
     @State private var pendingHFToken: String = ""
     @State private var pendingEnableImageModels = false
-    @State private var pendingOfflineMode = false
     @State private var needsRestart = false
     @State private var bugReportInFlight = false
     @State private var bugReportMessage: String?
@@ -43,7 +42,6 @@ struct SettingsView: View {
             pendingNamespace = controller.customNamespace
             pendingHFToken = controller.hfToken
             pendingEnableImageModels = controller.enableImageModels
-            pendingOfflineMode = controller.offlineMode
             needsRestart = false
         }
     }
@@ -74,13 +72,6 @@ struct SettingsView: View {
                     .foregroundColor(.secondary)
             }
 
-            Section {
-                Toggle("Offline Mode", isOn: $pendingOfflineMode)
-                Text("Skip internet checks and use only locally available models.")
-                    .font(.caption)
-                    .foregroundColor(.secondary)
-            }
-
             Section {
                 HStack {
                     Spacer()
@@ -454,7 +445,6 @@ struct SettingsView: View {
 
     private var hasGeneralChanges: Bool {
         pendingNamespace != controller.customNamespace || pendingHFToken != controller.hfToken
-            || pendingOfflineMode != controller.offlineMode
     }
 
     private var hasModelChanges: Bool {
@@ -464,7 +454,6 @@ struct SettingsView: View {
     private func applyGeneralSettings() {
         controller.customNamespace = pendingNamespace
         controller.hfToken = pendingHFToken
-        controller.offlineMode = pendingOfflineMode
         restartIfRunning()
     }
 

bench/bench.toml

@@ -2,4 +2,6 @@
 #
 # Lists the suite files to include. Each file defines benchmarks
 # with shared constraints, topology, and default args.
-include = ["single-m3-ultra.toml"]
+include = [
+    "single-m3-ultra.toml",
+]

bench/eval_configs/models.toml

@@ -1,292 +0,0 @@
-# Model evaluation configurations for exo_eval.
-#
-# Each [[model]] entry uses `patterns` — a list of substrings matched
-# against the model_id. First matching entry wins.
-#
-# Required fields:
-#   name, patterns, reasoning
-#
-# Optional per-model overrides (CLI flags take priority over these):
-#   temperature, top_p, max_tokens, reasoning_effort
-#
-# Fallback defaults (when no per-model config):
-#   reasoning:     temperature=1.0, max_tokens=131072, reasoning_effort="high"
-#   non-reasoning: temperature=0.0, max_tokens=16384
-#
-# All per-model values below are sourced from official model cards,
-# generation_config.json files, and vendor documentation.
-
-# ─── Qwen3.5 (Feb 2026) ─────────────────────────────────────────────
-# Source: HuggingFace model cards (Qwen/Qwen3.5-*)
-# 35B-A3B thinking general: temp=1.0, top_p=0.95, top_k=20
-# 397B thinking: temp=0.6, top_p=0.95, top_k=20
-# Non-thinking: temp=0.7, top_p=0.8, top_k=20
-# max_tokens: 32768 general, 81920 for complex math/code
-
-[[model]]
-name = "Qwen3.5 2B"
-patterns = ["Qwen3.5-2B"]
-reasoning = true
-temperature = 0.6
-top_p = 0.95
-max_tokens = 81920
-
-[[model]]
-name = "Qwen3.5 9B"
-patterns = ["Qwen3.5-9B"]
-reasoning = true
-temperature = 0.6
-top_p = 0.95
-max_tokens = 81920
-
-[[model]]
-name = "Qwen3.5 27B"
-patterns = ["Qwen3.5-27B"]
-reasoning = true
-temperature = 0.6
-top_p = 0.95
-max_tokens = 81920
-
-[[model]]
-name = "Qwen3.5 35B A3B"
-patterns = ["Qwen3.5-35B-A3B"]
-reasoning = true
-temperature = 1.0
-top_p = 0.95
-max_tokens = 81920
-
-[[model]]
-name = "Qwen3.5 122B A10B"
-patterns = ["Qwen3.5-122B-A10B"]
-reasoning = true
-temperature = 0.6
-top_p = 0.95
-max_tokens = 81920
-
-[[model]]
-name = "Qwen3.5 397B A17B"
-patterns = ["Qwen3.5-397B-A17B"]
-reasoning = true
-temperature = 0.6
-top_p = 0.95
-max_tokens = 81920
-
-# ─── Qwen3 (Apr 2025) ───────────────────────────────────────────────
-# Source: HuggingFace model cards (Qwen/Qwen3-*)
-# Thinking: temp=0.6, top_p=0.95, top_k=20
-# Non-thinking: temp=0.7, top_p=0.8, top_k=20
-# max_tokens: 32768 general, 38912 for complex math/code
-
-[[model]]
-name = "Qwen3 0.6B"
-patterns = ["Qwen3-0.6B"]
-reasoning = true
-temperature = 0.6
-top_p = 0.95
-max_tokens = 38912
-
-[[model]]
-name = "Qwen3 30B A3B"
-patterns = ["Qwen3-30B-A3B"]
-reasoning = true
-temperature = 0.6
-top_p = 0.95
-max_tokens = 38912
-
-[[model]]
-name = "Qwen3 235B A22B"
-patterns = ["Qwen3-235B-A22B"]
-reasoning = true
-temperature = 0.6
-top_p = 0.95
-max_tokens = 38912
-
-[[model]]
-name = "Qwen3 Next 80B Thinking"
-patterns = ["Qwen3-Next-80B-A3B-Thinking"]
-reasoning = true
-temperature = 0.6
-top_p = 0.95
-max_tokens = 38912
-
-[[model]]
-name = "Qwen3 Next 80B Instruct"
-patterns = ["Qwen3-Next-80B-A3B-Instruct"]
-reasoning = false
-temperature = 0.7
-top_p = 0.8
-max_tokens = 16384
-
-[[model]]
-name = "Qwen3 Coder 480B"
-patterns = ["Qwen3-Coder-480B"]
-reasoning = false
-temperature = 0.7
-top_p = 0.8
-max_tokens = 16384
-
-[[model]]
-name = "Qwen3 Coder Next"
-patterns = ["Qwen3-Coder-Next"]
-reasoning = false
-temperature = 0.7
-top_p = 0.8
-max_tokens = 16384
-
-# ─── GPT-OSS (OpenAI) ───────────────────────────────────────────────
-# Source: OpenAI GitHub README + HuggingFace discussion #21
-# temp=1.0, top_p=1.0, NO top_k, NO repetition_penalty
-# reasoning_effort supported: low/medium/high
-
-[[model]]
-name = "GPT-OSS 20B"
-patterns = ["gpt-oss-20b"]
-reasoning = true
-temperature = 1.0
-top_p = 1.0
-
-[[model]]
-name = "GPT-OSS 120B"
-patterns = ["gpt-oss-120b"]
-reasoning = true
-temperature = 1.0
-top_p = 1.0
-
-# ─── DeepSeek ────────────────────────────────────────────────────────
-# Source: https://api-docs.deepseek.com/quick_start/parameter_settings
-# Coding/Math: temp=0.0, General: temp=1.3, Creative: temp=1.5
-# NOTE: DeepSeek API applies nonlinear temp mapping. These are API values.
-# When running model directly: API temp 1.0 = model temp 0.3
-# We use temp=0.0 for eval (coding/math focus).
-
-[[model]]
-name = "DeepSeek V3.1"
-patterns = ["DeepSeek-V3.1"]
-reasoning = true
-temperature = 0.0
-
-# ─── GLM (ZhipuAI / THUDM) ──────────────────────────────────────────
-# Source: HuggingFace model cards + generation_config.json + docs.z.ai
-# GLM 4.5+: temp=1.0, top_p=0.95
-# Reasoning tasks: 131072 max_tokens; coding/SWE tasks: temp=0.7
-
-[[model]]
-name = "GLM-5"
-patterns = ["GLM-5"]
-reasoning = true
-temperature = 1.0
-top_p = 0.95
-max_tokens = 131072
-
-[[model]]
-name = "GLM 4.5 Air"
-patterns = ["GLM-4.5-Air"]
-reasoning = true
-temperature = 1.0
-top_p = 0.95
-
-[[model]]
-name = "GLM 4.7"
-patterns = ["GLM-4.7-"]
-reasoning = true
-temperature = 1.0
-top_p = 0.95
-max_tokens = 131072
-# Note: matches both GLM-4.7 and GLM-4.7-Flash
-
-# ─── Kimi (Moonshot AI) ─────────────────────────────────────────────
-# Source: HuggingFace model cards (moonshotai/Kimi-K2-*)
-# K2-Instruct: temp=0.6
-# K2-Thinking: temp=1.0, max_length=262144
-# K2.5: thinking temp=1.0, top_p=0.95; instant temp=0.6, top_p=0.95
-
-[[model]]
-name = "Kimi K2 Thinking"
-patterns = ["Kimi-K2-Thinking"]
-reasoning = true
-temperature = 1.0
-max_tokens = 131072
-
-[[model]]
-name = "Kimi K2.5"
-patterns = ["Kimi-K2.5"]
-reasoning = true
-temperature = 1.0
-top_p = 0.95
-max_tokens = 131072
-
-[[model]]
-name = "Kimi K2 Instruct"
-patterns = ["Kimi-K2-Instruct"]
-reasoning = false
-temperature = 0.6
-
-# ─── MiniMax ─────────────────────────────────────────────────────────
-# Source: HuggingFace model cards + generation_config.json
-# All models: temp=1.0, top_p=0.95, top_k=40
-
-[[model]]
-name = "MiniMax M2.5"
-patterns = ["MiniMax-M2.5"]
-reasoning = true
-temperature = 1.0
-top_p = 0.95
-
-[[model]]
-name = "MiniMax M2.1"
-patterns = ["MiniMax-M2.1"]
-reasoning = true
-temperature = 1.0
-top_p = 0.95
-
-# ─── Step (StepFun) ─────────────────────────────────────────────────
-# Source: HuggingFace model card (stepfun-ai/Step-3.5-Flash)
-# Reasoning: temp=1.0, top_p=0.95
-# General chat: temp=0.6, top_p=0.95
-# We use reasoning settings for eval.
-
-[[model]]
-name = "Step 3.5 Flash"
-patterns = ["Step-3.5-Flash"]
-reasoning = true
-temperature = 1.0
-top_p = 0.95
-
-# ─── Llama (Meta) ───────────────────────────────────────────────────
-# Source: generation_config.json + meta-llama/llama-models generation.py
-# All variants: temp=0.6, top_p=0.9
-
-[[model]]
-name = "Llama 3.2 1B"
-patterns = ["Llama-3.2-1B"]
-reasoning = false
-temperature = 0.6
-top_p = 0.9
-
-[[model]]
-name = "Llama 3.2 3B"
-patterns = ["Llama-3.2-3B"]
-reasoning = false
-temperature = 0.6
-top_p = 0.9
-
-[[model]]
-name = "Llama 3.1 8B"
-patterns = ["Llama-3.1-8B", "Meta-Llama-3.1-8B"]
-reasoning = false
-temperature = 0.6
-top_p = 0.9
-
-[[model]]
-name = "Llama 3.1 70B"
-patterns = ["Llama-3.1-70B", "Meta-Llama-3.1-70B"]
-reasoning = false
-temperature = 0.6
-top_p = 0.9
-
-[[model]]
-name = "Llama 3.3 70B"
-patterns = ["Llama-3.3-70B", "llama-3.3-70b"]
-reasoning = false
-temperature = 0.6
-top_p = 0.9

bench/eval_tool_calls.py

@@ -17,13 +17,11 @@ from harness import (
     ExoClient,
     ExoHttpError,
     add_common_instance_args,
-    ensure_cuda_available,
     instance_id_from_instance,
     nodes_used_in_instance,
     resolve_model_short_id,
     run_planning_phase,
     settle_and_fetch_placements,
-    validate_vllm_args,
     wait_for_instance_gone,
     wait_for_instance_ready,
 )
@@ -935,7 +933,6 @@ Examples:
         help="Write JSON results to stdout instead of file",
     )
     args = parser.parse_args()
-    validate_vllm_args(args)
 
     all_scenarios = load_scenarios(SCENARIOS_PATH)
     if args.scenarios:
@@ -955,8 +952,6 @@ Examples:
 
     log = sys.stderr if args.stdout else sys.stdout
     exo = ExoClient(args.host, args.port, timeout_s=args.timeout)
-    if args.ensure_cuda:
-        ensure_cuda_available(exo)
     _short_id, full_model_id = resolve_model_short_id(exo, args.model)
 
     selected = settle_and_fetch_placements(

bench/exo_bench.py

@@ -24,7 +24,6 @@ import json
 import sys
 import time
 from collections.abc import Callable
-from concurrent.futures import ThreadPoolExecutor, as_completed
 from pathlib import Path
 from statistics import mean
 from typing import Any
@@ -33,13 +32,11 @@ from harness import (
     ExoClient,
     ExoHttpError,
     add_common_instance_args,
-    ensure_cuda_available,
     instance_id_from_instance,
     nodes_used_in_instance,
     resolve_model_short_id,
     run_planning_phase,
     settle_and_fetch_placements,
-    validate_vllm_args,
     wait_for_instance_gone,
     wait_for_instance_ready,
 )
@@ -255,12 +252,6 @@ def main() -> int:
     ap.add_argument(
         "--repeat", type=int, default=1, help="Repetitions per (pp,tg) pair."
     )
-    ap.add_argument(
-        "--concurrency",
-        nargs="+",
-        default=["1"],
-        help="Concurrency levels (ints). Accepts commas. E.g. --concurrency 1,2,4,8. Default 1.",
-    )
     ap.add_argument(
         "--warmup",
         type=int,
@@ -282,7 +273,6 @@ def main() -> int:
         help="Force all pp×tg combinations (cartesian product) even when lists have equal length.",
     )
     args = ap.parse_args()
-    validate_vllm_args(args)
 
     pp_list = parse_int_list(args.pp)
     tg_list = parse_int_list(args.tg)
@@ -292,10 +282,6 @@ def main() -> int:
     if args.repeat <= 0:
         logger.error("--repeat must be >= 1")
         return 2
-    concurrency_list = parse_int_list(args.concurrency)
-    if not concurrency_list or any(c <= 0 for c in concurrency_list):
-        logger.error("--concurrency values must be >= 1")
-        return 2
 
     # Log pairing mode
     use_combinations = args.all_combinations or len(pp_list) != len(tg_list)
@@ -307,11 +293,7 @@ def main() -> int:
         logger.info(f"pp/tg mode: tandem (zip) - {len(pp_list)} pairs")
 
     client = ExoClient(args.host, args.port, timeout_s=args.timeout)
-    if args.ensure_cuda:
-        ensure_cuda_available(client)
-    short_id, full_model_id = resolve_model_short_id(
-        client, args.model, force_download=args.force_download
-    )
+    short_id, full_model_id = resolve_model_short_id(client, args.model)
 
     tokenizer = load_tokenizer_for_bench(full_model_id)
     if tokenizer is None:
@@ -410,131 +392,52 @@ def main() -> int:
                 pp_tg_pairs = list(zip(pp_list, tg_list, strict=True))
 
             for pp, tg in pp_tg_pairs:
-                for concurrency in concurrency_list:
-                    logger.info(f"--- pp={pp} tg={tg} concurrency={concurrency} ---")
-                    runs: list[dict[str, Any]] = []
-                    for r in range(args.repeat):
-                        time.sleep(3)
-
-                        if concurrency <= 1:
-                            # Sequential: single request
-                            try:
-                                row, actual_pp_tokens = run_one_completion(
-                                    client, full_model_id, pp, tg, prompt_sizer
-                                )
-                            except Exception as e:
-                                logger.error(e)
-                                continue
-                            row.update(
-                                {
-                                    "model_short_id": short_id,
-                                    "model_id": full_model_id,
-                                    "placement_sharding": sharding,
-                                    "placement_instance_meta": instance_meta,
-                                    "placement_nodes": n_nodes,
-                                    "instance_id": instance_id,
-                                    "pp_tokens": actual_pp_tokens,
-                                    "tg": tg,
-                                    "repeat_index": r,
-                                    "concurrency": 1,
-                                    **(
-                                        {"download_duration_s": download_duration_s}
-                                        if download_duration_s is not None
-                                        else {}
-                                    ),
-                                }
-                            )
-                            runs.append(row)
-                            all_rows.append(row)
-                        else:
-                            # Concurrent: fire N requests in parallel
-                            # Each thread gets its own ExoClient (separate HTTP connection)
-                            batch_results: list[tuple[dict[str, Any], int]] = []
-                            batch_errors = 0
-
-                            def _run_concurrent(
-                                idx: int, *, _pp: int = pp, _tg: int = tg
-                            ) -> tuple[dict[str, Any], int]:
-                                c = ExoClient(
-                                    args.host, args.port, timeout_s=args.timeout
-                                )
-                                return run_one_completion(
-                                    c, full_model_id, _pp, _tg, prompt_sizer
-                                )
-
-                            with ThreadPoolExecutor(max_workers=concurrency) as pool:
-                                futures = {
-                                    pool.submit(_run_concurrent, i): i
-                                    for i in range(concurrency)
-                                }
-                                for fut in as_completed(futures):
-                                    try:
-                                        batch_results.append(fut.result())
-                                    except Exception as e:
-                                        logger.error(f"Concurrent request failed: {e}")
-                                        batch_errors += 1
-
-                            for idx, (row, actual_pp_tokens) in enumerate(
-                                batch_results
-                            ):
-                                row.update(
-                                    {
-                                        "model_short_id": short_id,
-                                        "model_id": full_model_id,
-                                        "placement_sharding": sharding,
-                                        "placement_instance_meta": instance_meta,
-                                        "placement_nodes": n_nodes,
-                                        "instance_id": instance_id,
-                                        "pp_tokens": actual_pp_tokens,
-                                        "tg": tg,
-                                        "repeat_index": r,
-                                        "concurrency": concurrency,
-                                        "concurrent_index": idx,
-                                        **(
-                                            {"download_duration_s": download_duration_s}
-                                            if download_duration_s is not None
-                                            else {}
-                                        ),
-                                    }
-                                )
-                                runs.append(row)
-                                all_rows.append(row)
-
-                            if batch_results:
-                                valid_gen_tps = [
-                                    x["stats"]["generation_tps"]
-                                    for x, _ in batch_results
-                                    if x["stats"]["generation_tps"] > 0
-                                ]
-                                agg_gen_tps = (
-                                    mean(valid_gen_tps) if valid_gen_tps else 0.0
-                                )
-                                gen_tps = agg_gen_tps / concurrency
-                                logger.info(
-                                    f"[concurrent {concurrency}x]  "
-                                    f"agg_gen_tps={agg_gen_tps:.2f}  "
-                                    f"gen_tps={gen_tps:.2f}  "
-                                    f"errors={batch_errors}"
-                                )
-
-                    if runs:
-                        prompt_tps = mean(x["stats"]["prompt_tps"] for x in runs)
-                        gen_tps = mean(
-                            x["stats"]["generation_tps"] / x["concurrency"]
-                            for x in runs
-                        )
-                        ptok = mean(x["stats"]["prompt_tokens"] for x in runs)
-                        gtok = mean(x["stats"]["generation_tokens"] for x in runs)
-                        peak = mean(
-                            x["stats"]["peak_memory_usage"]["inBytes"] for x in runs
+                runs: list[dict[str, Any]] = []
+                for r in range(args.repeat):
+                    time.sleep(3)
+                    try:
+                        row, actual_pp_tokens = run_one_completion(
+                            client, full_model_id, pp, tg, prompt_sizer
                         )
+                    except Exception as e:
+                        logger.error(e)
+                        continue
+                    row.update(
+                        {
+                            "model_short_id": short_id,
+                            "model_id": full_model_id,
+                            "placement_sharding": sharding,
+                            "placement_instance_meta": instance_meta,
+                            "placement_nodes": n_nodes,
+                            "instance_id": instance_id,
+                            "pp_tokens": actual_pp_tokens,
+                            "tg": tg,
+                            "repeat_index": r,
+                            **(
+                                {"download_duration_s": download_duration_s}
+                                if download_duration_s is not None
+                                else {}
+                            ),
+                        }
+                    )
+                    runs.append(row)
+                    all_rows.append(row)
 
-                        logger.info(
-                            f"prompt_tps={prompt_tps:.2f} gen_tps={gen_tps:.2f}    "
-                            f"prompt_tokens={ptok} gen_tokens={gtok}    "
-                            f"peak_memory={format_peak_memory(peak)}\n"
-                        )
-                    time.sleep(2)
+                if runs:
+                    prompt_tps = mean(x["stats"]["prompt_tps"] for x in runs)
+                    gen_tps = mean(x["stats"]["generation_tps"] for x in runs)
+                    ptok = mean(x["stats"]["prompt_tokens"] for x in runs)
+                    gtok = mean(x["stats"]["generation_tokens"] for x in runs)
+                    peak = mean(
+                        x["stats"]["peak_memory_usage"]["inBytes"] for x in runs
+                    )
+
+                    logger.info(
+                        f"prompt_tps={prompt_tps:.2f} gen_tps={gen_tps:.2f}    "
+                        f"prompt_tokens={ptok} gen_tokens={gtok}    "
+                        f"peak_memory={format_peak_memory(peak)}\n"
+                    )
+                time.sleep(2)
         finally:
             try:
                 client.request_json("DELETE", f"/instance/{instance_id}")

bench/harness.py

@@ -69,10 +69,6 @@ class ExoClient:
     def post_bench_chat_completions(self, payload: dict[str, Any]) -> dict[str, Any]:
         return self.request_json("POST", "/bench/chat/completions", body=payload)
 
-    def post_bench_disaggregated(self, payload: dict[str, Any]) -> dict[str, Any]:
-        payload["disaggregated"] = True
-        return self.request_json("POST", "/bench/chat/completions", body=payload)
-
 
 def unwrap_instance(instance: dict[str, Any]) -> dict[str, Any]:
     if len(instance) != 1:
@@ -169,9 +165,7 @@ def wait_for_instance_gone(
     raise TimeoutError(f"Instance {instance_id} did not get deleted within {timeout=}")
 
 
-def resolve_model_short_id(
-    client: ExoClient, model_arg: str, *, force_download: bool = False
-) -> tuple[str, str]:
+def resolve_model_short_id(client: ExoClient, model_arg: str) -> tuple[str, str]:
     models = client.request_json("GET", "/models") or {}
     data = models.get("data") or []
 
@@ -187,44 +181,9 @@ def resolve_model_short_id(
             full_id = str(m["hugging_face_id"])
             return short_id, full_id
 
-    if force_download and "/" in model_arg:
-        logger.info(f"Model not in /models, adding from HuggingFace: {model_arg}")
-        result = client.request_json(
-            "POST", "/models/add", body={"model_id": model_arg}
-        )
-        if result:
-            short_id = str(result.get("name") or model_arg.rsplit("/", 1)[-1])
-            full_id = str(result.get("hugging_face_id") or model_arg)
-            return short_id, full_id
-
     raise ValueError(f"Model not found in /models: {model_arg}")
 
 
-def validate_vllm_args(args: argparse.Namespace) -> None:
-    if args.instance_meta != "vllm":
-        return
-    if args.sharding == "tensor":
-        raise SystemExit(
-            "--instance-meta vllm is incompatible with --sharding tensor (vllm is pipeline-only)"
-        )
-    if args.min_nodes > 1:
-        raise SystemExit(
-            "--instance-meta vllm is incompatible with --min-nodes > 1 (vllm is single-node)"
-        )
-    if args.max_nodes > 1:
-        raise SystemExit(
-            "--instance-meta vllm is incompatible with --max-nodes > 1 (vllm is single-node)"
-        )
-
-
-def ensure_cuda_available(client: ExoClient) -> None:
-    capabilities = client.request_json("GET", "/capabilities")
-    if not capabilities or not capabilities.get("vllm_available"):
-        raise SystemExit(
-            "--ensure-cuda: vllm is not available on the exo cluster (no CUDA capability)"
-        )
-
-
 def placement_filter(instance_meta: str, wanted: str) -> bool:
     s = (instance_meta or "").lower()
     if wanted == "both":
@@ -486,11 +445,6 @@ def add_common_instance_args(ap: argparse.ArgumentParser) -> None:
         "--port", type=int, default=int(os.environ.get("EXO_PORT", "52415"))
     )
     ap.add_argument("--model", required=True, help="Model short id or huggingface id")
-    ap.add_argument(
-        "--force-download",
-        action="store_true",
-        help="If model not in /models, add it from HuggingFace via exo and download.",
-    )
     ap.add_argument(
         "--max-nodes",
         type=int,
@@ -504,7 +458,7 @@ def add_common_instance_args(ap: argparse.ArgumentParser) -> None:
         help="Only consider placements using >= this many nodes.",
     )
     ap.add_argument(
-        "--instance-meta", choices=["ring", "jaccl", "vllm", "both"], default="both"
+        "--instance-meta", choices=["ring", "jaccl", "both"], default="both"
     )
     ap.add_argument(
         "--sharding", choices=["pipeline", "tensor", "both"], default="both"
@@ -533,8 +487,3 @@ def add_common_instance_args(ap: argparse.ArgumentParser) -> None:
         action="store_true",
         help="Delete existing models from smallest to largest to make room for benchmark model.",
     )
-    ap.add_argument(
-        "--ensure-cuda",
-        action="store_true",
-        help="Verify the exo cluster has CUDA/vllm capability; error if not.",
-    )

bench/parallel_requests.py

@@ -1,255 +0,0 @@
-# type: ignore
-import argparse
-import asyncio
-import sys
-import termios
-import time
-import tty
-
-import aiohttp
-
-NUM_REQUESTS = 10
-BASE_URL = ""
-
-QUESTIONS = [
-    "What is the capital of Australia?",
-    "How many bones are in the human body?",
-    "What year did World War II end?",
-    "What is the speed of light in meters per second?",
-    "Who wrote Romeo and Juliet?",
-    "What is the chemical formula for water?",
-    "How many planets are in our solar system?",
-    "What is the largest ocean on Earth?",
-    "Who painted the Mona Lisa?",
-    "What is the boiling point of water in Celsius?",
-]
-
-
-def write(s: str) -> None:
-    sys.stdout.write(s)
-
-
-# ---------------------------------------------------------------------------
-# Model picker (same style as exo_eval)
-# ---------------------------------------------------------------------------
-
-
-def fetch_models() -> list[str]:
-    import json
-    import urllib.request
-
-    with urllib.request.urlopen(f"{BASE_URL}/state") as resp:
-        data = json.loads(resp.read())
-    model_ids: set[str] = set()
-    for instance in data.get("instances", {}).values():
-        for variant in instance.values():
-            sa = variant.get("shardAssignments", {})
-            model_id = sa.get("modelId")
-            if model_id:
-                model_ids.add(model_id)
-    return sorted(model_ids)
-
-
-def pick_model() -> str | None:
-    models = fetch_models()
-    if not models:
-        print("No models found.")
-        return None
-
-    cursor = 0
-    total_lines = len(models) + 4
-
-    def render(first: bool = False) -> None:
-        if not first:
-            write(f"\033[{total_lines}A")
-        write("\033[J")
-        write("\033[1mSelect model\033[0m (up/down, enter confirm, q quit)\r\n\r\n")
-        for i, model in enumerate(models):
-            line = f"  {'>' if i == cursor else ' '} {model}"
-            write(f"\033[7m{line}\033[0m\r\n" if i == cursor else f"{line}\r\n")
-        write("\r\n")
-        sys.stdout.flush()
-
-    fd = sys.stdin.fileno()
-    old = termios.tcgetattr(fd)
-    try:
-        tty.setraw(fd)
-        write("\033[?25l")
-        render(first=True)
-        while True:
-            ch = sys.stdin.read(1)
-            if ch in ("q", "\x03"):
-                write("\033[?25h\033[0m\r\n")
-                return None
-            elif ch in ("\r", "\n"):
-                break
-            elif ch == "\x1b":
-                seq = sys.stdin.read(2)
-                if seq == "[A":
-                    cursor = (cursor - 1) % len(models)
-                elif seq == "[B":
-                    cursor = (cursor + 1) % len(models)
-            render()
-    finally:
-        termios.tcsetattr(fd, termios.TCSADRAIN, old)
-        write(f"\033[{total_lines}A\033[J")  # clear picker UI
-        write("\033[?25h\033[0m")
-        sys.stdout.flush()
-
-    return models[cursor]
-
-
-# ---------------------------------------------------------------------------
-# Parallel requests
-# ---------------------------------------------------------------------------
-
-statuses: list[str] = []
-times: list[str] = []
-previews: list[str] = []
-tokens: list[str] = []
-full_responses: list[dict | None] = []
-total_lines = 0
-start_time: float = 0
-selected_model: str = ""
-
-
-def render_progress(first: bool = False) -> None:
-    if not first:
-        write(f"\033[{total_lines}A")
-    write("\033[J")
-    elapsed = time.monotonic() - start_time if start_time else 0
-    done = sum(1 for s in statuses if s == "done")
-    write(
-        f"\033[1m{selected_model}\033[0m  [{done}/{NUM_REQUESTS}]  {elapsed:.1f}s\r\n\r\n"
-    )
-
-    for i in range(NUM_REQUESTS):
-        q = QUESTIONS[i % len(QUESTIONS)]
-        status = statuses[i]
-        if status == "pending":
-            color = "\033[33m"  # yellow
-        elif status == "running":
-            color = "\033[36m"  # cyan
-        elif status == "done":
-            color = "\033[32m"  # green
-        else:
-            color = "\033[31m"  # red
-        write(
-            f"  {i:>2}  {color}{status:<8}\033[0m  {times[i]:>6}  {tokens[i]:>5}tok  {q[:40]:<40}  {previews[i][:50]}\r\n"
-        )
-
-    write("\r\n")
-    sys.stdout.flush()
-
-
-async def send_request(
-    session: aiohttp.ClientSession, i: int, lock: asyncio.Lock
-) -> None:
-    payload = {
-        "model": selected_model,
-        "messages": [{"role": "user", "content": QUESTIONS[i % len(QUESTIONS)]}],
-        "max_tokens": 1024,
-    }
-    statuses[i] = "running"
-    async with lock:
-        render_progress()
-    t0 = time.monotonic()
-    try:
-        async with session.post(
-            f"{BASE_URL}/v1/chat/completions", json=payload
-        ) as resp:
-            data = await resp.json()
-            elapsed = time.monotonic() - t0
-            full_responses[i] = data
-            times[i] = f"{elapsed:.1f}s"
-            if resp.status == 200:
-                choice = data["choices"][0]
-                msg = choice["message"]
-                content = msg.get("content", "")
-                previews[i] = content[:50].replace("\n", " ") or "(empty)"
-                if "usage" in data:
-                    tokens[i] = str(data["usage"].get("total_tokens", ""))
-                statuses[i] = "done"
-            else:
-                statuses[i] = f"err:{resp.status}"
-                previews[i] = str(data.get("error", {}).get("message", ""))[:50]
-    except Exception as e:
-        elapsed = time.monotonic() - t0
-        times[i] = f"{elapsed:.1f}s"
-        statuses[i] = "error"
-        previews[i] = str(e)[:50]
-    async with lock:
-        render_progress()
-
-
-async def run_requests(print_stdout: bool = False) -> None:
-    global start_time, total_lines, statuses, times, previews, tokens, full_responses
-
-    statuses = ["pending"] * NUM_REQUESTS
-    times = ["-"] * NUM_REQUESTS
-    previews = ["-"] * NUM_REQUESTS
-    tokens = ["-"] * NUM_REQUESTS
-    full_responses = [None] * NUM_REQUESTS
-    total_lines = NUM_REQUESTS + 4
-
-    write("\033[?25l")  # hide cursor
-    start_time = time.monotonic()
-    render_progress(first=True)
-    lock = asyncio.Lock()
-    try:
-        async with aiohttp.ClientSession() as session:
-            tasks = [send_request(session, i, lock) for i in range(NUM_REQUESTS)]
-            await asyncio.gather(*tasks)
-        total = time.monotonic() - start_time
-        write(
-            f"\033[1m=== All {NUM_REQUESTS} requests done in {total:.1f}s ===\033[0m\r\n\r\n"
-        )
-
-        if print_stdout:
-            for i in range(NUM_REQUESTS):
-                data = full_responses[i]
-                if not data or "choices" not in data:
-                    continue
-                choice = data["choices"][0]
-                msg = choice["message"]
-                q = QUESTIONS[i % len(QUESTIONS)]
-                write(f"\033[1m--- #{i}: {q} ---\033[0m\r\n")
-                if msg.get("reasoning_content"):
-                    write(f"\033[2m[Thinking]: {msg['reasoning_content']}\033[0m\r\n")
-                write(f"{msg.get('content', '')}\r\n")
-                if "usage" in data:
-                    u = data["usage"]
-                    write(
-                        f"\033[2m[Usage: prompt={u.get('prompt_tokens')}, "
-                        f"completion={u.get('completion_tokens')}, "
-                        f"total={u.get('total_tokens')}]\033[0m\r\n"
-                    )
-                write("\r\n")
-    finally:
-        write("\033[?25h")  # show cursor
-        sys.stdout.flush()
-
-
-def main() -> None:
-    global selected_model, BASE_URL
-    parser = argparse.ArgumentParser(
-        description="Send parallel requests to an exo cluster"
-    )
-    parser.add_argument(
-        "--host", required=True, help="Hostname of the exo node (e.g. s1)"
-    )
-    parser.add_argument("--port", type=int, default=52415, help="Port (default: 52415)")
-    parser.add_argument(
-        "--stdout", action="store_true", help="Print full responses after completion"
-    )
-    args = parser.parse_args()
-    BASE_URL = f"http://{args.host}:{args.port}"
-    model = pick_model()
-    if not model:
-        return
-    selected_model = model
-    asyncio.run(run_requests(print_stdout=args.stdout))
-
-
-if __name__ == "__main__":
-    main()

bench/prefill_decode_bench.py

@@ -1,399 +0,0 @@
-# type: ignore
-#!/usr/bin/env python3
-"""Disaggregated prefill-decode benchmark for exo.
-
-Measures throughput when a vLLM node handles prefill (KV generation + transfer)
-and an MLX node handles decode (token generation).
-
-Requires a cluster with at least one CUDA/vLLM node and one Apple Silicon/MLX node.
-
-Usage:
-    uv run python prefill_decode_bench.py --model <decode-model> --pp 2048,8192 --tg 128
-    uv run python prefill_decode_bench.py --model <decode-model> --prefill-model <vllm-model> --pp 4096 --tg 128
-    uv run python prefill_decode_bench.py --model <model-id> --pp 2048 --tg 128 --no-overlapping
-    uv run python prefill_decode_bench.py --model <model-id> --pp 2048 --tg 128 --dry-run
-"""
-
-from __future__ import annotations
-
-import argparse
-import contextlib
-import itertools
-import json
-import sys
-import time
-from statistics import mean
-from typing import Any
-
-from exo_bench import (
-    PromptSizer,
-    format_peak_memory,
-    load_tokenizer_for_bench,
-    parse_int_list,
-)
-from harness import (
-    ExoClient,
-    ExoHttpError,
-    add_common_instance_args,
-    instance_id_from_instance,
-    nodes_used_in_instance,
-    resolve_model_short_id,
-    run_planning_phase,
-    settle_and_fetch_placements,
-    wait_for_instance_gone,
-    wait_for_instance_ready,
-)
-from loguru import logger
-
-
-def find_vllm_placement(placements: list[dict[str, Any]]) -> dict[str, Any] | None:
-    for p in placements:
-        if "vllm" in str(p.get("instance_meta", "")).lower():
-            return p
-    return None
-
-
-def find_mlx_placement(
-    placements: list[dict[str, Any]], decode_meta: str
-) -> dict[str, Any] | None:
-    for p in placements:
-        meta = str(p.get("instance_meta", "")).lower()
-        if decode_meta == "both":
-            if "ring" in meta or "jaccl" in meta:
-                return p
-        elif decode_meta in meta:
-            return p
-    return None
-
-
-def run_one_disaggregated(
-    client: ExoClient,
-    model_id: str,
-    pp_hint: int,
-    tg: int,
-    prompt_sizer: PromptSizer,
-) -> tuple[dict[str, Any], int]:
-    content, pp_tokens = prompt_sizer.build(pp_hint)
-    payload: dict[str, Any] = {
-        "model": model_id,
-        "messages": [{"role": "user", "content": content}],
-        "stream": False,
-        "max_tokens": tg,
-    }
-
-    t0 = time.perf_counter()
-    out = client.post_bench_disaggregated(payload)
-    elapsed = time.perf_counter() - t0
-
-    stats = out.get("generation_stats")
-
-    choices = out.get("choices") or [{}]
-    message = choices[0].get("message", {}) if choices else {}
-    text = message.get("content") or ""
-    preview = text[:200] if text else ""
-
-    return {
-        "elapsed_s": elapsed,
-        "output_text_preview": preview,
-        "stats": stats,
-    }, pp_tokens
-
-
-def main() -> int:
-    ap = argparse.ArgumentParser(
-        prog="prefill-decode-bench",
-        description="Benchmark disaggregated prefill-decode (vLLM prefill → MLX decode).",
-    )
-    add_common_instance_args(ap)
-    ap.add_argument(
-        "--pp",
-        nargs="+",
-        required=True,
-        help="Prompt-size hints (ints, must be >1000). Accepts commas.",
-    )
-    ap.add_argument(
-        "--tg",
-        nargs="+",
-        required=True,
-        help="Generation lengths (ints). Accepts commas.",
-    )
-    ap.add_argument(
-        "--repeat", type=int, default=1, help="Repetitions per (pp,tg) pair."
-    )
-    ap.add_argument(
-        "--warmup",
-        type=int,
-        default=0,
-        help="Warmup runs per placement (uses first pp/tg).",
-    )
-    ap.add_argument(
-        "--prefill-model",
-        default=None,
-        help="Model for the vLLM prefill node (defaults to --model if not set).",
-    )
-    ap.add_argument(
-        "--no-overlapping",
-        action="store_true",
-        help="Use batch KV transfer instead of streaming (overlapping).",
-    )
-    ap.add_argument(
-        "--decode-instance-meta",
-        choices=["ring", "jaccl", "both"],
-        default="ring",
-        help="Instance meta for the decode (MLX) node.",
-    )
-    ap.add_argument(
-        "--json-out",
-        default="bench/prefill_decode_results.json",
-        help="Write raw per-run results JSON to this path.",
-    )
-    ap.add_argument("--stdout", action="store_true", help="Write results to stdout")
-    ap.add_argument(
-        "--dry-run", action="store_true", help="List selected placements and exit."
-    )
-    ap.add_argument(
-        "--all-combinations",
-        action="store_true",
-        help="Force all pp×tg combinations (cartesian product) even when lists have equal length.",
-    )
-    args = ap.parse_args()
-
-    pp_list = parse_int_list(args.pp)
-    tg_list = parse_int_list(args.tg)
-    if not pp_list or not tg_list:
-        logger.error("pp and tg lists must be non-empty")
-        return 2
-    for pp in pp_list:
-        if pp <= 1000:
-            logger.error(f"pp={pp} must be >1000 (remote prefill requires >1000 uncached tokens)")
-            return 2
-    if args.repeat <= 0:
-        logger.error("--repeat must be >= 1")
-        return 2
-
-    use_combinations = args.all_combinations or len(pp_list) != len(tg_list)
-    if use_combinations:
-        logger.info(f"pp/tg mode: combinations (product) - {len(pp_list) * len(tg_list)} pairs")
-    else:
-        logger.info(f"pp/tg mode: tandem (zip) - {len(pp_list)} pairs")
-
-    overlapping = not args.no_overlapping
-    logger.info(f"KV transfer mode: {'overlapping (streaming)' if overlapping else 'batch'}")
-
-    client = ExoClient(args.host, args.port, timeout_s=args.timeout)
-
-    decode_short_id, decode_full_id = resolve_model_short_id(
-        client, args.model, force_download=args.force_download
-    )
-
-    prefill_model_arg = args.prefill_model or args.model
-    if args.prefill_model:
-        prefill_short_id, prefill_full_id = resolve_model_short_id(
-            client, args.prefill_model, force_download=args.force_download
-        )
-    else:
-        prefill_short_id, prefill_full_id = decode_short_id, decode_full_id
-
-    tokenizer = load_tokenizer_for_bench(decode_full_id)
-    if tokenizer is None:
-        raise RuntimeError("[prefill-decode-bench] tokenizer load failed")
-
-    try:
-        prompt_sizer = PromptSizer(tokenizer)
-    except Exception:
-        logger.error("[prefill-decode-bench] tokenizer usable but prompt sizing failed")
-        raise
-
-    original_instance_meta = args.instance_meta
-    original_max_nodes = args.max_nodes
-
-    args.instance_meta = "vllm"
-    args.min_nodes = 1
-    args.max_nodes = 1
-    vllm_placements = settle_and_fetch_placements(
-        client, prefill_full_id, args, settle_timeout=args.settle_timeout
-    )
-
-    args.instance_meta = args.decode_instance_meta
-    args.min_nodes = 1
-    args.max_nodes = original_max_nodes
-    mlx_placements = settle_and_fetch_placements(
-        client, decode_full_id, args, settle_timeout=args.settle_timeout
-    )
-
-    args.instance_meta = original_instance_meta
-
-    vllm_placement = find_vllm_placement(vllm_placements)
-    mlx_placement = find_mlx_placement(mlx_placements, args.decode_instance_meta)
-
-    if not vllm_placement:
-        logger.error("No vLLM placement found. Need a CUDA node for prefill.")
-        return 1
-    if not mlx_placement:
-        logger.error(f"No MLX ({args.decode_instance_meta}) placement found for decode.")
-        return 1
-
-    vllm_instance = vllm_placement["instance"]
-    mlx_instance = mlx_placement["instance"]
-    vllm_instance_id = instance_id_from_instance(vllm_instance)
-    mlx_instance_id = instance_id_from_instance(mlx_instance)
-    vllm_meta = str(vllm_placement.get("instance_meta", ""))
-    mlx_meta = str(mlx_placement.get("instance_meta", ""))
-    mlx_sharding = str(mlx_placement.get("sharding", ""))
-    mlx_nodes = nodes_used_in_instance(mlx_instance)
-
-    logger.info("=" * 80)
-    logger.info(f"PREFILL (vLLM):  {vllm_meta} / {prefill_short_id} ({prefill_full_id}) / instance_id={vllm_instance_id}")
-    logger.info(f"DECODE  (MLX):   {mlx_meta} / {mlx_sharding} / nodes={mlx_nodes} / {decode_short_id} ({decode_full_id}) / instance_id={mlx_instance_id}")
-    logger.info(f"Overlapping: {overlapping}")
-
-    if args.dry_run:
-        return 0
-
-    settle_deadline = (
-        time.monotonic() + args.settle_timeout if args.settle_timeout > 0 else None
-    )
-
-    logger.info("Planning phase: checking downloads...")
-    download_duration_s = run_planning_phase(
-        client,
-        prefill_full_id,
-        vllm_placement,
-        args.danger_delete_downloads,
-        args.timeout,
-        settle_deadline,
-    )
-    download_duration_mlx = run_planning_phase(
-        client,
-        decode_full_id,
-        mlx_placement,
-        args.danger_delete_downloads,
-        args.timeout,
-        settle_deadline,
-    )
-    if download_duration_s is not None:
-        logger.info(f"Download (vLLM): {download_duration_s:.1f}s")
-    if download_duration_mlx is not None:
-        logger.info(f"Download (MLX): {download_duration_mlx:.1f}s")
-
-    # Create vLLM instance first (starts prefill server)
-    logger.info("Creating vLLM (prefill) instance...")
-    client.request_json("POST", "/instance", body={"instance": vllm_instance})
-    try:
-        wait_for_instance_ready(client, vllm_instance_id)
-    except (RuntimeError, TimeoutError) as e:
-        logger.error(f"Failed to initialize vLLM instance: {e}")
-        with contextlib.suppress(ExoHttpError):
-            client.request_json("DELETE", f"/instance/{vllm_instance_id}")
-        return 1
-    logger.info("vLLM (prefill) instance ready")
-
-    # Create MLX instance (decode)
-    logger.info("Creating MLX (decode) instance...")
-    client.request_json("POST", "/instance", body={"instance": mlx_instance})
-    try:
-        wait_for_instance_ready(client, mlx_instance_id)
-    except (RuntimeError, TimeoutError) as e:
-        logger.error(f"Failed to initialize MLX instance: {e}")
-        with contextlib.suppress(ExoHttpError):
-            client.request_json("DELETE", f"/instance/{mlx_instance_id}")
-        with contextlib.suppress(ExoHttpError):
-            client.request_json("DELETE", f"/instance/{vllm_instance_id}")
-        return 1
-    logger.info("MLX (decode) instance ready")
-
-    time.sleep(2)
-
-    all_rows: list[dict[str, Any]] = []
-
-    try:
-        for i in range(args.warmup):
-            run_one_disaggregated(
-                client, decode_full_id, pp_list[0], tg_list[0], prompt_sizer
-            )
-            logger.debug(f"  warmup {i + 1}/{args.warmup} done")
-
-        if use_combinations:
-            pp_tg_pairs = list(itertools.product(pp_list, tg_list))
-        else:
-            pp_tg_pairs = list(zip(pp_list, tg_list, strict=True))
-
-        for pp, tg in pp_tg_pairs:
-            logger.info(f"--- pp={pp} tg={tg} ---")
-            runs: list[dict[str, Any]] = []
-            for r in range(args.repeat):
-                time.sleep(3)
-
-                try:
-                    row, actual_pp_tokens = run_one_disaggregated(
-                        client, decode_full_id, pp, tg, prompt_sizer
-                    )
-                except Exception as e:
-                    logger.error(e)
-                    continue
-
-                row.update(
-                    {
-                        "prefill_model_short_id": prefill_short_id,
-                        "prefill_model_id": prefill_full_id,
-                        "prefill_instance_meta": vllm_meta,
-                        "prefill_instance_id": vllm_instance_id,
-                        "decode_model_short_id": decode_short_id,
-                        "decode_model_id": decode_full_id,
-                        "decode_instance_meta": mlx_meta,
-                        "decode_sharding": mlx_sharding,
-                        "decode_nodes": mlx_nodes,
-                        "decode_instance_id": mlx_instance_id,
-                        "overlapping": overlapping,
-                        "pp_tokens": actual_pp_tokens,
-                        "tg": tg,
-                        "repeat_index": r,
-                    }
-                )
-                runs.append(row)
-                all_rows.append(row)
-
-            if runs:
-                prompt_tps = mean(x["stats"]["prompt_tps"] for x in runs)
-                gen_tps = mean(x["stats"]["generation_tps"] for x in runs)
-                ptok = mean(x["stats"]["prompt_tokens"] for x in runs)
-                gtok = mean(x["stats"]["generation_tokens"] for x in runs)
-                peak = mean(
-                    x["stats"]["peak_memory_usage"]["inBytes"] for x in runs
-                )
-                avg_elapsed = mean(x["elapsed_s"] for x in runs)
-
-                logger.info(
-                    f"prompt_tps={prompt_tps:.2f} gen_tps={gen_tps:.2f}    "
-                    f"prompt_tokens={ptok} gen_tokens={gtok}    "
-                    f"peak_memory={format_peak_memory(peak)}    "
-                    f"avg_elapsed={avg_elapsed:.2f}s\n"
-                )
-            time.sleep(2)
-    finally:
-        try:
-            client.request_json("DELETE", f"/instance/{mlx_instance_id}")
-        except ExoHttpError as e:
-            if e.status != 404:
-                raise
-        try:
-            client.request_json("DELETE", f"/instance/{vllm_instance_id}")
-        except ExoHttpError as e:
-            if e.status != 404:
-                raise
-        wait_for_instance_gone(client, mlx_instance_id)
-        wait_for_instance_gone(client, vllm_instance_id)
-        logger.debug("Deleted both instances")
-
-    if args.stdout:
-        json.dump(all_rows, sys.stdout, indent=2, ensure_ascii=False)
-    elif args.json_out:
-        with open(args.json_out, "w", encoding="utf-8") as f:
-            json.dump(all_rows, f, indent=2, ensure_ascii=False)
-        logger.debug(f"\nWrote results JSON: {args.json_out}")
-
-    return 0
-
-
-if __name__ == "__main__":
-    sys.exit(main())

bench/pyproject.toml

@@ -4,18 +4,13 @@ version = "0.1.0"
 description = "Benchmarking tool for exo distributed inference"
 requires-python = ">=3.13"
 dependencies = [
-  "httpx>=0.27.0",
-  "loguru>=0.7.3",
-  "transformers>=5.0.0",
-  "huggingface-hub>=0.33.4",
-  "tiktoken>=0.12.0",
-  "jinja2>=3.1.0",
-  "protobuf>=5.29.0",
-  "datasets>=2.0.0",
-  "math-verify>=0.7.0",
-  "lm-eval[api,math]>=0.4.0",
-  "human-eval>=1.0.3",
-  "numpy>=1.24.0",
+    "httpx>=0.27.0",
+    "loguru>=0.7.3",
+    "transformers>=5.0.0",
+    "huggingface-hub>=0.33.4",
+    "tiktoken>=0.12.0",
+    "jinja2>=3.1.0",
+    "protobuf>=5.29.0",
 ]
 
 [build-system]

bench/single-m3-ultra.toml

@@ -2,10 +2,10 @@
 #
 # Shared constraints applied to ALL benchmarks in this file.
 constraints = [
-  "All(MacOsBuild(=25D125))",
-  "Hosts(=1)",
-  "All(Chip(m3_ultra))",
-  "All(GpuCores(=80))",
+    "All(MacOsBuild(=25D125))",
+    "Hosts(=1)",
+    "All(Chip(m3_ultra))",
+    "All(GpuCores(=80))",
 ]
 
 [topology]

bench/test_mlx_bandwidth.py

@@ -1,377 +0,0 @@
-# type: ignore
-import argparse
-import json
-import os
-import statistics
-import sys
-import tempfile
-import time
-
-import mlx.core as mx
-
-DTYPE_MAP = {
-    "float32": (mx.float32, 4),
-    "float16": (mx.float16, 2),
-    "bfloat16": (mx.bfloat16, 2),
-}
-
-SIZES = [
-    1 * 1024,
-    4 * 1024,
-    16 * 1024,
-    64 * 1024,
-    256 * 1024,
-    1 * 1024 * 1024,
-    4 * 1024 * 1024,
-    16 * 1024 * 1024,
-    64 * 1024 * 1024,
-    256 * 1024 * 1024,
-    1 * 1024 * 1024 * 1024,
-    2 * 1024 * 1024 * 1024,
-    4 * 1024 * 1024 * 1024,
-    8 * 1024 * 1024 * 1024,
-]
-
-
-def format_bytes(n: int) -> str:
-    if n >= 1024 * 1024 * 1024:
-        return f"{n / (1024 * 1024 * 1024):.0f} GB"
-    if n >= 1024 * 1024:
-        return f"{n / (1024 * 1024):.0f} MB"
-    if n >= 1024:
-        return f"{n / 1024:.0f} KB"
-    return f"{n} B"
-
-
-def format_time(seconds: float) -> str:
-    if seconds >= 1.0:
-        return f"{seconds:.3f} s"
-    if seconds >= 0.001:
-        return f"{seconds * 1000:.2f} ms"
-    return f"{seconds * 1_000_000:.1f} us"
-
-
-def format_bandwidth(bytes_per_sec: float) -> str:
-    if bytes_per_sec >= 1024 * 1024 * 1024:
-        return f"{bytes_per_sec / (1024 * 1024 * 1024):.2f} GB/s"
-    if bytes_per_sec >= 1024 * 1024:
-        return f"{bytes_per_sec / (1024 * 1024):.1f} MB/s"
-    return f"{bytes_per_sec / 1024:.1f} KB/s"
-
-
-def barrier(group: mx.distributed.Group) -> None:
-    mx.eval(mx.distributed.all_sum(mx.array(1.0), group=group))
-
-
-def init_ring(
-    rank: int, self_ip: str, peer_ip: str, port: int, tmpdir: str
-) -> mx.distributed.Group:
-    if rank == 0:
-        hosts = [f"{self_ip}:{port}", f"{peer_ip}:{port}"]
-    else:
-        hosts = [f"{peer_ip}:{port}", f"{self_ip}:{port}"]
-
-    hostfile = os.path.join(tmpdir, "hosts.json")
-    with open(hostfile, "w") as f:
-        json.dump(hosts, f)
-
-    for var in ("MLX_HOSTFILE", "MLX_RANK", "MLX_IBV_DEVICES", "MLX_JACCL_COORDINATOR"):
-        os.environ.pop(var, None)
-
-    os.environ["MLX_HOSTFILE"] = hostfile
-    os.environ["MLX_RANK"] = str(rank)
-    return mx.distributed.init(backend="ring", strict=True)
-
-
-def init_jaccl(
-    rank: int, interface: str, coordinator: str, port: int, tmpdir: str
-) -> mx.distributed.Group:
-    devices = [[None, interface], [interface, None]]
-    devfile = os.path.join(tmpdir, "devices.json")
-    with open(devfile, "w") as f:
-        json.dump(devices, f)
-
-    for var in ("MLX_HOSTFILE", "MLX_RANK", "MLX_IBV_DEVICES", "MLX_JACCL_COORDINATOR"):
-        os.environ.pop(var, None)
-
-    os.environ["MLX_IBV_DEVICES"] = devfile
-    os.environ["MLX_RANK"] = str(rank)
-    if rank == 0:
-        os.environ["MLX_JACCL_COORDINATOR"] = f"0.0.0.0:{port}"
-    else:
-        os.environ["MLX_JACCL_COORDINATOR"] = coordinator
-
-    return mx.distributed.init(backend="jaccl", strict=True)
-
-
-def bench_unidirectional(
-    group: mx.distributed.Group,
-    rank: int,
-    size_bytes: int,
-    dtype: mx.Dtype,
-    element_size: int,
-    warmup: int,
-    iterations: int,
-) -> list[float]:
-    n_elements = size_bytes // element_size
-    tensor = mx.random.normal(shape=(n_elements,)).astype(dtype)
-    mx.eval(tensor)
-
-    for _ in range(warmup):
-        if rank == 0:
-            sent = mx.distributed.send(tensor, dst=1, group=group)
-            mx.eval(sent)
-        else:
-            received = mx.distributed.recv_like(tensor, src=0, group=group)
-            mx.eval(received)
-        barrier(group)
-
-    times: list[float] = []
-    for _ in range(iterations):
-        barrier(group)
-        t0 = time.perf_counter()
-        if rank == 0:
-            sent = mx.distributed.send(tensor, dst=1, group=group)
-            mx.eval(sent)
-        else:
-            received = mx.distributed.recv_like(tensor, src=0, group=group)
-            mx.eval(received)
-        barrier(group)
-        t1 = time.perf_counter()
-        times.append(t1 - t0)
-
-    return times
-
-
-def bench_rtt(
-    group: mx.distributed.Group,
-    rank: int,
-    size_bytes: int,
-    dtype: mx.Dtype,
-    element_size: int,
-    warmup: int,
-    iterations: int,
-) -> list[float]:
-    n_elements = size_bytes // element_size
-    tensor = mx.random.normal(shape=(n_elements,)).astype(dtype)
-    mx.eval(tensor)
-
-    for _ in range(warmup):
-        if rank == 0:
-            sent = mx.distributed.send(tensor, dst=1, group=group)
-            mx.eval(sent)
-            received = mx.distributed.recv_like(tensor, src=1, group=group)
-            mx.eval(received)
-        else:
-            received = mx.distributed.recv_like(tensor, src=0, group=group)
-            mx.eval(received)
-            sent = mx.distributed.send(received, dst=0, group=group)
-            mx.eval(sent)
-        barrier(group)
-
-    times: list[float] = []
-    for _ in range(iterations):
-        barrier(group)
-        t0 = time.perf_counter()
-        if rank == 0:
-            sent = mx.distributed.send(tensor, dst=1, group=group)
-            mx.eval(sent)
-            received = mx.distributed.recv_like(tensor, src=1, group=group)
-            mx.eval(received)
-        else:
-            received = mx.distributed.recv_like(tensor, src=0, group=group)
-            mx.eval(received)
-            sent = mx.distributed.send(received, dst=0, group=group)
-            mx.eval(sent)
-        barrier(group)
-        t1 = time.perf_counter()
-        times.append(t1 - t0)
-
-    return times
-
-
-def bench_all_gather(
-    group: mx.distributed.Group,
-    rank: int,
-    size_bytes: int,
-    dtype: mx.Dtype,
-    element_size: int,
-    warmup: int,
-    iterations: int,
-) -> list[float]:
-    n_elements = (size_bytes // 2) // element_size
-    tensor = mx.random.normal(shape=(n_elements,)).astype(dtype)
-    mx.eval(tensor)
-
-    for _ in range(warmup):
-        gathered = mx.distributed.all_gather(tensor, group=group)
-        mx.eval(gathered)
-        barrier(group)
-
-    times: list[float] = []
-    for _ in range(iterations):
-        barrier(group)
-        t0 = time.perf_counter()
-        gathered = mx.distributed.all_gather(tensor, group=group)
-        mx.eval(gathered)
-        t1 = time.perf_counter()
-        times.append(t1 - t0)
-
-    return times
-
-
-def print_table(title: str, rows: list[dict[str, str]]) -> None:
-    print(f"\n=== {title} ===")
-    headers = ["Size", "Median", "Min", "Max", "Bandwidth"]
-    widths = [
-        max(len(h), max((len(r[h]) for r in rows), default=0)) + 2 for h in headers
-    ]
-    header_line = "".join(h.ljust(w) for h, w in zip(headers, widths, strict=True))
-    print(header_line)
-    print("-" * len(header_line))
-    for row in rows:
-        print("".join(row[h].ljust(w) for h, w in zip(headers, widths, strict=True)))
-
-
-def run_bench(
-    name: str,
-    bench_fn,
-    group: mx.distributed.Group,
-    rank: int,
-    dtype: mx.Dtype,
-    element_size: int,
-    warmup: int,
-    iterations: int,
-    bw_multiplier: int = 1,
-) -> None:
-    rows: list[dict[str, str]] = []
-    for size in SIZES:
-        if rank == 0:
-            print(f"  {name}: {format_bytes(size)}...", end="", flush=True)
-        times = bench_fn(group, rank, size, dtype, element_size, warmup, iterations)
-        if rank == 0:
-            med = statistics.median(times)
-            mn = min(times)
-            mx_ = max(times)
-            bw = (size * bw_multiplier) / med
-            rows.append(
-                {
-                    "Size": format_bytes(size),
-                    "Median": format_time(med),
-                    "Min": format_time(mn),
-                    "Max": format_time(mx_),
-                    "Bandwidth": format_bandwidth(bw),
-                }
-            )
-            print(f" {format_bandwidth(bw)}")
-    if rank == 0:
-        print_table(name, rows)
-
-
-def parse_args() -> argparse.Namespace:
-    parser = argparse.ArgumentParser(
-        description="MLX Distributed Communication Benchmark"
-    )
-    subparsers = parser.add_subparsers(dest="backend", required=True)
-
-    ring_parser = subparsers.add_parser("ring")
-    ring_parser.add_argument("--rank", type=int, required=True, choices=[0, 1])
-    ring_parser.add_argument("--self-ip", required=True)
-    ring_parser.add_argument("--peer-ip", required=True)
-    ring_parser.add_argument("--port", type=int, default=5555)
-
-    jaccl_parser = subparsers.add_parser("jaccl")
-    jaccl_parser.add_argument("--rank", type=int, required=True, choices=[0, 1])
-    jaccl_parser.add_argument("--interface", required=True)
-    jaccl_parser.add_argument(
-        "--coordinator",
-        type=str,
-        default=None,
-        help="IP:PORT of rank 0 (required for rank 1)",
-    )
-    jaccl_parser.add_argument(
-        "--port", type=int, default=9999, help="Coordinator port (rank 0 only)"
-    )
-
-    for p in [ring_parser, jaccl_parser]:
-        p.add_argument("--warmup", type=int, default=3)
-        p.add_argument("--iterations", type=int, default=10)
-        p.add_argument("--dtype", choices=list(DTYPE_MAP.keys()), default="float32")
-
-    args = parser.parse_args()
-
-    if args.backend == "jaccl" and args.rank == 1 and args.coordinator is None:
-        jaccl_parser.error("--coordinator is required for rank 1")
-
-    return args
-
-
-def main() -> int:
-    args = parse_args()
-    dtype, element_size = DTYPE_MAP[args.dtype]
-
-    with tempfile.TemporaryDirectory() as tmpdir:
-        if args.backend == "ring":
-            print(f"Initializing ring backend (rank {args.rank})...")
-            group = init_ring(args.rank, args.self_ip, args.peer_ip, args.port, tmpdir)
-        else:
-            print(f"Initializing jaccl backend (rank {args.rank})...")
-            group = init_jaccl(
-                args.rank, args.interface, args.coordinator or "", args.port, tmpdir
-            )
-
-        print(f"Rank {group.rank()} of {group.size()} initialized")
-        barrier(group)
-
-        if args.rank == 0:
-            print("\nMLX Distributed Communication Benchmark")
-            print(
-                f"Backend: {args.backend} | Dtype: {args.dtype} | Warmup: {args.warmup} | Iterations: {args.iterations}"
-            )
-
-        run_bench(
-            "Unidirectional (rank 0 -> rank 1)",
-            bench_unidirectional,
-            group,
-            args.rank,
-            dtype,
-            element_size,
-            args.warmup,
-            args.iterations,
-        )
-        run_bench(
-            "Round-Trip (ping-pong)",
-            bench_rtt,
-            group,
-            args.rank,
-            dtype,
-            element_size,
-            args.warmup,
-            args.iterations,
-            bw_multiplier=2,
-        )
-        run_bench(
-            "All-Gather",
-            bench_all_gather,
-            group,
-            args.rank,
-            dtype,
-            element_size,
-            args.warmup,
-            args.iterations,
-        )
-
-        if args.rank == 0:
-            print("\nDone.")
-        else:
-            print("Rank 1 complete.")
-
-    return 0
-
-
-if __name__ == "__main__":
-    try:
-        sys.exit(main())
-    except KeyboardInterrupt:
-        print("\nInterrupted.")
-        sys.exit(1)

bench/vendor/lcb_testing_util.py

@@ -1,593 +0,0 @@
-# type: ignore
-# Vendored from LiveCodeBench (https://github.com/LiveCodeBench/LiveCodeBench)
-# File: lcb_runner/evaluation/testing_util.py
-# License: MIT
-# Vendored 2026-03-07 — do not modify without updating from upstream.
-
-import ast
-import faulthandler
-import json
-import platform
-
-# to run the solution files we're using a timing based approach
-import signal
-import sys
-import time
-
-# used for debugging to time steps
-from datetime import datetime
-from decimal import Decimal
-from enum import Enum
-from io import StringIO
-
-# from pyext import RuntimeModule
-from types import ModuleType
-
-# used for testing the code that reads from input
-from unittest.mock import mock_open, patch
-
-import_string = "from string import *\nfrom re import *\nfrom datetime import *\nfrom collections import *\nfrom heapq import *\nfrom bisect import *\nfrom copy import *\nfrom math import *\nfrom random import *\nfrom statistics import *\nfrom itertools import *\nfrom functools import *\nfrom operator import *\nfrom io import *\nfrom sys import *\nfrom json import *\nfrom builtins import *\nfrom typing import *\nimport string\nimport re\nimport datetime\nimport collections\nimport heapq\nimport bisect\nimport copy\nimport math\nimport random\nimport statistics\nimport itertools\nimport functools\nimport operator\nimport io\nimport sys\nimport json\nsys.setrecursionlimit(50000)\n"
-
-
-def truncatefn(s, length=300):
-    if isinstance(s, str):
-        pass
-    else:
-        s = str(s)
-    if len(s) <= length:
-        return s
-
-    return s[: length // 2] + "...(truncated) ..." + s[-length // 2 :]
-
-
-class CODE_TYPE(Enum):
-    call_based = 0
-    standard_input = 1
-
-
-# stuff for setting up signal timer
-class TimeoutException(Exception):
-    pass
-
-
-def timeout_handler(signum, frame):
-    print("timeout occured: alarm went off")
-    raise TimeoutException
-
-
-# used to capture stdout as a list
-# from https://stackoverflow.com/a/16571630/6416660
-# alternative use redirect_stdout() from contextlib
-class Capturing(list):
-    def __enter__(self):
-        self._stdout = sys.stdout
-        sys.stdout = self._stringio = StringIO()
-        # Make closing the StringIO a no-op
-        self._stringio.close = lambda x: 1
-        return self
-
-    def __exit__(self, *args):
-        self.append(self._stringio.getvalue())
-        del self._stringio  # free up some memory
-        sys.stdout = self._stdout
-
-
-# Custom mock for sys.stdin that supports buffer attribute
-class MockStdinWithBuffer:
-    def __init__(self, inputs: str):
-        self.inputs = inputs
-        self._stringio = StringIO(inputs)
-        self.buffer = MockBuffer(inputs)
-
-    def read(self, *args):
-        return self.inputs
-
-    def readline(self, *args):
-        return self._stringio.readline(*args)
-
-    def readlines(self, *args):
-        return self.inputs.split("\n")
-
-    def __getattr__(self, name):
-        # Delegate other attributes to StringIO
-        return getattr(self._stringio, name)
-
-
-class MockBuffer:
-    def __init__(self, inputs: str):
-        self.inputs = inputs.encode("utf-8")  # Convert to bytes
-
-    def read(self, *args):
-        # Return as byte strings that can be split
-        return self.inputs
-
-    def readline(self, *args):
-        return self.inputs.split(b"\n")[0] + b"\n"
-
-
-def clean_if_name(code: str) -> str:
-    try:
-        astree = ast.parse(code)
-        last_block = astree.body[-1]
-        if isinstance(last_block, ast.If):
-            condition = last_block.test
-            if ast.unparse(condition).strip() == "__name__ == '__main__'":
-                code = (
-                    ast.unparse(astree.body[:-1]) + "\n" + ast.unparse(last_block.body)  # type: ignore
-                )
-    except:
-        pass
-
-    return code
-
-
-def make_function(code: str) -> str:
-    try:
-        import_stmts = []
-        all_other_stmts = []
-        astree = ast.parse(code)
-        for stmt in astree.body:
-            if isinstance(stmt, (ast.Import, ast.ImportFrom)):
-                import_stmts.append(stmt)
-            else:
-                all_other_stmts.append(stmt)
-
-        function_ast = ast.FunctionDef(
-            name="wrapped_function",
-            args=ast.arguments(
-                posonlyargs=[], args=[], kwonlyargs=[], kw_defaults=[], defaults=[]
-            ),
-            body=all_other_stmts,
-            decorator_list=[],
-            lineno=-1,
-        )
-        main_code = (
-            import_string
-            + "\n"
-            + ast.unparse(import_stmts)
-            + "\n"
-            + ast.unparse(function_ast)
-        )
-        return main_code
-    except Exception:
-        return code
-
-
-def call_method(method, inputs):
-    if isinstance(inputs, list):
-        inputs = "\n".join(inputs)
-
-    inputs_line_iterator = iter(inputs.split("\n"))
-
-    # Create custom stdin mock with buffer support
-    mock_stdin = MockStdinWithBuffer(inputs)
-
-    # sys.setrecursionlimit(10000)
-
-    # @patch('builtins.input', side_effect=inputs.split("\n"))
-    @patch("builtins.open", mock_open(read_data=inputs))
-    @patch("sys.stdin", mock_stdin)  # Use our custom mock instead of StringIO
-    @patch("sys.stdin.readline", lambda *args: next(inputs_line_iterator))
-    @patch("sys.stdin.readlines", lambda *args: inputs.split("\n"))
-    @patch("sys.stdin.read", lambda *args: inputs)
-    # @patch('sys.stdout.write', print)
-    def _inner_call_method(_method):
-        try:
-            return _method()
-        except SystemExit:
-            pass
-        finally:
-            pass
-
-    return _inner_call_method(method)
-
-
-def get_function(compiled_sol, fn_name: str):  # type: ignore
-    try:
-        assert hasattr(compiled_sol, fn_name)
-        return getattr(compiled_sol, fn_name)
-    except Exception:
-        return
-
-
-def compile_code(code: str, timeout: int):
-    signal.alarm(timeout)
-    try:
-        tmp_sol = ModuleType("tmp_sol", "")
-        exec(code, tmp_sol.__dict__)
-        if "class Solution" in code:
-            # leetcode wraps solutions in `Solution`
-            # this is a hack to check if it is leetcode solution or not
-            # currently livecodebench only supports LeetCode but
-            # else condition allows future extensibility to other platforms
-            compiled_sol = tmp_sol.Solution()
-        else:
-            # do nothing in the other case since function is accesible
-            compiled_sol = tmp_sol
-
-        assert compiled_sol is not None
-    finally:
-        signal.alarm(0)
-
-    return compiled_sol
-
-
-def convert_line_to_decimals(line: str) -> tuple[bool, list[Decimal]]:
-    try:
-        decimal_line = [Decimal(elem) for elem in line.split()]
-    except:
-        return False, []
-    return True, decimal_line
-
-
-def get_stripped_lines(val: str):
-    ## you don't want empty lines to add empty list after splitlines!
-    val = val.strip()
-
-    return [val_line.strip() for val_line in val.split("\n")]
-
-
-def grade_call_based(
-    code: str, all_inputs: list, all_outputs: list, fn_name: str, timeout: int
-):
-    # call-based clean up logic
-    # need to wrap in try-catch logic after to catch the correct errors, but for now this is fine.
-    code = import_string + "\n\n" + code
-    compiled_sol = compile_code(code, timeout)
-
-    if compiled_sol is None:
-        return
-
-    method = get_function(compiled_sol, fn_name)
-
-    if method is None:
-        return
-
-    all_inputs = [
-        [json.loads(line) for line in inputs.split("\n")] for inputs in all_inputs
-    ]
-
-    all_outputs = [json.loads(output) for output in all_outputs]
-
-    total_execution = 0
-    all_results = []
-    for idx, (gt_inp, gt_out) in enumerate(zip(all_inputs, all_outputs)):
-        signal.alarm(timeout)
-        faulthandler.enable()
-        try:
-            # can lock here so time is useful
-            start = time.time()
-            prediction = method(*gt_inp)
-            total_execution += time.time() - start
-            signal.alarm(0)
-
-            # don't penalize model if it produces tuples instead of lists
-            # ground truth sequences are not tuples
-            if isinstance(prediction, tuple):
-                prediction = list(prediction)
-
-            tmp_result = prediction == gt_out
-
-            # handle floating point comparisons
-
-            all_results.append(tmp_result)
-
-            if not tmp_result:
-                return all_results, {
-                    "output": truncatefn(prediction),
-                    "inputs": truncatefn(gt_inp),
-                    "expected": truncatefn(gt_out),
-                    "error_code": -2,
-                    "error_message": "Wrong Answer",
-                }
-        except Exception as e:
-            signal.alarm(0)
-            if "timeoutexception" in repr(e).lower():
-                all_results.append(-3)
-                return all_results, {
-                    "error": repr(e),
-                    "error_code": -3,
-                    "error_message": "Time Limit Exceeded",
-                    "inputs": truncatefn(gt_inp),
-                    "expected": truncatefn(gt_out),
-                }
-            else:
-                all_results.append(-4)
-                return all_results, {
-                    "error": repr(e),
-                    "error_code": -4,
-                    "error_message": "Runtime Error",
-                    "inputs": truncatefn(gt_inp),
-                    "expected": truncatefn(gt_out),
-                }
-
-        finally:
-            signal.alarm(0)
-            faulthandler.disable()
-
-    return all_results, {"execution time": total_execution}
-
-
-def grade_stdio(
-    code: str,
-    all_inputs: list,
-    all_outputs: list,
-    timeout: int,
-):
-    ## runtime doesn't interact well with __name__ == '__main__'
-    code = clean_if_name(code)
-
-    ## we wrap the given code inside another function
-    code = make_function(code)
-
-    compiled_sol = compile_code(code, timeout)
-    if compiled_sol is None:
-        return
-
-    method = get_function(compiled_sol, "wrapped_function")
-
-    if method is None:
-        return
-
-    all_results = []
-    total_execution_time = 0
-    for idx, (gt_inp, gt_out) in enumerate(zip(all_inputs, all_outputs)):
-        signal.alarm(timeout)
-        faulthandler.enable()
-
-        signal.alarm(timeout)
-        with Capturing() as captured_output:
-            try:
-                start = time.time()
-                call_method(method, gt_inp)
-                total_execution_time += time.time() - start
-                # reset the alarm
-                signal.alarm(0)
-            except Exception as e:
-                signal.alarm(0)
-                if "timeoutexception" in repr(e).lower():
-                    all_results.append(-3)
-                    return all_results, {
-                        "error": repr(e),
-                        "error_code": -3,
-                        "error_message": "Time Limit Exceeded",
-                        "inputs": truncatefn(gt_inp),
-                        "expected": truncatefn(gt_out),
-                    }
-                else:
-                    all_results.append(-4)
-                    return all_results, {
-                        "error": repr(e),
-                        "error_code": -4,
-                        "error_message": "Runtime Error",
-                        "inputs": truncatefn(gt_inp),
-                        "expected": truncatefn(gt_out),
-                    }
-
-            finally:
-                signal.alarm(0)
-                faulthandler.disable()
-
-        prediction = captured_output[0]
-
-        stripped_prediction_lines = get_stripped_lines(prediction)
-        stripped_gt_out_lines = get_stripped_lines(gt_out)
-
-        ## WA happens in multiple circumstances
-        ## so cache the return to make it clean!
-        WA_send_args = {
-            "output": truncatefn(prediction),
-            "inputs": truncatefn(gt_inp),
-            "expected": truncatefn(gt_out),
-            "error_code": -2,
-        }
-
-        if len(stripped_prediction_lines) != len(stripped_gt_out_lines):
-            all_results.append(-2)
-            WA_send_args["error_message"] = "Wrong answer: mismatched output length"
-            return all_results, WA_send_args
-
-        for output_line_idx, (
-            stripped_prediction_line,
-            stripped_gt_out_line,
-        ) in enumerate(zip(stripped_prediction_lines, stripped_gt_out_lines)):
-            WA_send_args["error_message"] = (
-                f"Wrong answer at {output_line_idx=}: {truncatefn(stripped_prediction_line)} != {truncatefn(stripped_gt_out_line)}"
-            )
-
-            ## CASE 1: exact match
-            if stripped_prediction_line == stripped_gt_out_line:
-                continue
-
-            ## CASE 2: element-wise comparision
-            ## if there are floating elements
-            ## use `decimal` library for good floating point comparision
-            ## otherwise gotcha: np.isclose(50000000000000000, 50000000000000001) = True
-            ## note that we should always be able to convert to decimals
-
-            success, decimal_prediction_line = convert_line_to_decimals(
-                stripped_prediction_line
-            )
-            if not success:
-                all_results.append(-2)
-                return all_results, WA_send_args
-            success, decimal_gtout_line = convert_line_to_decimals(stripped_gt_out_line)
-            if not success:
-                all_results.append(-2)
-                return all_results, WA_send_args
-
-            if decimal_prediction_line == decimal_gtout_line:
-                continue
-
-            all_results.append(-2)
-            return all_results, WA_send_args
-        all_results.append(True)
-
-    return all_results, {"execution time": total_execution_time}
-
-
-def run_test(sample, test=None, debug=False, timeout=6):
-    """
-    if test(generated_code) is not None it'll try to run the code.
-    otherwise it'll just return an input and output pair.
-    """
-    signal.signal(signal.SIGALRM, timeout_handler)
-
-    # Disable functionalities that can make destructive changes to the test.
-    # max memory is set to 4GB
-    reliability_guard()
-
-    if debug:
-        print(f"start = {datetime.now().time()}")
-
-    try:
-        in_outs = json.loads(sample["input_output"])
-    except ValueError as e:
-        raise e
-        in_outs = None
-
-    if in_outs:
-        if in_outs.get("fn_name") is None:
-            which_type = CODE_TYPE.standard_input  # Standard input
-            method_name = None
-
-        else:
-            which_type = CODE_TYPE.call_based  # Call-based
-            method_name = in_outs["fn_name"]
-
-    if debug:
-        print(f"loaded input_output = {datetime.now().time()}")
-
-    if test is None:
-        assert False, "should not happen: test code is none"
-        return in_outs, {"error": "no test code provided"}
-    elif test is not None:
-        results = []
-        sol = import_string
-        if debug:
-            print(f"loading test code = {datetime.now().time()}")
-
-        if which_type == CODE_TYPE.call_based:
-            signal.alarm(timeout)
-            try:
-                results, metadata = grade_call_based(
-                    code=test,
-                    all_inputs=in_outs["inputs"],
-                    all_outputs=in_outs["outputs"],
-                    fn_name=method_name,
-                    timeout=timeout,
-                )
-                return results, metadata
-            except Exception as e:
-                return [-4], {
-                    "error_code": -4,
-                    "error_message": f"Error during testing: {e}",
-                }
-            finally:
-                signal.alarm(0)
-        elif which_type == CODE_TYPE.standard_input:
-            # sol
-            # if code has if __name__ == "__main__": then remove it
-
-            signal.alarm(timeout)
-            try:
-                results, metadata = grade_stdio(
-                    code=test,
-                    all_inputs=in_outs["inputs"],
-                    all_outputs=in_outs["outputs"],
-                    timeout=timeout,
-                )
-                return results, metadata
-            except Exception as e:
-                return [-4], {
-                    "error_code": -4,
-                    "error_message": f"Error during testing: {e}",
-                }
-            finally:
-                signal.alarm(0)
-
-
-def reliability_guard(maximum_memory_bytes=None):
-    """
-    This disables various destructive functions and prevents the generated code
-    from interfering with the test (e.g. fork bomb, killing other processes,
-    removing filesystem files, etc.)
-    WARNING
-    This function is NOT a security sandbox. Untrusted code, including, model-
-    generated code, should not be blindly executed outside of one. See the
-    Codex paper for more information about OpenAI's code sandbox, and proceed
-    with caution.
-    """
-
-    if maximum_memory_bytes is not None:
-        import resource
-
-        resource.setrlimit(
-            resource.RLIMIT_AS, (maximum_memory_bytes, maximum_memory_bytes)
-        )
-        resource.setrlimit(
-            resource.RLIMIT_DATA, (maximum_memory_bytes, maximum_memory_bytes)
-        )
-        if not platform.uname().system == "Darwin":
-            resource.setrlimit(
-                resource.RLIMIT_STACK, (maximum_memory_bytes, maximum_memory_bytes)
-            )
-
-    faulthandler.disable()
-
-    import builtins
-
-    # builtins.exit = None
-    builtins.quit = None
-
-    import os
-
-    os.environ["OMP_NUM_THREADS"] = "1"
-
-    os.kill = None
-    os.system = None
-    os.putenv = None
-    os.remove = None
-    os.removedirs = None
-    os.rmdir = None
-    os.fchdir = None
-    os.setuid = None
-    os.fork = None
-    os.forkpty = None
-    os.killpg = None
-    os.rename = None
-    os.renames = None
-    os.truncate = None
-    os.replace = None
-    os.unlink = None
-    os.fchmod = None
-    os.fchown = None
-    os.chmod = None
-    os.chown = None
-    os.chroot = None
-    os.fchdir = None
-    os.lchflags = None
-    os.lchmod = None
-    os.lchown = None
-    os.getcwd = None
-    os.chdir = None
-
-    import shutil
-
-    shutil.rmtree = None
-    shutil.move = None
-    shutil.chown = None
-
-    import subprocess
-
-    subprocess.Popen = None
-
-    __builtins__["help"] = None
-
-    import sys
-
-    sys.modules["ipdb"] = None
-    sys.modules["joblib"] = None
-    sys.modules["resource"] = None
-    sys.modules["psutil"] = None
-    sys.modules["tkinter"] = None

bench_overnight.sh

@@ -1,24 +0,0 @@
-#!/bin/bash
-set -e
-
-export PATH="/opt/homebrew/bin:$PATH"
-
-echo "=== Starting overnight bench runs at $(date) ==="
-
-echo "--- [4/8] Qwen3.5-122B-A10B-GPTQ-Int4 ---"
-echo "Skipping because Int 4"
-#uv run bench/exo_bench.py --force-download --model "Qwen/Qwen3.5-122B-A10B-GPTQ-Int4" --pp 700 --tg 36000 --repeat 1
-
-echo "--- [5/8] Qwen3.5-27B-FP8 ---"
-#uv run bench/exo_bench.py --force-download --model "Qwen/Qwen3.5-27B-FP8" --pp 700 --tg 35133 --repeat 1
-
-echo "--- [6/8] GLM-4.7-Flash-bf16 ---"
-uv run bench/exo_bench.py --force-download --model "mlx-community/GLM-4.7-Flash-bf16" --pp 700 --tg 29000 --repeat 1
-
-echo "--- [7/8] NVIDIA-Nemotron-3-Nano-30B-A3B (23000,1200) ---"
-uv run bench/exo_bench.py --force-download --model "mlx-community/NVIDIA-Nemotron-3-Nano-30B-A3B-MLX-BF16" --pp 700 --tg 23000,1200 --repeat 1
-
-echo "--- [8/8] Qwen3.5-27B-bf16 ---"
-uv run bench/exo_bench.py --force-download --model "mlx-community/Qwen3.5-27B-bf16" --pp 700 --tg 35400 --repeat 1
-
-echo "=== All bench runs complete at $(date) ==="

dashboard/src/lib/components/ChatForm.svelte

@@ -1,6 +1,9 @@
 <script lang="ts">
   import {
     isLoading,
+    sendMessage,
+    generateImage,
+    editImage,
     editingImage,
     clearEditingImage,
     selectedChatModel,
@@ -25,7 +28,7 @@
     modelTasks?: Record<string, string[]>;
     modelCapabilities?: Record<string, string[]>;
     onSend?: () => void;
-    onAutoSend: (
+    onAutoSend?: (
       content: string,
       files?: {
         id: string;
@@ -213,10 +216,49 @@
     uploadedFiles = [];
     resetTextareaHeight();
 
-    // Parent controls all send logic (including image routing,
-    // launching non-running models before sending, etc.)
-    onAutoSend(content, files);
+    // When onAutoSend is provided, the parent controls all send logic
+    // (including launching non-running models before sending)
+    if (onAutoSend) {
+      onAutoSend(content, files);
+      onSend?.();
+      setTimeout(() => textareaRef?.focus(), 10);
+      return;
+    }
+
+    // Use image editing if in edit mode
+    if (isEditMode && currentEditingImage && content) {
+      editImage(content, currentEditingImage.imageDataUrl);
+    }
+    // If user attached an image with an ImageToImage model, use edit endpoint
+    else if (
+      currentModel &&
+      modelSupportsImageEditing(currentModel) &&
+      files.length > 0 &&
+      content
+    ) {
+      // Use the first attached image for editing
+      const imageFile = files[0];
+      if (imageFile.preview) {
+        editImage(content, imageFile.preview);
+      }
+    } else if (
+      currentModel &&
+      modelSupportsTextToImage(currentModel) &&
+      content
+    ) {
+      // Use image generation for text-to-image models
+      generateImage(content);
+    } else {
+      sendMessage(
+        content,
+        files,
+        modelSupportsThinking() ? thinkingEnabled : null,
+      );
+    }
+
     onSend?.();
+
+    // Refocus the textarea after sending
     setTimeout(() => textareaRef?.focus(), 10);
   }
 

dashboard/src/lib/components/ChatModelSelector.svelte

@@ -9,10 +9,6 @@
     quantization: string;
   }
 
-  function normalizeBaseModel(s: string): string {
-    return s.toLowerCase().replace(/[-_]/g, " ").trim();
-  }
-
   // Auto mode tier list (for when user just starts typing)
   export const AUTO_TIERS: string[][] = [
     // Tier 1 (frontier)
@@ -47,9 +43,8 @@
 
   /** Return the tier index (0 = best) for a base_model name. */
   export function getAutoTierIndex(baseModel: string): number {
-    const norm = normalizeBaseModel(baseModel);
     for (let i = 0; i < AUTO_TIERS.length; i++) {
-      if (AUTO_TIERS[i].some((t) => normalizeBaseModel(t) === norm)) return i;
+      if (AUTO_TIERS[i].includes(baseModel)) return i;
     }
     return AUTO_TIERS.length; // not in any tier → lowest priority
   }
@@ -65,7 +60,7 @@
         const variants = modelList
           .filter(
             (m) =>
-              normalizeBaseModel(m.base_model) === normalizeBaseModel(baseModel) &&
+              m.base_model === baseModel &&
               (m.storage_size_megabytes || 0) / 1024 <= memoryGB &&
               (m.storage_size_megabytes || 0) > 0,
           )
@@ -167,7 +162,7 @@
   /** For a given base_model name, find the biggest quant variant that fits in memory. */
   function pickBestVariant(baseModel: string): ChatModelInfo | null {
     const variants = models
-      .filter((m) => normalizeBaseModel(m.base_model) === normalizeBaseModel(baseModel) && fitsInMemory(m))
+      .filter((m) => m.base_model === baseModel && fitsInMemory(m))
       .sort((a, b) => getModelSizeGB(b) - getModelSizeGB(a));
     return variants[0] ?? null;
   }

dashboard/src/lib/components/ChatSidebar.svelte

@@ -18,18 +18,12 @@
     class?: string;
     onNewChat?: () => void;
     onSelectConversation?: () => void;
-    isMobileDrawer?: boolean;
-    isOpen?: boolean;
-    onClose?: () => void;
   }
 
   let {
     class: className = "",
     onNewChat,
     onSelectConversation,
-    isMobileDrawer = false,
-    isOpen = false,
-    onClose,
   }: Props = $props();
 
   const conversationList = $derived(conversations());
@@ -59,10 +53,6 @@
   function handleSelectConversation(id: string) {
     onSelectConversation?.();
     loadConversation(id);
-    // Close mobile drawer when selecting a conversation
-    if (isMobileDrawer && isOpen) {
-      onClose?.();
-    }
   }
 
   function handleStartEdit(id: string, name: string, event: MouseEvent) {
@@ -262,7 +252,9 @@
   }
 </script>
 
-{#snippet sidebarContent()}
+<aside
+  class="flex flex-col h-full bg-exo-dark-gray border-r border-exo-yellow/10 {className}"
+>
   <!-- Header -->
   <div class="p-4">
     <button
@@ -599,30 +591,4 @@
       </button>
     </div>
   </div>
-{/snippet}
-
-{#if isMobileDrawer}
-  <!-- Mobile drawer with overlay -->
-  {#if isOpen}
-    <!-- Overlay backdrop -->
-    <button
-      type="button"
-      class="fixed inset-0 bg-black/60 backdrop-blur-sm z-40 md:hidden"
-      onclick={() => onClose?.()}
-      aria-label="Close sidebar"
-    ></button>
-    <!-- Drawer panel -->
-    <aside
-      class="fixed left-0 top-0 bottom-0 w-72 bg-exo-dark-gray border-r border-exo-yellow/10 z-50 flex flex-col md:hidden"
-    >
-      {@render sidebarContent()}
-    </aside>
-  {/if}
-{:else}
-  <!-- Desktop sidebar -->
-  <aside
-    class="flex flex-col h-full bg-exo-dark-gray border-r border-exo-yellow/10 {className}"
-  >
-    {@render sidebarContent()}
-  </aside>
-{/if}
+</aside>

dashboard/src/lib/components/DeviceIcon.svelte

@@ -9,7 +9,7 @@
    */
 
   interface Props {
-    /** "macbook pro" | "mac studio" | "mac mini" | "dgx spark" | "linux" etc. */
+    /** "macbook pro" | "mac studio" | "mac mini" etc. */
     deviceType: string;
     /** Center X coordinate in SVG space */
     cx: number;
@@ -38,43 +38,10 @@
   const LOGO_NATIVE_WIDTH = 814;
   const LOGO_NATIVE_HEIGHT = 1000;
 
-  // NVIDIA logo SVG path
-  const NVIDIA_LOGO_PATH =
-    "M0.81 0.429V0.299c0.013 -0.001 0.026 -0.002 0.038 -0.002 0.355 -0.011 0.588 0.306 0.588 0.306S1.186 0.952 0.916 0.952c-0.036 0 -0.071 -0.006 -0.105 -0.017V0.542c0.138 0.017 0.166 0.078 0.249 0.216l0.185 -0.155s-0.135 -0.177 -0.362 -0.177c-0.024 -0.001 -0.048 0.001 -0.072 0.003m0 -0.429v0.194l0.038 -0.002c0.494 -0.017 0.816 0.405 0.816 0.405s-0.37 0.45 -0.754 0.45c-0.034 0 -0.066 -0.003 -0.099 -0.009v0.12c0.027 0.003 0.055 0.006 0.082 0.006 0.358 0 0.618 -0.183 0.869 -0.399 0.042 0.034 0.212 0.114 0.247 0.15 -0.238 0.2 -0.794 0.361 -1.11 0.361 -0.03 0 -0.059 -0.002 -0.088 -0.005v0.169h1.362V0zm0 0.935v0.102c-0.331 -0.059 -0.423 -0.404 -0.423 -0.404s0.159 -0.176 0.423 -0.205v0.112h-0.001C0.671 0.524 0.562 0.654 0.562 0.654s0.062 0.218 0.248 0.282m-0.588 -0.316s0.196 -0.29 0.589 -0.32V0.194C0.376 0.229 0 0.597 0 0.597s0.213 0.616 0.81 0.672v-0.112c-0.438 -0.054 -0.588 -0.538 -0.588 -0.538";
-
   const wireColor = "rgba(179,179,179,0.8)";
   const strokeWidth = 1.5;
 
   const modelLower = $derived(deviceType.toLowerCase());
-  const isSpark = $derived(
-    modelLower.includes("dgx") || modelLower.includes("gx10"),
-  );
-  const isLinux = $derived(!isSpark && modelLower.startsWith("linux"));
-  const isLinuxLaptop = $derived(isLinux && modelLower.includes("laptop"));
-
-  // ── DGX Spark dimensions ──
-  const dgxW = $derived(size * 1.55);
-  const dgxH = $derived(size * 0.58);
-  const dgxX = $derived(cx - dgxW / 2);
-  const dgxY = $derived(cy - dgxH / 2);
-  const dgxChassisX = $derived(dgxX - dgxW * 0.03);
-  const dgxChassisW = $derived(dgxW * 1.05);
-  const dgxHandleW = $derived(dgxW * 0.27);
-  const dgxHandleGap = $derived(dgxH * 0.05);
-  const dgxHandleH = $derived(dgxH - dgxHandleGap * 2);
-  const dgxHandleY = $derived(dgxY + dgxHandleGap);
-  const dgxInnerHandleW = $derived(dgxW * 0.12);
-  const dgxInnerHandleH = $derived(dgxHandleH - dgxH * 0.06);
-  const dgxLeftHandleX = $derived(dgxX + 4);
-  const dgxRightHandleX = $derived(dgxX + dgxW - dgxHandleW - 4);
-  const dgxClipId = $derived(`di-dgx-${uid}`);
-  const dgxTextureId = $derived(`di-dgx-tex-${uid}`);
-
-  // ── Linux Desktop dimensions (reuses Mac Studio proportions) ──
-  const linuxDesktopClipId = $derived(`di-linux-desktop-${uid}`);
-
-  // ── Linux Laptop dimensions (reuses MacBook proportions) ──
-  const linuxScreenClipId = $derived(`di-linux-screen-${uid}`);
 
   // ── Mac Studio dimensions (same ratios as TopologyGraph) ──
   const studioW = $derived(size * 1.25);
@@ -147,264 +114,7 @@
   const studioClipId = $derived(`di-studio-${uid}`);
 </script>
 
-{#if isSpark}
-  <!-- DGX Spark -->
-  <defs>
-    <clipPath id={dgxClipId}>
-      <rect x={dgxX} y={dgxY} width={dgxW} height={dgxH} rx="3" />
-    </clipPath>
-    <pattern
-      id={dgxTextureId}
-      patternUnits="userSpaceOnUse"
-      width="8"
-      height="8"
-    >
-      <rect width="8" height="8" fill="#6f6248" />
-      <circle cx="2" cy="2" r="1" fill="#5a4f3b" opacity="0.5" />
-      <circle cx="6" cy="6" r="1" fill="#4a4232" opacity="0.45" />
-    </pattern>
-  </defs>
-
-  <!-- Main body -->
-  <rect
-    x={dgxChassisX}
-    y={dgxY}
-    width={dgxChassisW}
-    height={dgxH}
-    rx="3"
-    fill="url(#{dgxTextureId})"
-    stroke={wireColor}
-    stroke-width={strokeWidth}
-  />
-
-  <!-- Side border accents -->
-  <rect
-    x={dgxChassisX}
-    y={dgxY}
-    width={dgxW * 0.02}
-    height={dgxH}
-    fill="#8a7a56"
-  />
-  <rect
-    x={dgxChassisX + dgxChassisW - dgxW * 0.02}
-    y={dgxY}
-    width={dgxW * 0.02}
-    height={dgxH}
-    fill="#8a7a56"
-  />
-
-  <!-- Memory fill -->
-  {#if ramPercent > 0}
-    <rect
-      x={dgxX}
-      y={dgxY + dgxH - (ramPercent / 100) * dgxH}
-      width={dgxW}
-      height={(ramPercent / 100) * dgxH}
-      fill="rgba(255,215,0,0.45)"
-      clip-path="url(#{dgxClipId})"
-    />
-  {/if}
-
-  <!-- Left handle -->
-  <rect
-    x={dgxLeftHandleX}
-    y={dgxHandleY}
-    width={dgxHandleW}
-    height={dgxHandleH}
-    rx="2.4"
-    fill="#b3a170"
-    stroke="#403723"
-    stroke-width="0.7"
-  />
-  <rect
-    x={dgxLeftHandleX + dgxHandleW * 0.06}
-    y={dgxHandleY + dgxH * 0.03}
-    width={dgxInnerHandleW}
-    height={dgxInnerHandleH}
-    rx="1.6"
-    fill="#8a7a56"
-  />
-
-  <!-- Right handle -->
-  <rect
-    x={dgxRightHandleX}
-    y={dgxHandleY}
-    width={dgxHandleW}
-    height={dgxHandleH}
-    rx="2.4"
-    fill="#b3a170"
-    stroke="#403723"
-    stroke-width="0.7"
-  />
-  <rect
-    x={dgxRightHandleX + dgxHandleW - dgxInnerHandleW - dgxHandleW * 0.08}
-    y={dgxHandleY + dgxH * 0.03}
-    width={dgxInnerHandleW}
-    height={dgxInnerHandleH}
-    rx="1.6"
-    fill="#8a7a56"
-  />
-
-  <!-- NVIDIA logo (rotated 90deg on left handle) -->
-  {@const badgeW = dgxW * 0.09}
-  {@const badgeH = dgxHandleH * 0.5}
-  {@const badgeX = dgxLeftHandleX + dgxHandleW - badgeW - dgxHandleW * 0.06}
-  {@const badgeYPos = dgxHandleY + (dgxHandleH - badgeH) / 2}
-  {@const textSz = badgeW * 0.58}
-  {@const logoW = textSz * 1.2}
-  {@const logoH = logoW * (1.438 / 2.174)}
-  {@const ctrX = badgeX + badgeW / 2 - badgeW * 0.03}
-  {@const ctrY = badgeYPos + badgeH / 2}
-  {@const labelGap = badgeW * 0.15}
-  {@const totalW = logoW + labelGap + textSz * 3.6}
-  <g transform="rotate(90 {ctrX} {ctrY})">
-    <svg
-      x={ctrX - totalW / 2}
-      y={ctrY - logoH / 2}
-      width={logoW}
-      height={logoH}
-      viewBox="0 0 2.174 1.438"
-    >
-      <path d={NVIDIA_LOGO_PATH} fill="#76b900" />
-    </svg>
-    <text
-      x={ctrX - totalW / 2 + logoW + labelGap}
-      y={ctrY}
-      text-anchor="start"
-      dominant-baseline="middle"
-      fill="#8a7a56"
-      font-size={textSz}
-      font-family="monospace"
-      font-weight="700">NVIDIA</text
-    >
-  </g>
-{:else if isLinuxLaptop}
-  <!-- Linux Laptop — MacBook shape with Tux logo -->
-  <defs>
-    <clipPath id={linuxScreenClipId}>
-      <rect
-        x={mbScreenX + mbBezel}
-        y={mbY + mbBezel}
-        width={mbScreenW - mbBezel * 2}
-        height={mbScreenH - mbBezel * 2}
-        rx="2"
-      />
-    </clipPath>
-  </defs>
-
-  <rect
-    x={mbScreenX}
-    y={mbY}
-    width={mbScreenW}
-    height={mbScreenH}
-    rx="3"
-    fill="#1a1a1a"
-    stroke={wireColor}
-    stroke-width={strokeWidth}
-  />
-  <rect
-    x={mbScreenX + mbBezel}
-    y={mbY + mbBezel}
-    width={mbScreenW - mbBezel * 2}
-    height={mbScreenH - mbBezel * 2}
-    rx="2"
-    fill="#0a0a12"
-  />
-  {#if ramPercent > 0}
-    <rect
-      x={mbScreenX + mbBezel}
-      y={mbY + mbBezel + (mbMemTotalH - mbMemH)}
-      width={mbScreenW - mbBezel * 2}
-      height={mbMemH}
-      fill="rgba(255,215,0,0.85)"
-      clip-path="url(#{linuxScreenClipId})"
-    />
-  {/if}
-
-  <!-- Terminal prompt on screen -->
-  <text
-    x={cx}
-    y={mbY + mbScreenH / 2}
-    text-anchor="middle"
-    dominant-baseline="middle"
-    fill="#FFFFFF"
-    opacity="0.9"
-    font-size={mbScreenH * 0.25}
-    font-family="SF Mono, Monaco, monospace"
-    font-weight="700">{">_"}</text
-  >
-
-  <path
-    d="M {mbBaseTopX} {mbBaseY} L {mbBaseTopX +
-      mbBaseTopW} {mbBaseY} L {mbBaseBottomX + mbBaseBottomW} {mbBaseY +
-      mbBaseH} L {mbBaseBottomX} {mbBaseY + mbBaseH} Z"
-    fill="#2c2c2c"
-    stroke={wireColor}
-    stroke-width="1"
-  />
-  <rect
-    x={mbKbX}
-    y={mbKbY}
-    width={mbKbW}
-    height={mbKbH}
-    fill="rgba(0,0,0,0.2)"
-    rx="2"
-  />
-  <rect
-    x={mbTpX}
-    y={mbTpY}
-    width={mbTpW}
-    height={mbTpH}
-    fill="rgba(255,255,255,0.08)"
-    rx="2"
-  />
-{:else if isLinux}
-  <!-- Linux Desktop — Mac Studio shape with Tux logo -->
-  <defs>
-    <clipPath id={linuxDesktopClipId}>
-      <rect
-        x={studioX}
-        y={studioY + studioTopH}
-        width={studioW}
-        height={studioH - studioTopH}
-        rx={studioCorner - 1}
-      />
-    </clipPath>
-  </defs>
-
-  <rect
-    x={studioX}
-    y={studioY}
-    width={studioW}
-    height={studioH}
-    rx={studioCorner}
-    fill="#1a1a1a"
-    stroke={wireColor}
-    stroke-width={strokeWidth}
-  />
-  {#if ramPercent > 0}
-    <rect
-      x={studioX}
-      y={studioY + studioTopH + (studioMemTotalH - studioMemH)}
-      width={studioW}
-      height={studioMemH}
-      fill="rgba(255,215,0,0.75)"
-      clip-path="url(#{linuxDesktopClipId})"
-    />
-  {/if}
-
-  <!-- Terminal prompt on front face -->
-  <text
-    x={cx}
-    y={studioY + studioTopH + (studioH - studioTopH) / 2}
-    text-anchor="middle"
-    dominant-baseline="middle"
-    fill="rgba(255,255,255,0.5)"
-    font-size={(studioH - studioTopH) * 0.4}
-    font-family="SF Mono, Monaco, monospace"
-    font-weight="700">{">_"}</text
-  >
-{:else if modelLower === "mac studio" || modelLower === "mac mini"}
+{#if modelLower === "mac studio" || modelLower === "mac mini"}
   <!-- Mac Studio / Mac Mini -->
   <defs>
     <clipPath id={studioClipId}>

dashboard/src/lib/components/FamilyLogos.svelte

@@ -82,12 +82,6 @@
       d="M12.025 1.13c-5.77 0-10.449 4.647-10.449 10.378 0 1.112.178 2.181.503 3.185.064-.222.203-.444.416-.577a.96.96 0 0 1 .524-.15c.293 0 .584.124.84.284.278.173.48.408.71.694.226.282.458.611.684.951v-.014c.017-.324.106-.622.264-.874s.403-.487.762-.543c.3-.047.596.06.787.203s.31.313.4.467c.15.257.212.468.233.542.01.026.653 1.552 1.657 2.54.616.605 1.01 1.223 1.082 1.912.055.537-.096 1.059-.38 1.572.637.121 1.294.187 1.967.187.657 0 1.298-.063 1.921-.178-.287-.517-.44-1.041-.384-1.581.07-.69.465-1.307 1.081-1.913 1.004-.987 1.647-2.513 1.657-2.539.021-.074.083-.285.233-.542.09-.154.208-.323.4-.467a1.08 1.08 0 0 1 .787-.203c.359.056.604.29.762.543s.247.55.265.874v.015c.225-.34.457-.67.683-.952.23-.286.432-.52.71-.694.257-.16.547-.284.84-.285a.97.97 0 0 1 .524.151c.228.143.373.388.43.625l.006.04a10.3 10.3 0 0 0 .534-3.273c0-5.731-4.678-10.378-10.449-10.378M8.327 6.583a1.5 1.5 0 0 1 .713.174 1.487 1.487 0 0 1 .617 2.013c-.183.343-.762-.214-1.102-.094-.38.134-.532.914-.917.71a1.487 1.487 0 0 1 .69-2.803m7.486 0a1.487 1.487 0 0 1 .689 2.803c-.385.204-.536-.576-.916-.71-.34-.12-.92.437-1.103.094a1.487 1.487 0 0 1 .617-2.013 1.5 1.5 0 0 1 .713-.174m-10.68 1.55a.96.96 0 1 1 0 1.921.96.96 0 0 1 0-1.92m13.838 0a.96.96 0 1 1 0 1.92.96.96 0 0 1 0-1.92M8.489 11.458c.588.01 1.965 1.157 3.572 1.164 1.607-.007 2.984-1.155 3.572-1.164.196-.003.305.12.305.454 0 .886-.424 2.328-1.563 3.202-.22-.756-1.396-1.366-1.63-1.32q-.011.001-.02.006l-.044.026-.01.008-.03.024q-.018.017-.035.036l-.032.04a1 1 0 0 0-.058.09l-.014.025q-.049.088-.11.19a1 1 0 0 1-.083.116 1.2 1.2 0 0 1-.173.18q-.035.029-.075.058a1.3 1.3 0 0 1-.251-.243 1 1 0 0 1-.076-.107c-.124-.193-.177-.363-.337-.444-.034-.016-.104-.008-.2.022q-.094.03-.216.087-.06.028-.125.063l-.13.074q-.067.04-.136.086a3 3 0 0 0-.135.096 3 3 0 0 0-.26.219 2 2 0 0 0-.12.121 2 2 0 0 0-.106.128l-.002.002a2 2 0 0 0-.09.132l-.001.001a1.2 1.2 0 0 0-.105.212q-.013.036-.024.073c-1.139-.875-1.563-2.317-1.563-3.203 0-.334.109-.457.305-.454m.836 10.354c.824-1.19.766-2.082-.365-3.194-1.13-1.112-1.789-2.738-1.789-2.738s-.246-.945-.806-.858-.97 1.499.202 2.362c1.173.864-.233 1.45-.685.64-.45-.812-1.683-2.896-2.322-3.295s-1.089-.175-.938.647 2.822 2.813 2.562 3.244-1.176-.506-1.176-.506-2.866-2.567-3.49-1.898.473 1.23 2.037 2.16c1.564.932 1.686 1.178 1.464 1.53s-3.675-2.511-4-1.297c-.323 1.214 3.524 1.567 3.287 2.405-.238.839-2.71-1.587-3.216-.642-.506.946 3.49 2.056 3.522 2.064 1.29.33 4.568 1.028 5.713-.624m5.349 0c-.824-1.19-.766-2.082.365-3.194 1.13-1.112 1.789-2.738 1.789-2.738s.246-.945.806-.858.97 1.499-.202 2.362c-1.173.864.233 1.45.685.64.451-.812 1.683-2.896 2.322-3.295s1.089-.175.938.647-2.822 2.813-2.562 3.244 1.176-.506 1.176-.506 2.866-2.567 3.49-1.898-.473 1.23-2.037 2.16c-1.564.932-1.686 1.178-1.464 1.53s3.675-2.511 4-1.297c.323 1.214-3.524 1.567-3.287 2.405.238.839 2.71-1.587 3.216-.642.506.946-3.49 2.056-3.522 2.064-1.29.33-4.568 1.028-5.713-.624"
     />
   </svg>
-{:else if family === "step"}
-  <svg class="w-6 h-6 {className}" viewBox="0 0 24 24" fill="currentColor">
-    <path
-      d="M22.012 0h1.032v.927H24v.968h-.956V3.78h-1.032V1.896h-1.878v-.97h1.878V0zM2.6 12.371V1.87h.969v10.502h-.97zm10.423.66h10.95v.918h-6.208v9.579h-4.742V13.03zM5.629 3.333v12.356H0v4.51h10.386V8L20.859 8l-.003-4.668-15.227.001z"
-    />
-  </svg>
 {:else}
   <svg class="w-6 h-6 {className}" viewBox="0 0 24 24" fill="currentColor">
     <path

dashboard/src/lib/components/FamilySidebar.svelte

@@ -41,13 +41,13 @@
 </script>
 
 <div
-  class="flex flex-col gap-1 py-2 px-1 border-r border-exo-yellow/10 bg-exo-medium-gray/30 min-w-[72px] sm:min-w-[64px] overflow-y-auto scrollbar-hide"
+  class="flex flex-col gap-1 py-2 px-1 border-r border-exo-yellow/10 bg-exo-medium-gray/30 min-w-[64px] overflow-y-auto scrollbar-hide"
 >
   <!-- All models (no filter) -->
   <button
     type="button"
     onclick={() => onSelect(null)}
-    class="group flex flex-col items-center justify-center p-2 sm:p-2 rounded transition-all duration-200 cursor-pointer min-h-[44px] sm:min-h-0 {selectedFamily ===
+    class="group flex flex-col items-center justify-center p-2 rounded transition-all duration-200 cursor-pointer {selectedFamily ===
     null
       ? 'bg-exo-yellow/20 border-l-2 border-exo-yellow'
       : 'hover:bg-white/5 border-l-2 border-transparent'}"

dashboard/src/lib/components/HeaderNav.svelte

@@ -6,12 +6,6 @@
   export let showSidebarToggle = false;
   export let sidebarVisible = true;
   export let onToggleSidebar: (() => void) | null = null;
-  export let showMobileMenuToggle = false;
-  export let mobileMenuOpen = false;
-  export let onToggleMobileMenu: (() => void) | null = null;
-  export let showMobileRightToggle = false;
-  export let mobileRightOpen = false;
-  export let onToggleMobileRight: (() => void) | null = null;
   export let downloadProgress: {
     count: number;
     percentage: number;
@@ -33,96 +27,49 @@
       onToggleSidebar();
     }
   }
-
-  function handleToggleMobileMenu(): void {
-    if (onToggleMobileMenu) {
-      onToggleMobileMenu();
-    }
-  }
-
-  function handleToggleMobileRight(): void {
-    if (onToggleMobileRight) {
-      onToggleMobileRight();
-    }
-  }
 </script>
 
 <header
-  class="relative z-20 flex items-center justify-center px-4 md:px-6 pt-4 md:pt-8 pb-3 md:pb-4 bg-exo-dark-gray"
+  class="relative z-20 flex items-center justify-center px-6 pt-8 pb-4 bg-exo-dark-gray"
 >
-  <!-- Left: Sidebar Toggle (desktop) or Mobile Sidebar Toggle (mobile) -->
-  <div
-    class="absolute left-4 md:left-6 top-1/2 -translate-y-1/2 flex items-center gap-2"
-  >
-    <!-- Mobile sidebar toggle -->
-    <button
-      onclick={handleToggleMobileMenu}
-      class="p-2 rounded border border-exo-light-gray/30 hover:border-exo-yellow/50 hover:bg-exo-medium-gray/30 transition-colors cursor-pointer md:hidden"
-      title={mobileMenuOpen ? "Hide sidebar" : "Show sidebar"}
-      aria-label={mobileMenuOpen
-        ? "Hide conversation sidebar"
-        : "Show conversation sidebar"}
-      aria-pressed={mobileMenuOpen}
-    >
-      <svg
-        fill="none"
-        viewBox="0 0 24 24"
-        stroke="currentColor"
-        stroke-width="2"
-        class="w-5 h-5 {mobileMenuOpen
-          ? 'text-exo-yellow'
-          : 'text-exo-light-gray'}"
+  <!-- Left: Sidebar Toggle -->
+  {#if showSidebarToggle}
+    <div class="absolute left-6 top-1/2 -translate-y-1/2">
+      <button
+        onclick={handleToggleSidebar}
+        class="p-2 rounded border border-exo-light-gray/30 hover:border-exo-yellow/50 hover:bg-exo-medium-gray/30 transition-colors cursor-pointer"
+        title={sidebarVisible ? "Hide sidebar" : "Show sidebar"}
+        aria-label={sidebarVisible
+          ? "Hide conversation sidebar"
+          : "Show conversation sidebar"}
+        aria-pressed={sidebarVisible}
       >
-        {#if mobileMenuOpen}
-          <path
-            stroke-linecap="round"
-            stroke-linejoin="round"
-            d="M11 19l-7-7 7-7m8 14l-7-7 7-7"
-          ></path>
-        {:else}
-          <path
-            stroke-linecap="round"
-            stroke-linejoin="round"
-            d="M13 5l7 7-7 7M5 5l7 7-7 7"
-          ></path>
-        {/if}
-      </svg>
-    </button>
-    <!-- Desktop sidebar toggle -->
-    <button
-      onclick={handleToggleSidebar}
-      class="p-2 rounded border border-exo-light-gray/30 hover:border-exo-yellow/50 hover:bg-exo-medium-gray/30 transition-colors cursor-pointer hidden md:block"
-      title={sidebarVisible ? "Hide sidebar" : "Show sidebar"}
-      aria-label={sidebarVisible
-        ? "Hide conversation sidebar"
-        : "Show conversation sidebar"}
-      aria-pressed={sidebarVisible}
-    >
-      <svg
-        fill="none"
-        viewBox="0 0 24 24"
-        stroke="currentColor"
-        stroke-width="2"
-        class="w-5 h-5 {sidebarVisible
-          ? 'text-exo-yellow'
-          : 'text-exo-light-gray'}"
-      >
-        {#if sidebarVisible}
-          <path
-            stroke-linecap="round"
-            stroke-linejoin="round"
-            d="M11 19l-7-7 7-7m8 14l-7-7 7-7"
-          ></path>
-        {:else}
-          <path
-            stroke-linecap="round"
-            stroke-linejoin="round"
-            d="M13 5l7 7-7 7M5 5l7 7-7 7"
-          ></path>
-        {/if}
-      </svg>
-    </button>
-  </div>
+        <svg
+          class="w-5 h-5 {sidebarVisible
+            ? 'text-exo-yellow'
+            : 'text-exo-light-gray'}"
+          fill="none"
+          viewBox="0 0 24 24"
+          stroke="currentColor"
+          stroke-width="2"
+        >
+          {#if sidebarVisible}
+            <path
+              stroke-linecap="round"
+              stroke-linejoin="round"
+              d="M11 19l-7-7 7-7m8 14l-7-7 7-7"
+            />
+          {:else}
+            <path
+              stroke-linecap="round"
+              stroke-linejoin="round"
+              d="M13 5l7 7-7 7M5 5l7 7-7 7"
+            />
+          {/if}
+        </svg>
+      </button>
+    </div>
+  {/if}
 
   <!-- Center: Logo (clickable to go home) -->
   <button
@@ -136,55 +83,19 @@
     <img
       src="/exo-logo.png"
       alt="EXO"
-      class="h-12 md:h-18 drop-shadow-[0_0_4px_rgba(255,215,0,0.3)]"
+      class="h-18 drop-shadow-[0_0_4px_rgba(255,215,0,0.3)]"
     />
   </button>
 
-  <!-- Right: Home + Downloads + Mobile Right Toggle -->
+  <!-- Right: Home + Downloads -->
   <nav
-    class="absolute right-4 md:right-6 top-1/2 -translate-y-1/2 flex items-center gap-2 md:gap-4"
+    class="absolute right-6 top-1/2 -translate-y-1/2 flex items-center gap-4"
     aria-label="Main navigation"
   >
-    <!-- Mobile right sidebar toggle (instances/models) - only show when not in chat mode -->
-    {#if showMobileRightToggle}
-      <button
-        onclick={handleToggleMobileRight}
-        class="p-2 rounded border border-exo-light-gray/30 hover:border-exo-yellow/50 hover:bg-exo-medium-gray/30 transition-colors cursor-pointer md:hidden"
-        title={mobileRightOpen ? "Hide instances" : "Show instances"}
-        aria-label={mobileRightOpen
-          ? "Hide instances panel"
-          : "Show instances panel"}
-        aria-pressed={mobileRightOpen}
-      >
-        <svg
-          fill="none"
-          viewBox="0 0 24 24"
-          stroke="currentColor"
-          stroke-width="2"
-          class="w-5 h-5 {mobileRightOpen
-            ? 'text-exo-yellow'
-            : 'text-exo-light-gray'}"
-        >
-          {#if mobileRightOpen}
-            <path
-              stroke-linecap="round"
-              stroke-linejoin="round"
-              d="M13 5l7 7-7 7M5 5l7 7-7 7"
-            ></path>
-          {:else}
-            <path
-              stroke-linecap="round"
-              stroke-linejoin="round"
-              d="M11 19l-7-7 7-7m8 14l-7-7 7-7"
-            ></path>
-          {/if}
-        </svg>
-      </button>
-    {/if}
     {#if showHome}
       <button
         onclick={handleHome}
-        class="flex text-sm text-white/70 hover:text-exo-yellow transition-colors tracking-wider uppercase items-center gap-2 cursor-pointer"
+        class="text-sm text-white/70 hover:text-exo-yellow transition-colors tracking-wider uppercase flex items-center gap-2 cursor-pointer"
         title="Back to topology view"
       >
         <svg
@@ -200,12 +111,12 @@
             d="M3 12l2-2m0 0l7-7 7 7M5 10v10a1 1 0 001 1h3m10-11l2 2m-2-2v10a1 1 0 01-1 1h-3m-6 0a1 1 0 001-1v-4a1 1 0 011-1h2a1 1 0 011 1v4a1 1 0 001 1m-6 0h6"
           />
         </svg>
-        <span class="hidden sm:inline">Home</span>
+        Home
       </button>
     {/if}
     <a
       href="/#/downloads"
-      class="text-xs md:text-sm text-white/70 hover:text-exo-yellow transition-colors tracking-wider uppercase flex items-center gap-1.5 md:gap-2 cursor-pointer"
+      class="text-sm text-white/70 hover:text-exo-yellow transition-colors tracking-wider uppercase flex items-center gap-2 cursor-pointer"
       title="View downloads overview"
     >
       {#if downloadProgress}
@@ -257,7 +168,7 @@
           <path d="M5 21h14" />
         </svg>
       {/if}
-      <span class="hidden sm:inline">Downloads</span>
+      Downloads
     </a>
   </nav>
 </header>

dashboard/src/lib/components/HuggingFaceResultItem.svelte

@@ -36,12 +36,8 @@
     return num.toString();
   }
 
-  // Show short name for mlx-community models, full ID for everything else
-  const modelName = $derived(
-    model.author === "mlx-community"
-      ? model.id.split("/").pop() || model.id
-      : model.id,
-  );
+  // Extract model name from full ID (e.g., "mlx-community/Llama-3.2-1B" -> "Llama-3.2-1B")
+  const modelName = $derived(model.id.split("/").pop() || model.id);
 </script>
 
 <div

dashboard/src/lib/components/MarkdownContent.svelte

@@ -507,29 +507,9 @@
   });
 
   $effect(() => {
-    if (!containerRef || !browser) return;
-
-    function handleDelegatedClick(event: MouseEvent) {
-      const codeBtn = (event.target as HTMLElement).closest(
-        ".copy-code-btn",
-      ) as HTMLButtonElement | null;
-      if (codeBtn) {
-        handleCopyClick({ currentTarget: codeBtn } as unknown as Event);
-        return;
-      }
-      const mathBtn = (event.target as HTMLElement).closest(
-        ".copy-math-btn",
-      ) as HTMLButtonElement | null;
-      if (mathBtn) {
-        handleMathCopyClick({ currentTarget: mathBtn } as unknown as Event);
-        return;
-      }
+    if (containerRef && processedHtml) {
+      setupCopyButtons();
     }
-
-    containerRef.addEventListener("click", handleDelegatedClick);
-    return () => {
-      containerRef?.removeEventListener("click", handleDelegatedClick);
-    };
   });
 </script>
 

dashboard/src/lib/components/ModelCard.svelte

@@ -169,10 +169,8 @@
 
   function getDeviceType(
     name: string,
-  ): "macbook" | "studio" | "mini" | "dgx" | "linux" | "unknown" {
+  ): "macbook" | "studio" | "mini" | "unknown" {
     const lower = name.toLowerCase();
-    if (lower.includes("dgx") || lower.includes("gx10")) return "dgx";
-    if (lower.includes("linux")) return "linux";
     if (lower.includes("macbook")) return "macbook";
     if (lower.includes("studio")) return "studio";
     if (lower.includes("mini")) return "mini";
@@ -280,7 +278,7 @@
     let placementNodes: Array<{
       id: string;
       deviceName: string;
-      deviceType: "macbook" | "studio" | "mini" | "dgx" | "linux" | "unknown";
+      deviceType: "macbook" | "studio" | "mini" | "unknown";
       totalGB: number;
       currentUsedGB: number;
       modelUsageGB: number;
@@ -970,137 +968,6 @@
                     />
                   {/if}
                 </g>
-              {:else if node.deviceType === "dgx"}
-                <!-- DGX Spark icon -->
-                {@const s = node.iconSize}
-                {@const dgxW = s * 1.4}
-                {@const dgxH = s * 0.52}
-                <g transform="translate({-dgxW / 2}, {-dgxH / 2})">
-                  <!-- Chassis -->
-                  <rect
-                    x="0"
-                    y="0"
-                    width={dgxW}
-                    height={dgxH}
-                    rx="2"
-                    fill="#6f6248"
-                    stroke={node.isUsed ? "#FFD700" : "#4B5563"}
-                    stroke-width="1.5"
-                  />
-                  <!-- Side accents -->
-                  <rect
-                    x="0"
-                    y="0"
-                    width={dgxW * 0.02}
-                    height={dgxH}
-                    fill="#8a7a56"
-                  />
-                  <rect
-                    x={dgxW - dgxW * 0.02}
-                    y="0"
-                    width={dgxW * 0.02}
-                    height={dgxH}
-                    fill="#8a7a56"
-                  />
-                  <!-- Left handle -->
-                  <rect
-                    x={dgxW * 0.04}
-                    y={dgxH * 0.08}
-                    width={dgxW * 0.22}
-                    height={dgxH * 0.84}
-                    rx="2"
-                    fill="#b3a170"
-                    stroke="#403723"
-                    stroke-width="0.5"
-                  />
-                  <!-- Right handle -->
-                  <rect
-                    x={dgxW - dgxW * 0.04 - dgxW * 0.22}
-                    y={dgxH * 0.08}
-                    width={dgxW * 0.22}
-                    height={dgxH * 0.84}
-                    rx="2"
-                    fill="#b3a170"
-                    stroke="#403723"
-                    stroke-width="0.5"
-                  />
-                  <!-- Memory fill -->
-                  <rect
-                    x="2"
-                    y={dgxH - dgxH * (node.currentPercent / 100)}
-                    width={dgxW - 4}
-                    height={dgxH * (node.currentPercent / 100)}
-                    fill="rgba(255,215,0,0.35)"
-                  />
-                  {#if node.modelUsageGB > 0 && node.isUsed}
-                    <rect
-                      x="2"
-                      y={dgxH - dgxH * (node.newPercent / 100)}
-                      width={dgxW - 4}
-                      height={dgxH *
-                        ((node.newPercent - node.currentPercent) / 100)}
-                      fill="#FFD700"
-                      filter="url(#memGlow-{filterId})"
-                      class="animate-pulse-slow"
-                    />
-                  {/if}
-                </g>
-              {:else if node.deviceType === "linux"}
-                <!-- Linux Tux penguin icon -->
-                {@const sz = node.iconSize}
-                {@const sc = sz / 100}
-                <g transform="translate({-sz / 2}, {-sz / 2})">
-                  <!-- Body -->
-                  <path
-                    d="M50 8c-8 0-14 6-14 13 0 4 2 8 5 10-8 4-16 14-16 28v12c0 4 2 7 5 9l-6 4c-2 1-3 3-3 5v3c0 2 2 4 4 4h10l4-4h22l4 4h10c2 0 4-2 4-4v-3c0-2-1-4-3-5l-6-4c3-2 5-5 5-9V59c0-14-8-24-16-28 3-2 5-6 5-10 0-7-6-13-14-13z"
-                    transform="scale({sc})"
-                    fill="#1a1a1a"
-                    stroke={node.isUsed ? "#FFD700" : "#4B5563"}
-                    stroke-width={1.5 / sc}
-                  />
-                  <!-- Belly -->
-                  <path
-                    d="M38 52c0-8 5-15 12-15s12 7 12 15v14c0 4-5 7-12 7s-12-3-12-7V52z"
-                    transform="scale({sc})"
-                    fill="rgba(220,220,220,0.85)"
-                  />
-                  <!-- Eyes -->
-                  <circle cx={44 * sc} cy={16 * sc} r={2.5 * sc} fill="white" />
-                  <circle cx={56 * sc} cy={16 * sc} r={2.5 * sc} fill="white" />
-                  <circle
-                    cx={44 * sc}
-                    cy={16 * sc}
-                    r={1.2 * sc}
-                    fill="#1a1a1a"
-                  />
-                  <circle
-                    cx={56 * sc}
-                    cy={16 * sc}
-                    r={1.2 * sc}
-                    fill="#1a1a1a"
-                  />
-                  <!-- Beak -->
-                  <path
-                    d="M{46 * sc} {22 * sc} L{50 * sc} {27 * sc} L{54 *
-                      sc} {22 * sc} Z"
-                    fill="#E8A317"
-                  />
-                  <!-- Feet -->
-                  <ellipse
-                    cx={42 * sc}
-                    cy={94 * sc}
-                    rx={6 * sc}
-                    ry={2.5 * sc}
-                    fill="#E8A317"
-                  />
-                  <ellipse
-                    cx={58 * sc}
-                    cy={94 * sc}
-                    rx={6 * sc}
-                    ry={2.5 * sc}
-                    fill="#E8A317"
-                  />
-                </g>
               {:else}
                 <!-- Unknown device - hexagon -->
                 <g

dashboard/src/lib/components/ModelPickerGroup.svelte

@@ -32,7 +32,6 @@
     group: ModelGroup;
     isExpanded: boolean;
     isFavorite: boolean;
-    isHighlighted?: boolean;
     selectedModelId: string | null;
     canModelFit: (id: string) => boolean;
     getModelFitStatus: (id: string) => ModelFitStatus;
@@ -49,7 +48,6 @@
     group,
     isExpanded,
     isFavorite,
-    isHighlighted = false,
     selectedModelId,
     canModelFit,
     getModelFitStatus,
@@ -152,11 +150,10 @@
 </script>
 
 <div
-  data-model-ids={group.variants.map((v) => v.id).join(" ")}
   class="border-b border-white/5 last:border-b-0 {!anyVariantFits &&
   !anyVariantHasInstance
     ? 'opacity-50'
-    : ''} {isHighlighted ? 'model-just-added' : ''}"
+    : ''}"
 >
   <!-- Main row -->
   <div
@@ -647,21 +644,3 @@
     </div>
   {/if}
 </div>
-
-<style>
-  .model-just-added {
-    animation: highlightFade 4s ease-out forwards;
-  }
-
-  @keyframes highlightFade {
-    0%,
-    40% {
-      background-color: rgba(20, 83, 45, 0.25);
-      box-shadow: inset 0 0 0 1px rgba(74, 222, 128, 0.4);
-    }
-    100% {
-      background-color: transparent;
-      box-shadow: none;
-    }
-  }
-</style>

dashboard/src/lib/components/ModelPickerModal.svelte

@@ -1,5 +1,4 @@
 <script lang="ts">
-  import { tick } from "svelte";
   import { fade, fly } from "svelte/transition";
   import { cubicOut } from "svelte/easing";
   import FamilySidebar from "./FamilySidebar.svelte";
@@ -8,7 +7,6 @@
   import HuggingFaceResultItem from "./HuggingFaceResultItem.svelte";
   import { getNodesWithModelDownloaded } from "$lib/utils/downloads";
   import { getRecentEntries } from "$lib/stores/recents.svelte";
-  import { addToast } from "$lib/stores/toast.svelte";
 
   interface ModelInfo {
     id: string;
@@ -193,13 +191,6 @@
   let hfSearchDebounceTimer: ReturnType<typeof setTimeout> | null = null;
   let manualModelId = $state("");
   let addModelError = $state<string | null>(null);
-  let justAddedModelId = $state<string | null>(null);
-  let justAddedTimer: ReturnType<typeof setTimeout> | null = null;
-
-  // Inline HuggingFace search in main search bar
-  let mainSearchHfResults = $state<HuggingFaceModel[]>([]);
-  let mainSearchHfLoading = $state(false);
-  let mainSearchDebounceTimer: ReturnType<typeof setTimeout> | null = null;
 
   // Reset transient state when modal opens, but preserve tab selection
   $effect(() => {
@@ -209,11 +200,6 @@
       showFilters = false;
       manualModelId = "";
       addModelError = null;
-      justAddedModelId = null;
-      if (justAddedTimer) {
-        clearTimeout(justAddedTimer);
-        justAddedTimer = null;
-      }
     }
   });
 
@@ -228,50 +214,6 @@
     }
   });
 
-  // Inline HuggingFace search when local search returns no results
-  $effect(() => {
-    const query = searchQuery.trim();
-    const noLocalResults = filteredGroups.length === 0;
-
-    if (mainSearchDebounceTimer) {
-      clearTimeout(mainSearchDebounceTimer);
-      mainSearchDebounceTimer = null;
-    }
-
-    if (
-      selectedFamily === "huggingface" ||
-      selectedFamily === "recents" ||
-      selectedFamily === "favorites" ||
-      query.length < 2 ||
-      !noLocalResults
-    ) {
-      mainSearchHfResults = [];
-      mainSearchHfLoading = false;
-      return;
-    }
-
-    mainSearchHfLoading = true;
-    mainSearchDebounceTimer = setTimeout(async () => {
-      try {
-        const response = await fetch(
-          `/models/search?query=${encodeURIComponent(query)}&limit=10`,
-        );
-        if (response.ok) {
-          const results: HuggingFaceModel[] = await response.json();
-          mainSearchHfResults = results.filter(
-            (r) => !existingModelIds.has(r.id),
-          );
-        } else {
-          mainSearchHfResults = [];
-        }
-      } catch {
-        mainSearchHfResults = [];
-      } finally {
-        mainSearchHfLoading = false;
-      }
-    }, 500);
-  });
-
   async function fetchTrendingModels() {
     hfIsLoadingTrending = true;
     try {
@@ -332,24 +274,6 @@
     addModelError = null;
     try {
       await onAddModel(modelId);
-      // Success: show toast, switch to All Models, highlight the model
-      const shortName = modelId.split("/").pop() || modelId;
-      addToast({ type: "success", message: `Added ${shortName}` });
-      justAddedModelId = modelId;
-      selectedFamily = null;
-      searchQuery = "";
-      // Scroll to the newly added model after DOM update
-      await tick();
-      const el = document.querySelector(
-        `[data-model-ids~="${CSS.escape(modelId)}"]`,
-      );
-      el?.scrollIntoView({ behavior: "smooth", block: "center" });
-      // Clear highlight after 4 seconds
-      if (justAddedTimer) clearTimeout(justAddedTimer);
-      justAddedTimer = setTimeout(() => {
-        justAddedModelId = null;
-        justAddedTimer = null;
-      }, 4000);
     } catch (error) {
       addModelError =
         error instanceof Error ? error.message : "Failed to add model";
@@ -379,16 +303,12 @@
     return hfTrendingModels;
   });
 
-  function normalizeBaseModel(s: string): string {
-    return s.toLowerCase().replace(/[-_]/g, " ").trim();
-  }
-
   // Group models by base_model
   const groupedModels = $derived.by((): ModelGroup[] => {
     const groups = new Map<string, ModelGroup>();
 
     for (const model of models) {
-      const groupId = normalizeBaseModel(model.base_model || model.id);
+      const groupId = model.base_model || model.id;
       const groupName = model.base_model || model.name || model.id;
 
       if (!groups.has(groupId)) {
@@ -582,7 +502,7 @@
       const model = models.find((m) => m.id === id);
       if (model) {
         result.push({
-          id: normalizeBaseModel(model.base_model || model.id),
+          id: model.base_model || model.id,
           name: model.name || model.id,
           capabilities: model.capabilities || ["text"],
           family: model.family || "",
@@ -921,8 +841,6 @@
                 {group}
                 isExpanded={expandedGroups.has(group.id)}
                 isFavorite={favorites.has(group.id)}
-                isHighlighted={justAddedModelId !== null &&
-                  group.variants.some((v) => v.id === justAddedModelId)}
                 {selectedModelId}
                 {canModelFit}
                 {getModelFitStatus}
@@ -992,8 +910,6 @@
               {group}
               isExpanded={expandedGroups.has(group.id)}
               isFavorite={favorites.has(group.id)}
-              isHighlighted={justAddedModelId !== null &&
-                group.variants.some((v) => v.id === justAddedModelId)}
               {selectedModelId}
               {canModelFit}
               {getModelFitStatus}
@@ -1021,8 +937,6 @@
               {group}
               isExpanded={expandedGroups.has(group.id)}
               isFavorite={favorites.has(group.id)}
-              isHighlighted={justAddedModelId !== null &&
-                group.variants.some((v) => v.id === justAddedModelId)}
               {selectedModelId}
               {canModelFit}
               {getModelFitStatus}
@@ -1034,55 +948,6 @@
               {instanceStatuses}
             />
           {/each}
-          <!-- Inline HuggingFace search results (shown when no local results match) -->
-          {#if filteredGroups.length === 0 && searchQuery.trim().length >= 2 && selectedFamily !== "huggingface" && selectedFamily !== "recents" && selectedFamily !== "favorites"}
-            {#if mainSearchHfLoading}
-              <div
-                class="flex items-center gap-2 px-3 py-2 border-t border-orange-400/20 bg-orange-950/20"
-              >
-                <span
-                  class="w-4 h-4 border-2 border-orange-400 border-t-transparent rounded-full animate-spin"
-                ></span>
-                <span class="text-xs font-mono text-orange-400/60"
-                  >Searching HuggingFace...</span
-                >
-              </div>
-            {:else if mainSearchHfResults.length > 0}
-              <div
-                class="sticky top-0 z-10 flex items-center gap-2 px-3 py-2 bg-orange-950/30 border-y border-orange-400/20 backdrop-blur-sm"
-              >
-                <span
-                  class="text-xs font-mono text-orange-400 tracking-wider uppercase"
-                  >From HuggingFace</span
-                >
-              </div>
-              {#each mainSearchHfResults as model}
-                <HuggingFaceResultItem
-                  {model}
-                  isAdded={existingModelIds.has(model.id)}
-                  isAdding={addingModelId === model.id}
-                  onAdd={() => handleAddModel(model.id)}
-                  onSelect={() => handleSelectHfModel(model.id)}
-                  downloadedOnNodes={downloadsData
-                    ? getNodesWithModelDownloaded(downloadsData, model.id).map(
-                        getNodeName,
-                      )
-                    : []}
-                />
-              {/each}
-              <button
-                type="button"
-                class="w-full px-3 py-2 text-xs font-mono text-orange-400/60 hover:text-orange-400 hover:bg-orange-500/10 transition-colors text-center"
-                onclick={() => {
-                  hfSearchQuery = searchQuery;
-                  searchHuggingFace(searchQuery);
-                  selectedFamily = "huggingface";
-                }}
-              >
-                See all results on Hub
-              </button>
-            {/if}
-          {/if}
         {/if}
       </div>
     </div>

dashboard/src/lib/components/TopologyGraph.svelte

@@ -117,10 +117,6 @@
   const LOGO_NATIVE_WIDTH = 814;
   const LOGO_NATIVE_HEIGHT = 1000;
 
-  // NVIDIA logo SVG path (from exo-nvidia)
-  const NVIDIA_LOGO_PATH =
-    "M0.81 0.429V0.299c0.013 -0.001 0.026 -0.002 0.038 -0.002 0.355 -0.011 0.588 0.306 0.588 0.306S1.186 0.952 0.916 0.952c-0.036 0 -0.071 -0.006 -0.105 -0.017V0.542c0.138 0.017 0.166 0.078 0.249 0.216l0.185 -0.155s-0.135 -0.177 -0.362 -0.177c-0.024 -0.001 -0.048 0.001 -0.072 0.003m0 -0.429v0.194l0.038 -0.002c0.494 -0.017 0.816 0.405 0.816 0.405s-0.37 0.45 -0.754 0.45c-0.034 0 -0.066 -0.003 -0.099 -0.009v0.12c0.027 0.003 0.055 0.006 0.082 0.006 0.358 0 0.618 -0.183 0.869 -0.399 0.042 0.034 0.212 0.114 0.247 0.15 -0.238 0.2 -0.794 0.361 -1.11 0.361 -0.03 0 -0.059 -0.002 -0.088 -0.005v0.169h1.362V0zm0 0.935v0.102c-0.331 -0.059 -0.423 -0.404 -0.423 -0.404s0.159 -0.176 0.423 -0.205v0.112h-0.001C0.671 0.524 0.562 0.654 0.562 0.654s0.062 0.218 0.248 0.282m-0.588 -0.316s0.196 -0.29 0.589 -0.32V0.194C0.376 0.229 0 0.597 0 0.597s0.213 0.616 0.81 0.672v-0.112c-0.438 -0.054 -0.588 -0.538 -0.588 -0.538";
-
   function formatBytes(bytes: number, decimals = 1): string {
     if (!bytes || bytes === 0) return "0B";
     const k = 1024;
@@ -558,13 +554,6 @@
       const clipPathId = `clip-${nodeInfo.id.replace(/[^a-zA-Z0-9]/g, "-")}`;
 
       const modelLower = modelId.toLowerCase();
-      const identity = identitiesData[nodeInfo.id];
-      const nameLower = (friendlyName || "").toLowerCase();
-      const isSpark = modelLower.includes("dgx") || modelLower.includes("gx10");
-      const isLinux =
-        !isSpark &&
-        (modelLower.startsWith("linux") || identity?.osVersion === "Linux");
-      const isLinuxLaptop = isLinux && modelLower.includes("laptop");
 
       // Check node states for styling
       const isHighlighted = highlightedNodes.has(nodeInfo.id);
@@ -634,382 +623,7 @@
           `${friendlyName}\nID: ${nodeInfo.id.slice(-8)}\nMemory: ${formatBytes(ramUsed)}/${formatBytes(ramTotal)}`,
         );
 
-      if (isSpark) {
-        // NVIDIA DGX Spark — gold chassis with textured front, side handles, and NVIDIA badge
-        iconBaseWidth = nodeRadius * 1.55;
-        iconBaseHeight = nodeRadius * 0.58;
-        const x = nodeInfo.x - iconBaseWidth / 2;
-        const y = nodeInfo.y - iconBaseHeight / 2;
-        const chassisX = x - iconBaseWidth * 0.03;
-        const chassisWidth = iconBaseWidth * 1.05;
-        const cornerRadius = 3;
-
-        const dgxClipId = `dgx-clip-${nodeInfo.id.replace(/[^a-zA-Z0-9]/g, "-")}`;
-        defs
-          .append("clipPath")
-          .attr("id", dgxClipId)
-          .append("rect")
-          .attr("x", x)
-          .attr("y", y)
-          .attr("width", iconBaseWidth)
-          .attr("height", iconBaseHeight)
-          .attr("rx", cornerRadius);
-
-        // Chassis texture pattern
-        const textureId = `chassis-texture-${nodeInfo.id.replace(/[^a-zA-Z0-9]/g, "-")}`;
-        defs
-          .append("pattern")
-          .attr("id", textureId)
-          .attr("patternUnits", "userSpaceOnUse")
-          .attr("width", 8)
-          .attr("height", 8);
-        const texturePattern = defs.select(`#${textureId}`);
-        texturePattern
-          .append("rect")
-          .attr("width", 8)
-          .attr("height", 8)
-          .attr("fill", "#6f6248");
-        texturePattern
-          .append("circle")
-          .attr("cx", 2)
-          .attr("cy", 2)
-          .attr("r", 1)
-          .attr("fill", "#5a4f3b")
-          .attr("opacity", 0.5);
-        texturePattern
-          .append("circle")
-          .attr("cx", 6)
-          .attr("cy", 6)
-          .attr("r", 1)
-          .attr("fill", "#4a4232")
-          .attr("opacity", 0.45);
-
-        // Main body
-        nodeG
-          .append("rect")
-          .attr("class", "node-outline")
-          .attr("x", chassisX)
-          .attr("y", y)
-          .attr("width", chassisWidth)
-          .attr("height", iconBaseHeight)
-          .attr("rx", cornerRadius)
-          .attr("fill", `url(#${textureId})`)
-          .attr("stroke", wireColor)
-          .attr("stroke-width", strokeWidth);
-
-        // Side border accents
-        const sideThickness = iconBaseWidth * 0.02;
-        nodeG
-          .append("rect")
-          .attr("x", chassisX)
-          .attr("y", y)
-          .attr("width", sideThickness)
-          .attr("height", iconBaseHeight)
-          .attr("fill", "#8a7a56");
-        nodeG
-          .append("rect")
-          .attr("x", chassisX + chassisWidth - sideThickness)
-          .attr("y", y)
-          .attr("width", sideThickness)
-          .attr("height", iconBaseHeight)
-          .attr("fill", "#8a7a56");
-
-        // Memory fill (bottom up)
-        if (ramUsagePercent > 0) {
-          const memFillHeight = (ramUsagePercent / 100) * iconBaseHeight;
-          nodeG
-            .append("rect")
-            .attr("x", x)
-            .attr("y", y + iconBaseHeight - memFillHeight)
-            .attr("width", iconBaseWidth)
-            .attr("height", memFillHeight)
-            .attr("fill", "rgba(255,215,0,0.45)")
-            .attr("clip-path", `url(#${dgxClipId})`);
-        }
-
-        // Side handles with inner recess
-        const handleWidth = iconBaseWidth * 0.27;
-        const handleGap = iconBaseHeight * 0.05;
-        const handleHeight = iconBaseHeight - handleGap * 2;
-        const handleY = y + handleGap;
-        const innerHandleWidth = iconBaseWidth * 0.12;
-        const innerHandleHeight = handleHeight - iconBaseHeight * 0.06;
-        const leftHandleX = x + 4;
-        const rightHandleX = x + iconBaseWidth - handleWidth - 4;
-
-        // Left handle
-        nodeG
-          .append("rect")
-          .attr("x", leftHandleX)
-          .attr("y", handleY)
-          .attr("width", handleWidth)
-          .attr("height", handleHeight)
-          .attr("rx", 2.4)
-          .attr("fill", "#b3a170")
-          .attr("stroke", "#403723")
-          .attr("stroke-width", 0.7);
-        nodeG
-          .append("rect")
-          .attr("x", leftHandleX + handleWidth * 0.06)
-          .attr("y", handleY + iconBaseHeight * 0.03)
-          .attr("width", innerHandleWidth)
-          .attr("height", innerHandleHeight)
-          .attr("rx", 1.6)
-          .attr("fill", "#8a7a56");
-
-        // Right handle
-        nodeG
-          .append("rect")
-          .attr("x", rightHandleX)
-          .attr("y", handleY)
-          .attr("width", handleWidth)
-          .attr("height", handleHeight)
-          .attr("rx", 2.4)
-          .attr("fill", "#b3a170")
-          .attr("stroke", "#403723")
-          .attr("stroke-width", 0.7);
-        nodeG
-          .append("rect")
-          .attr(
-            "x",
-            rightHandleX + handleWidth - innerHandleWidth - handleWidth * 0.08,
-          )
-          .attr("y", handleY + iconBaseHeight * 0.03)
-          .attr("width", innerHandleWidth)
-          .attr("height", innerHandleHeight)
-          .attr("rx", 1.6)
-          .attr("fill", "#8a7a56");
-
-        // NVIDIA logo + text label (rotated 90 deg on left handle)
-        const badgeWidth = iconBaseWidth * 0.09;
-        const badgeHeight = handleHeight * 0.5;
-        const badgeX =
-          leftHandleX + handleWidth - badgeWidth - handleWidth * 0.06;
-        const badgeY = handleY + (handleHeight - badgeHeight) / 2;
-        const textSize = badgeWidth * 0.58;
-        const logoWidth = textSize * 1.2;
-        const logoHeight = logoWidth * (1.438 / 2.174);
-        const centerX = badgeX + badgeWidth / 2 - badgeWidth * 0.03;
-        const centerY = badgeY + badgeHeight / 2;
-        const gap = badgeWidth * 0.15;
-        const totalWidth = logoWidth + gap + textSize * 3.6;
-
-        const labelGroup = nodeG
-          .append("g")
-          .attr("transform", `rotate(90 ${centerX} ${centerY})`);
-
-        labelGroup
-          .append("svg")
-          .attr("x", centerX - totalWidth / 2)
-          .attr("y", centerY - logoHeight / 2)
-          .attr("width", logoWidth)
-          .attr("height", logoHeight)
-          .attr("viewBox", "0 0 2.174 1.438")
-          .append("path")
-          .attr("d", NVIDIA_LOGO_PATH)
-          .attr("fill", "#76b900");
-
-        labelGroup
-          .append("text")
-          .attr("x", centerX - totalWidth / 2 + logoWidth + gap)
-          .attr("y", centerY)
-          .attr("text-anchor", "start")
-          .attr("dominant-baseline", "middle")
-          .attr("fill", "#8a7a56")
-          .attr("font-size", textSize)
-          .attr("font-family", "monospace")
-          .attr("font-weight", "700")
-          .text("NVIDIA");
-      } else if (isLinuxLaptop) {
-        // Linux Laptop — same shape as MacBook but with Tux logo
-        iconBaseWidth = nodeRadius * 1.6;
-        iconBaseHeight = nodeRadius * 1.15;
-        const x = nodeInfo.x - iconBaseWidth / 2;
-        const y = nodeInfo.y - iconBaseHeight / 2;
-
-        const screenHeight = iconBaseHeight * 0.7;
-        const baseHeight = iconBaseHeight * 0.3;
-        const screenWidth = iconBaseWidth * 0.85;
-        const screenX = nodeInfo.x - screenWidth / 2;
-        const screenBezel = 3;
-
-        const linuxScreenClipId = `linux-screen-${nodeInfo.id.replace(/[^a-zA-Z0-9]/g, "-")}`;
-        defs
-          .append("clipPath")
-          .attr("id", linuxScreenClipId)
-          .append("rect")
-          .attr("x", screenX + screenBezel)
-          .attr("y", y + screenBezel)
-          .attr("width", screenWidth - screenBezel * 2)
-          .attr("height", screenHeight - screenBezel * 2)
-          .attr("rx", 2);
-
-        // Screen outer frame
-        nodeG
-          .append("rect")
-          .attr("class", "node-outline")
-          .attr("x", screenX)
-          .attr("y", y)
-          .attr("width", screenWidth)
-          .attr("height", screenHeight)
-          .attr("rx", 3)
-          .attr("fill", "#1a1a1a")
-          .attr("stroke", wireColor)
-          .attr("stroke-width", strokeWidth);
-
-        // Screen inner
-        nodeG
-          .append("rect")
-          .attr("x", screenX + screenBezel)
-          .attr("y", y + screenBezel)
-          .attr("width", screenWidth - screenBezel * 2)
-          .attr("height", screenHeight - screenBezel * 2)
-          .attr("rx", 2)
-          .attr("fill", "#0a0a12");
-
-        // Memory fill on screen
-        if (ramUsagePercent > 0) {
-          const memFillTotalHeight = screenHeight - screenBezel * 2;
-          const memFillActualHeight =
-            (ramUsagePercent / 100) * memFillTotalHeight;
-          nodeG
-            .append("rect")
-            .attr("x", screenX + screenBezel)
-            .attr(
-              "y",
-              y + screenBezel + (memFillTotalHeight - memFillActualHeight),
-            )
-            .attr("width", screenWidth - screenBezel * 2)
-            .attr("height", memFillActualHeight)
-            .attr("fill", "rgba(255,215,0,0.85)")
-            .attr("clip-path", `url(#${linuxScreenClipId})`);
-        }
-
-        // Terminal prompt on screen
-        nodeG
-          .append("text")
-          .attr("x", nodeInfo.x)
-          .attr("y", y + screenHeight / 2)
-          .attr("text-anchor", "middle")
-          .attr("dominant-baseline", "middle")
-          .attr("fill", "#FFFFFF")
-          .attr("opacity", 0.9)
-          .attr("font-size", screenHeight * 0.25)
-          .attr("font-family", "SF Mono, Monaco, monospace")
-          .attr("font-weight", "700")
-          .text(">_");
-
-        // Keyboard base (trapezoidal)
-        const baseY = y + screenHeight;
-        const baseTopWidth = screenWidth;
-        const baseBottomWidth = iconBaseWidth;
-        const baseTopX = nodeInfo.x - baseTopWidth / 2;
-        const baseBottomX = nodeInfo.x - baseBottomWidth / 2;
-
-        nodeG
-          .append("path")
-          .attr(
-            "d",
-            `M ${baseTopX} ${baseY} L ${baseTopX + baseTopWidth} ${baseY} L ${baseBottomX + baseBottomWidth} ${baseY + baseHeight} L ${baseBottomX} ${baseY + baseHeight} Z`,
-          )
-          .attr("fill", "#2c2c2c")
-          .attr("stroke", wireColor)
-          .attr("stroke-width", 1);
-
-        // Keyboard area
-        const keyboardX = baseTopX + 6;
-        const keyboardY = baseY + 3;
-        const keyboardWidth = baseTopWidth - 12;
-        const keyboardHeight = baseHeight * 0.55;
-        nodeG
-          .append("rect")
-          .attr("x", keyboardX)
-          .attr("y", keyboardY)
-          .attr("width", keyboardWidth)
-          .attr("height", keyboardHeight)
-          .attr("fill", "rgba(0,0,0,0.2)")
-          .attr("rx", 2);
-
-        // Trackpad
-        const trackpadWidth = baseTopWidth * 0.4;
-        const trackpadX = nodeInfo.x - trackpadWidth / 2;
-        const trackpadY = baseY + keyboardHeight + 5;
-        const trackpadHeight = baseHeight * 0.3;
-        nodeG
-          .append("rect")
-          .attr("x", trackpadX)
-          .attr("y", trackpadY)
-          .attr("width", trackpadWidth)
-          .attr("height", trackpadHeight)
-          .attr("fill", "rgba(255,255,255,0.08)")
-          .attr("rx", 2);
-      } else if (isLinux) {
-        // Linux Desktop — same shape as Mac Studio but with Tux logo
-        iconBaseWidth = nodeRadius * 1.25;
-        iconBaseHeight = nodeRadius * 0.85;
-        const x = nodeInfo.x - iconBaseWidth / 2;
-        const y = nodeInfo.y - iconBaseHeight / 2;
-        const cornerRadius = 4;
-        const topSurfaceHeight = iconBaseHeight * 0.15;
-
-        const linuxDesktopClipId = `linux-desktop-${nodeInfo.id.replace(/[^a-zA-Z0-9]/g, "-")}`;
-        defs
-          .append("clipPath")
-          .attr("id", linuxDesktopClipId)
-          .append("rect")
-          .attr("x", x)
-          .attr("y", y + topSurfaceHeight)
-          .attr("width", iconBaseWidth)
-          .attr("height", iconBaseHeight - topSurfaceHeight)
-          .attr("rx", cornerRadius - 1);
-
-        // Main body
-        nodeG
-          .append("rect")
-          .attr("class", "node-outline")
-          .attr("x", x)
-          .attr("y", y)
-          .attr("width", iconBaseWidth)
-          .attr("height", iconBaseHeight)
-          .attr("rx", cornerRadius)
-          .attr("fill", "#1a1a1a")
-          .attr("stroke", wireColor)
-          .attr("stroke-width", strokeWidth);
-
-        // Memory fill
-        if (ramUsagePercent > 0) {
-          const memFillTotalHeight = iconBaseHeight - topSurfaceHeight;
-          const memFillActualHeight =
-            (ramUsagePercent / 100) * memFillTotalHeight;
-          nodeG
-            .append("rect")
-            .attr("x", x)
-            .attr(
-              "y",
-              y + topSurfaceHeight + (memFillTotalHeight - memFillActualHeight),
-            )
-            .attr("width", iconBaseWidth)
-            .attr("height", memFillActualHeight)
-            .attr("fill", "rgba(255,215,0,0.75)")
-            .attr("clip-path", `url(#${linuxDesktopClipId})`);
-        }
-
-        // Terminal prompt on front face
-        nodeG
-          .append("text")
-          .attr("x", nodeInfo.x)
-          .attr(
-            "y",
-            y + topSurfaceHeight + (iconBaseHeight - topSurfaceHeight) / 2,
-          )
-          .attr("text-anchor", "middle")
-          .attr("dominant-baseline", "middle")
-          .attr("fill", "rgba(255,255,255,0.5)")
-          .attr("font-size", (iconBaseHeight - topSurfaceHeight) * 0.4)
-          .attr("font-family", "SF Mono, Monaco, monospace")
-          .attr("font-weight", "700")
-          .text(">_");
-      } else if (modelLower === "mac studio") {
+      if (modelLower === "mac studio") {
         // Mac Studio - classic cube with memory fill
         iconBaseWidth = nodeRadius * 1.25;
         iconBaseHeight = nodeRadius * 0.85;
@@ -1568,12 +1182,8 @@
           debugLabelY += debugLineHeight;
         }
 
-        const dbgIdentity = identitiesData[nodeInfo.id];
-        if (dbgIdentity?.osVersion) {
-          const osLabel =
-            dbgIdentity.osVersion === "Linux"
-              ? "Linux"
-              : `macOS ${dbgIdentity.osVersion}${dbgIdentity.osBuildVersion ? ` (${dbgIdentity.osBuildVersion})` : ""}`;
+        const identity = identitiesData[nodeInfo.id];
+        if (identity?.osVersion) {
           nodeG
             .append("text")
             .attr("x", nodeInfo.x)
@@ -1582,7 +1192,9 @@
             .attr("fill", "rgba(179,179,179,0.7)")
             .attr("font-size", debugFontSize)
             .attr("font-family", "SF Mono, Monaco, monospace")
-            .text(osLabel);
+            .text(
+              `macOS ${identity.osVersion}${identity.osBuildVersion ? ` (${identity.osBuildVersion})` : ""}`,
+            );
         }
       }
     });

dashboard/src/lib/stores/app.svelte.ts

@@ -168,7 +168,7 @@ export interface ModelDownloadStatus {
 export interface PlacementPreview {
   model_id: string;
   sharding: "Pipeline" | "Tensor";
-  instance_meta: "MlxRing" | "MlxJaccl" | "Vllm";
+  instance_meta: "MlxRing" | "MlxJaccl";
   instance: unknown | null;
   memory_delta_by_node: Record<string, number> | null;
   error: string | null;
@@ -547,7 +547,6 @@ class AppStore {
       { total: { inBytes: number }; available: { inBytes: number } }
     >
   >({});
-  vllmAvailable = $state(false);
   placementPreviews = $state<PlacementPreview[]>([]);
   selectedPreviewModelId = $state<string | null>(null);
   isLoadingPreviews = $state(false);
@@ -581,8 +580,6 @@ class AppStore {
   debugMode = $state(false);
   topologyOnlyMode = $state(false);
   chatSidebarVisible = $state(true); // Shown by default
-  mobileChatSidebarOpen = $state(false); // Mobile drawer state
-  mobileRightSidebarOpen = $state(false); // Mobile right drawer state
 
   // Image generation params
   imageGenerationParams = $state<ImageGenerationParams>({
@@ -1248,30 +1245,6 @@ class AppStore {
     this.saveChatSidebarVisibleToStorage();
   }
 
-  getMobileChatSidebarOpen(): boolean {
-    return this.mobileChatSidebarOpen;
-  }
-
-  setMobileChatSidebarOpen(open: boolean) {
-    this.mobileChatSidebarOpen = open;
-  }
-
-  toggleMobileChatSidebar() {
-    this.mobileChatSidebarOpen = !this.mobileChatSidebarOpen;
-  }
-
-  getMobileRightSidebarOpen(): boolean {
-    return this.mobileRightSidebarOpen;
-  }
-
-  setMobileRightSidebarOpen(open: boolean) {
-    this.mobileRightSidebarOpen = open;
-  }
-
-  toggleMobileRightSidebar() {
-    this.mobileRightSidebarOpen = !this.mobileRightSidebarOpen;
-  }
-
   startPolling() {
     this.fetchState();
     this.fetchInterval = setInterval(() => this.fetchState(), 1000);
@@ -1332,15 +1305,6 @@ class AppStore {
         this.isConnected = true;
       }
       this.consecutiveFailures = 0;
-
-      fetch("/capabilities")
-        .then((r) => (r.ok ? r.json() : null))
-        .then((data: { vllm_available?: boolean } | null) => {
-          this.vllmAvailable = data?.vllm_available ?? false;
-        })
-        .catch(() => {
-          this.vllmAvailable = false;
-        });
     } catch (error) {
       this.consecutiveFailures++;
       if (
@@ -1587,7 +1551,6 @@ class AppStore {
     // Remove messages after user message (including the user message for image requests
     // since generateImage/editImage will re-add it)
     this.messages = this.messages.slice(0, lastUserIndex);
-    this.updateActiveConversation();
 
     switch (requestType) {
       case "image-generation":
@@ -3195,23 +3158,6 @@ class AppStore {
     return (await response.json()) as TraceStatsResponse;
   }
 
-  /**
-   * Delete traces by task IDs
-   */
-  async deleteTraces(
-    taskIds: string[],
-  ): Promise<{ deleted: string[]; notFound: string[] }> {
-    const response = await fetch("/v1/traces/delete", {
-      method: "POST",
-      headers: { "Content-Type": "application/json" },
-      body: JSON.stringify({ taskIds }),
-    });
-    if (!response.ok) {
-      throw new Error(`Failed to delete traces: ${response.status}`);
-    }
-    return await response.json();
-  }
-
   /**
    * Get the URL for the raw trace file (for Perfetto)
    */
@@ -3319,18 +3265,6 @@ export const toggleChatSidebarVisible = () =>
   appStore.toggleChatSidebarVisible();
 export const setChatSidebarVisible = (visible: boolean) =>
   appStore.setChatSidebarVisible(visible);
-
-// Mobile sidebar state
-export const mobileChatSidebarOpen = () => appStore.mobileChatSidebarOpen;
-export const toggleMobileChatSidebar = () => appStore.toggleMobileChatSidebar();
-export const setMobileChatSidebarOpen = (open: boolean) =>
-  appStore.setMobileChatSidebarOpen(open);
-export const mobileRightSidebarOpen = () => appStore.mobileRightSidebarOpen;
-export const toggleMobileRightSidebar = () =>
-  appStore.toggleMobileRightSidebar();
-export const setMobileRightSidebarOpen = (open: boolean) =>
-  appStore.setMobileRightSidebarOpen(open);
-
 export const refreshState = () => appStore.fetchState();
 
 // Connection status
@@ -3344,7 +3278,6 @@ export const nodeThunderbolt = () => appStore.nodeThunderbolt;
 export const nodeRdmaCtl = () => appStore.nodeRdmaCtl;
 export const thunderboltBridgeCycles = () => appStore.thunderboltBridgeCycles;
 export const nodeThunderboltBridge = () => appStore.nodeThunderboltBridge;
-export const vllmAvailable = () => appStore.vllmAvailable;
 
 // Image generation params
 export const imageGenerationParams = () => appStore.getImageGenerationParams();
@@ -3368,5 +3301,3 @@ export const fetchTraceStats = (taskId: string) =>
   appStore.fetchTraceStats(taskId);
 export const getTraceRawUrl = (taskId: string) =>
   appStore.getTraceRawUrl(taskId);
-export const deleteTraces = (taskIds: string[]) =>
-  appStore.deleteTraces(taskIds);

dashboard/src/routes/+page.svelte

@@ -42,29 +42,18 @@
     setSelectedChatModel,
     selectedChatModel,
     sendMessage,
-    generateImage,
-    editImage,
-    editingImage,
-    messages,
     debugMode,
     toggleDebugMode,
     topologyOnlyMode,
     toggleTopologyOnlyMode,
     chatSidebarVisible,
     toggleChatSidebarVisible,
-    mobileChatSidebarOpen,
-    toggleMobileChatSidebar,
-    setMobileChatSidebarOpen,
-    mobileRightSidebarOpen,
-    toggleMobileRightSidebar,
-    setMobileRightSidebarOpen,
     nodeThunderbolt,
     nodeRdmaCtl,
     thunderboltBridgeCycles,
     nodeThunderboltBridge,
     nodeIdentities,
     isConnected,
-    vllmAvailable,
     type DownloadProgress,
     type PlacementPreview,
   } from "$lib/stores/app.svelte";
@@ -89,8 +78,6 @@
   const debugEnabled = $derived(debugMode());
   const topologyOnlyEnabled = $derived(topologyOnlyMode());
   const sidebarVisible = $derived(chatSidebarVisible());
-  const mobileChatOpen = $derived(mobileChatSidebarOpen());
-  const mobileRightOpen = $derived(mobileRightSidebarOpen());
   const tbBridgeCycles = $derived(thunderboltBridgeCycles());
   const tbBridgeData = $derived(nodeThunderboltBridge());
   const identitiesData = $derived(nodeIdentities());
@@ -295,7 +282,7 @@
     const seen = new Set<string>();
     const deduped: typeof candidates = [];
     for (const m of candidates) {
-      const key = (m.base_model || m.family || m.id).toLowerCase().replace(/[-_]/g, " ");
+      const key = m.base_model || m.family || m.id;
       if (seen.has(key)) continue;
       seen.add(key);
       deduped.push(m);
@@ -701,10 +688,7 @@
       ? Object.keys(topologyData()!.nodes).length
       : 1;
     const sharding = nodeCount <= 1 ? "Pipeline" : selectedSharding;
-    const instanceType =
-      nodeCount <= 1 && selectedInstanceType !== "Vllm"
-        ? "MlxRing"
-        : selectedInstanceType;
+    const instanceType = nodeCount <= 1 ? "MlxRing" : selectedInstanceType;
     try {
       const placementResponse = await fetch(
         `/instance/placement?model_id=${encodeURIComponent(modelId)}&sharding=${sharding}&instance_meta=${instanceType}&min_nodes=1`,
@@ -841,54 +825,8 @@
     if (!model?.tasks) return false;
     return model.tasks.includes("ImageToImage");
   }
-
-  // Route a message to the correct endpoint based on model capabilities.
-  // Image models go to generateImage/editImage; text models go to sendMessage.
-  function routeMessage(
-    content: string,
-    files?: {
-      id: string;
-      name: string;
-      type: string;
-      textContent?: string;
-      preview?: string;
-    }[],
-  ) {
-    const model = selectedChatModel();
-    if (!model) {
-      sendMessage(content, files, null);
-      return;
-    }
-
-    const currentEditImage = editingImage();
-
-    // Image editing mode (explicit edit or attached image with ImageToImage model)
-    if (currentEditImage && content && modelSupportsImageEditing(model)) {
-      editImage(content, currentEditImage.imageDataUrl);
-      return;
-    }
-    if (
-      modelSupportsImageEditing(model) &&
-      files?.length &&
-      files[0].preview &&
-      content
-    ) {
-      editImage(content, files[0].preview);
-      return;
-    }
-
-    // Text-to-image generation
-    if (modelSupportsImageGeneration(model) && content) {
-      generateImage(content);
-      return;
-    }
-
-    // Default: text chat
-    sendMessage(content, files, null);
-  }
-
   let selectedSharding = $state<"Pipeline" | "Tensor">("Pipeline");
-  type InstanceMeta = "MlxRing" | "MlxJaccl" | "Vllm";
+  type InstanceMeta = "MlxRing" | "MlxJaccl";
 
   // Launch defaults persistence
   const LAUNCH_DEFAULTS_KEY = "exo-launch-defaults-v2";
@@ -934,11 +872,7 @@
     // Apply sharding and instance type unconditionally
     selectedSharding = defaults.sharding;
     selectedInstanceType =
-      defaults.instanceType === "Vllm"
-        ? "Vllm"
-        : defaults.instanceType === "MlxRing"
-          ? "MlxRing"
-          : "MlxJaccl";
+      defaults.instanceType === "MlxRing" ? "MlxRing" : "MlxJaccl";
 
     // Apply minNodes if valid (between 1 and maxNodes)
     if (
@@ -955,7 +889,6 @@
       availableModels.some((m) => m.id === defaults.modelId)
     ) {
       selectPreviewModel(defaults.modelId);
-      setSelectedChatModel(defaults.modelId);
     }
   }
 
@@ -1152,7 +1085,9 @@
   }
 
   const matchesSelectedRuntime = (runtime: InstanceMeta): boolean =>
-    runtime === selectedInstanceType;
+    selectedInstanceType === "MlxRing"
+      ? runtime === "MlxRing"
+      : runtime === "MlxJaccl";
 
   // Helper to check if a model can be launched (has valid placement with >= minNodes)
   function canModelFit(modelId: string): boolean {
@@ -1389,7 +1324,6 @@
 
   function handleModelPickerSelect(modelId: string) {
     selectPreviewModel(modelId);
-    setSelectedChatModel(modelId);
     saveLaunchDefaults();
     isModelPickerOpen = false;
   }
@@ -1582,11 +1516,7 @@
           downloadKind
         ] as Record<string, unknown>;
 
-        if (
-          downloadKind !== "DownloadOngoing" &&
-          downloadKind !== "DownloadPending"
-        )
-          continue;
+        if (downloadKind !== "DownloadOngoing") continue;
         if (!downloadPayload) continue;
 
         const downloadModelId = extractModelIdFromDownload(downloadPayload);
@@ -1601,38 +1531,9 @@
           if (downloadModelId !== modelId) continue;
         }
 
-        // For DownloadPending with partial bytes (paused/resumed downloads),
-        // synthesize a progress object from the top-level downloaded/total fields
-        let progress: DownloadProgress | null;
-        if (downloadKind === "DownloadPending") {
-          const pendingDownloaded = getBytes(
-            downloadPayload.downloaded ??
-              downloadPayload.downloaded_bytes ??
-              downloadPayload.downloadedBytes,
-          );
-          const pendingTotal = getBytes(
-            downloadPayload.total ??
-              downloadPayload.total_bytes ??
-              downloadPayload.totalBytes,
-          );
-          if (pendingDownloaded <= 0 && pendingTotal <= 0) continue;
-          isDownloading = true;
-          progress = {
-            totalBytes: pendingTotal,
-            downloadedBytes: pendingDownloaded,
-            speed: 0,
-            etaMs: 0,
-            percentage:
-              pendingTotal > 0 ? (pendingDownloaded / pendingTotal) * 100 : 0,
-            completedFiles: 0,
-            totalFiles: 0,
-            files: [],
-          };
-        } else {
-          isDownloading = true;
-          progress = parseDownloadProgress(downloadPayload);
-        }
+        isDownloading = true;
 
+        const progress = parseDownloadProgress(downloadPayload);
         if (progress) {
           // Sum all values across nodes - each node downloads independently
           totalBytes += progress.totalBytes;
@@ -1784,11 +1685,7 @@
           }
         }
 
-        if (
-          downloadKind !== "DownloadOngoing" &&
-          downloadKind !== "DownloadPending"
-        )
-          continue;
+        if (downloadKind !== "DownloadOngoing") continue;
         if (!downloadPayload) continue;
 
         // Check if this download is for this instance's model
@@ -1798,37 +1695,9 @@
           downloadModelId &&
           downloadModelId === instanceModelId
         ) {
-          // For DownloadPending with partial bytes, synthesize progress
-          let progress: DownloadProgress | null;
-          if (downloadKind === "DownloadPending") {
-            const pendingDownloaded = getBytes(
-              downloadPayload.downloaded ??
-                downloadPayload.downloaded_bytes ??
-                downloadPayload.downloadedBytes,
-            );
-            const pendingTotal = getBytes(
-              downloadPayload.total ??
-                downloadPayload.total_bytes ??
-                downloadPayload.totalBytes,
-            );
-            if (pendingDownloaded <= 0 && pendingTotal <= 0) continue;
-            isDownloading = true;
-            progress = {
-              totalBytes: pendingTotal,
-              downloadedBytes: pendingDownloaded,
-              speed: 0,
-              etaMs: 0,
-              percentage:
-                pendingTotal > 0 ? (pendingDownloaded / pendingTotal) * 100 : 0,
-              completedFiles: 0,
-              totalFiles: 0,
-              files: [],
-            };
-          } else {
-            isDownloading = true;
-            progress = parseDownloadProgress(downloadPayload);
-          }
+          isDownloading = true;
 
+          const progress = parseDownloadProgress(downloadPayload);
           if (progress) {
             // Sum all values across nodes - each node downloads independently
             totalBytes += progress.totalBytes;
@@ -2097,7 +1966,6 @@
     let instanceType = "Unknown";
     if (instanceTag === "MlxRingInstance") instanceType = "MLX Ring";
     else if (instanceTag === "MlxJacclInstance") instanceType = "MLX RDMA";
-    else if (instanceTag === "VllmInstance") instanceType = "vLLM (CUDA)";
 
     const inst = instance as {
       shardAssignments?: {
@@ -2416,8 +2284,7 @@
     selectedChatCategory = null;
     pendingAutoMessage = null;
     userForcedIdle = true;
-    // Restore chat model from the sidebar preview selection so both selectors stay in sync
-    setSelectedChatModel(selectedModelId ?? "");
+    setSelectedChatModel("");
     clearChat();
   }
 
@@ -2647,11 +2514,6 @@
         return;
       }
     }
-
-    // Fallthrough: model exists but has no active instance/download/loading state
-    chatLaunchState = "idle";
-    pendingChatModelId = null;
-    selectedChatCategory = null;
   });
 
   // Suggested prompts per category
@@ -2775,11 +2637,7 @@
   }
 
   // Launch a model for seamless chat
-  async function launchModelForChat(
-    modelId: string,
-    category: string,
-    skipCreate = false,
-  ) {
+  async function launchModelForChat(modelId: string, category: string) {
     userForcedIdle = false;
     pendingChatModelId = modelId;
     selectedChatCategory = category;
@@ -2787,7 +2645,7 @@
     // Check if already running — skip straight to chat
     if (hasRunningInstance(modelId)) {
       setSelectedChatModel(modelId);
-      if (!skipCreate) createConversation();
+      createConversation();
       chatLaunchState = "ready";
       return;
     }
@@ -2796,7 +2654,7 @@
     if (hasExistingInstance(modelId)) {
       setSelectedChatModel(modelId);
       pendingChatModelId = modelId;
-      if (!skipCreate) createConversation();
+      createConversation();
       const dlStatus = getModelDownloadStatus(modelId);
       if (dlStatus.isDownloading) {
         chatLaunchState = "downloading";
@@ -2849,7 +2707,7 @@
 
       setSelectedChatModel(modelId);
       recordRecentLaunch(modelId);
-      if (!skipCreate) createConversation();
+      createConversation();
       chatLaunchState = "downloading";
     } catch (error) {
       addToast({ type: "error", message: `Network error: ${error}` });
@@ -2907,7 +2765,7 @@
         // Running model is same or better tier — use it directly
         setSelectedChatModel(bestRunning.id);
         if (!chatStarted) createConversation();
-        routeMessage(content, files);
+        sendMessage(content, files);
         return;
       }
     }
@@ -2924,7 +2782,7 @@
     if (hasRunningInstance(autoModel.id)) {
       setSelectedChatModel(autoModel.id);
       if (!chatStarted) createConversation();
-      routeMessage(content, files);
+      sendMessage(content, files);
       return;
     }
 
@@ -3077,7 +2935,7 @@
       if (pendingAutoMessage) {
         const msg = pendingAutoMessage;
         pendingAutoMessage = null;
-        routeMessage(msg.content, msg.files);
+        sendMessage(msg.content, msg.files);
       }
       return;
     }
@@ -3135,7 +2993,6 @@
   // Handle model selection from the picker when opened from chat context
   function handleChatPickerSelect(modelId: string) {
     setSelectedChatModel(modelId);
-    selectPreviewModel(modelId);
     userForcedIdle = false;
     isModelPickerOpen = false;
   }
@@ -3155,8 +3012,7 @@
 
     // Model is selected and running — send directly
     if (model && hasRunningInstance(model)) {
-      chatLaunchState = "ready";
-      routeMessage(content, files);
+      sendMessage(content, files, null);
       return;
     }
 
@@ -3164,7 +3020,7 @@
     if (model) {
       pendingAutoMessage = { content, files };
       userForcedIdle = false;
-      launchModelForChat(model, "picker", messages().length > 0);
+      launchModelForChat(model, "picker");
       return;
     }
 
@@ -4733,35 +4589,16 @@
       showSidebarToggle={true}
       {sidebarVisible}
       onToggleSidebar={toggleChatSidebarVisible}
-      showMobileMenuToggle={true}
-      mobileMenuOpen={mobileChatOpen}
-      onToggleMobileMenu={toggleMobileChatSidebar}
-      showMobileRightToggle={!chatStarted && !topologyOnlyEnabled}
-      {mobileRightOpen}
-      onToggleMobileRight={toggleMobileRightSidebar}
       downloadProgress={activeDownloadSummary}
     />
   {/if}
 
-  <!-- Mobile Chat Sidebar Drawer -->
-  {#if !topologyOnlyEnabled}
-    <ChatSidebar
-      isMobileDrawer={true}
-      isOpen={mobileChatOpen}
-      onClose={() => setMobileChatSidebarOpen(false)}
-      onNewChat={handleNewChat}
-      onSelectConversation={() => {
-        userForcedIdle = false;
-      }}
-    />
-  {/if}
-
   <!-- Main Content -->
   <main class="flex-1 flex overflow-hidden relative">
-    <!-- Left: Conversation History Sidebar (hidden in topology-only mode, welcome state, or when toggled off) - Desktop only -->
+    <!-- Left: Conversation History Sidebar (hidden in topology-only mode, welcome state, or when toggled off) -->
     {#if !topologyOnlyEnabled && sidebarVisible}
       <div
-        class="hidden md:block w-80 flex-shrink-0 border-r border-exo-yellow/10"
+        class="w-80 flex-shrink-0 border-r border-exo-yellow/10"
         role="complementary"
         aria-label="Conversation history"
       >
@@ -5071,33 +4908,11 @@
           </div>
         </div>
 
-        <!-- Mobile Right Sidebar Drawer (Instances) -->
-        {#if mobileRightOpen}
-          <!-- Overlay backdrop -->
-          <button
-            type="button"
-            class="fixed inset-0 bg-black/60 backdrop-blur-sm z-40 md:hidden"
-            onclick={() => setMobileRightSidebarOpen(false)}
-            aria-label="Close instances panel"
-          ></button>
-          <!-- Drawer panel -->
-          <aside
-            class="fixed right-0 top-0 bottom-0 w-80 bg-exo-dark-gray border-l border-exo-yellow/10 z-50 flex flex-col md:hidden overflow-y-auto"
-            aria-label="Instance controls mobile"
-          >
-            {@render rightSidebarContent()}
-          </aside>
-        {/if}
-
-        <!-- Right Sidebar: Instance Controls (wider on welcome page for better visibility) - Desktop only -->
+        <!-- Right Sidebar: Instance Controls (wider on welcome page for better visibility) -->
         <aside
-          class="hidden md:flex w-80 border-l border-exo-yellow/10 bg-exo-dark-gray flex-col flex-shrink-0"
+          class="w-80 border-l border-exo-yellow/10 bg-exo-dark-gray flex flex-col flex-shrink-0"
           aria-label="Instance controls"
         >
-          {@render rightSidebarContent()}
-        </aside>
-
-        {#snippet rightSidebarContent()}
           <!-- Running Instances Panel (only shown when instances exist) - Scrollable -->
           {#if instanceCount > 0}
             <div class="p-4 flex-shrink-0">
@@ -5847,32 +5662,6 @@
                         </span>
                         RDMA (Fast)
                       </button>
-                      {#if vllmAvailable()}
-                        <button
-                          onclick={() => {
-                            selectedInstanceType = "Vllm";
-                            saveLaunchDefaults();
-                          }}
-                          class="flex items-center gap-2 py-1.5 px-3 text-xs font-mono border rounded transition-all duration-200 cursor-pointer {selectedInstanceType ===
-                          'Vllm'
-                            ? 'bg-transparent text-exo-yellow border-exo-yellow'
-                            : 'bg-transparent text-white/70 border-exo-medium-gray/50 hover:border-exo-yellow/50'}"
-                        >
-                          <span
-                            class="w-3 h-3 rounded-full border-2 flex items-center justify-center {selectedInstanceType ===
-                            'Vllm'
-                              ? 'border-exo-yellow'
-                              : 'border-exo-medium-gray'}"
-                          >
-                            {#if selectedInstanceType === "Vllm"}
-                              <span
-                                class="w-1.5 h-1.5 rounded-full bg-exo-yellow"
-                              ></span>
-                            {/if}
-                          </span>
-                          vLLM (CUDA)
-                        </button>
-                      {/if}
                     </div>
                   </div>
 
@@ -6005,7 +5794,7 @@
               {/if}
             </div>
           </div>
-        {/snippet}
+        </aside>
       </div>
     {:else}
       <!-- CHAT STATE: Chat + Mini-Map -->
@@ -6015,7 +5804,43 @@
           class="flex-1 flex flex-col min-w-0 overflow-hidden"
           in:fade={{ duration: 300, delay: 100 }}
         >
-          {#if chatLaunchState !== "idle" && chatLaunchState !== "ready"}
+          {#if chatLaunchState === "idle"}
+            <!-- No running instance: show model selector -->
+            <div
+              class="flex-1 overflow-y-auto flex items-center justify-center px-8 py-6"
+            >
+              <ChatModelSelector
+                models={models.map((m) => ({
+                  id: m.id,
+                  name: m.name ?? "",
+                  base_model: m.base_model ?? "",
+                  storage_size_megabytes: m.storage_size_megabytes ?? 0,
+                  capabilities: m.capabilities ?? [],
+                  family: m.family ?? "",
+                  quantization: m.quantization ?? "",
+                }))}
+                clusterLabel={chatClusterLabel}
+                totalMemoryGB={availableMemoryGB()}
+                onSelect={handleChatModelSelect}
+                onAddModel={handleChatAddModel}
+              />
+            </div>
+            <div
+              class="flex-shrink-0 px-8 pb-6 pt-4 bg-gradient-to-t from-exo-black via-exo-black to-transparent"
+            >
+              <div class="max-w-7xl mx-auto">
+                <ChatForm
+                  placeholder="Ask anything — we'll pick the best model automatically"
+                  showModelSelector={!!bestRunningModelId}
+                  modelDisplayOverride={bestRunningModelId ?? undefined}
+                  modelTasks={modelTasks()}
+                  modelCapabilities={modelCapabilities()}
+                  onAutoSend={handleAutoSend}
+                  onOpenModelPicker={openChatModelPicker}
+                />
+              </div>
+            </div>
+          {:else if chatLaunchState !== "idle" && chatLaunchState !== "ready"}
             <!-- Model launching/downloading/loading: show progress -->
             <div class="flex-1 flex items-center justify-center px-8 py-6">
               <div class="flex flex-col items-center gap-6 max-w-md w-full">
@@ -6122,8 +5947,8 @@
                 />
               </div>
             </div>
-          {:else if messages().length > 0 || chatLaunchState === "ready"}
-            <!-- Normal chat: show messages -->
+          {:else}
+            <!-- Normal chat: model is running -->
             <div
               class="flex-1 overflow-y-auto px-8 py-6"
               bind:this={chatScrollRef}
@@ -6179,49 +6004,13 @@
                 />
               </div>
             </div>
-          {:else}
-            <!-- No running instance, no messages: show model selector -->
-            <div
-              class="flex-1 overflow-y-auto flex items-center justify-center px-8 py-6"
-            >
-              <ChatModelSelector
-                models={models.map((m) => ({
-                  id: m.id,
-                  name: m.name ?? "",
-                  base_model: m.base_model ?? "",
-                  storage_size_megabytes: m.storage_size_megabytes ?? 0,
-                  capabilities: m.capabilities ?? [],
-                  family: m.family ?? "",
-                  quantization: m.quantization ?? "",
-                }))}
-                clusterLabel={chatClusterLabel}
-                totalMemoryGB={availableMemoryGB()}
-                onSelect={handleChatModelSelect}
-                onAddModel={handleChatAddModel}
-              />
-            </div>
-            <div
-              class="flex-shrink-0 px-8 pb-6 pt-4 bg-gradient-to-t from-exo-black via-exo-black to-transparent"
-            >
-              <div class="max-w-7xl mx-auto">
-                <ChatForm
-                  placeholder="Ask anything — we'll pick the best model automatically"
-                  showModelSelector={!!bestRunningModelId}
-                  modelDisplayOverride={bestRunningModelId ?? undefined}
-                  modelTasks={modelTasks()}
-                  modelCapabilities={modelCapabilities()}
-                  onAutoSend={handleAutoSend}
-                  onOpenModelPicker={openChatModelPicker}
-                />
-              </div>
-            </div>
           {/if}
         </div>
 
-        <!-- Right: Mini-Map Sidebar - Desktop only -->
+        <!-- Right: Mini-Map Sidebar -->
         {#if minimized}
           <aside
-            class="hidden md:flex w-80 border-l border-exo-yellow/20 bg-exo-dark-gray flex-col flex-shrink-0 overflow-y-auto"
+            class="w-80 border-l border-exo-yellow/20 bg-exo-dark-gray flex flex-col flex-shrink-0 overflow-y-auto"
             in:fly={{ x: 100, duration: 400, easing: cubicInOut }}
             aria-label="Cluster topology"
           >
@@ -6246,11 +6035,12 @@
               </div>
 
               <div
-                class="relative aspect-square bg-exo-dark-gray rounded-lg overflow-hidden pointer-events-none"
+                class="relative aspect-square bg-exo-dark-gray rounded-lg overflow-hidden"
               >
                 <TopologyGraph
                   highlightedNodes={highlightedNodes()}
                   filteredNodes={nodeFilter}
+                  onNodeClick={togglePreviewNodeFilter}
                 />
 
                 {@render clusterWarningsCompact()}

dashboard/src/routes/traces/+page.svelte

@@ -3,7 +3,6 @@
   import {
     listTraces,
     getTraceRawUrl,
-    deleteTraces,
     type TraceListItem,
   } from "$lib/stores/app.svelte";
   import HeaderNav from "$lib/components/HeaderNav.svelte";
@@ -11,51 +10,6 @@
   let traces = $state<TraceListItem[]>([]);
   let loading = $state(true);
   let error = $state<string | null>(null);
-  let selectedIds = $state<Set<string>>(new Set());
-  let deleting = $state(false);
-
-  let allSelected = $derived(
-    traces.length > 0 && selectedIds.size === traces.length,
-  );
-
-  function toggleSelect(taskId: string) {
-    const next = new Set(selectedIds);
-    if (next.has(taskId)) {
-      next.delete(taskId);
-    } else {
-      next.add(taskId);
-    }
-    selectedIds = next;
-  }
-
-  function toggleSelectAll() {
-    if (allSelected) {
-      selectedIds = new Set();
-    } else {
-      selectedIds = new Set(traces.map((t) => t.taskId));
-    }
-  }
-
-  async function handleDelete() {
-    if (selectedIds.size === 0) return;
-    const count = selectedIds.size;
-    if (
-      !confirm(
-        `Delete ${count} trace${count === 1 ? "" : "s"}? This cannot be undone.`,
-      )
-    )
-      return;
-    deleting = true;
-    try {
-      await deleteTraces([...selectedIds]);
-      selectedIds = new Set();
-      await refresh();
-    } catch (e) {
-      error = e instanceof Error ? e.message : "Failed to delete traces";
-    } finally {
-      deleting = false;
-    }
-  }
 
   function formatBytes(bytes: number): string {
     if (!bytes || bytes <= 0) return "0B";
@@ -155,16 +109,6 @@
         </h1>
       </div>
       <div class="flex items-center gap-3">
-        {#if selectedIds.size > 0}
-          <button
-            type="button"
-            class="text-xs font-mono text-red-400 hover:text-red-300 transition-colors uppercase border border-red-500/40 px-2 py-1 rounded"
-            onclick={handleDelete}
-            disabled={deleting}
-          >
-            {deleting ? "Deleting..." : `Delete (${selectedIds.size})`}
-          </button>
-        {/if}
         <button
           type="button"
           class="text-xs font-mono text-exo-light-gray hover:text-exo-yellow transition-colors uppercase border border-exo-medium-gray/40 px-2 py-1 rounded"
@@ -199,41 +143,14 @@
       </div>
     {:else}
       <div class="space-y-3">
-        <div class="flex items-center gap-2 px-1">
-          <button
-            type="button"
-            class="text-xs font-mono uppercase transition-colors {allSelected
-              ? 'text-exo-yellow'
-              : 'text-exo-light-gray hover:text-exo-yellow'}"
-            onclick={toggleSelectAll}
-          >
-            {allSelected ? "Deselect all" : "Select all"}
-          </button>
-        </div>
         {#each traces as trace}
-          {@const isSelected = selectedIds.has(trace.taskId)}
-          <!-- svelte-ignore a11y_no_static_element_interactions -->
           <div
-            role="button"
-            tabindex="0"
-            class="w-full text-left rounded border-l-2 border-r border-t border-b transition-all p-4 flex items-center justify-between gap-4 cursor-pointer {isSelected
-              ? 'bg-exo-yellow/10 border-l-exo-yellow border-r-exo-medium-gray/30 border-t-exo-medium-gray/30 border-b-exo-medium-gray/30'
-              : 'bg-exo-black/30 border-l-transparent border-r-exo-medium-gray/30 border-t-exo-medium-gray/30 border-b-exo-medium-gray/30 hover:bg-white/[0.03]'}"
-            onclick={() => toggleSelect(trace.taskId)}
-            onkeydown={(e) => {
-              if (e.key === "Enter" || e.key === " ") {
-                e.preventDefault();
-                toggleSelect(trace.taskId);
-              }
-            }}
+            class="rounded border border-exo-medium-gray/30 bg-exo-black/30 p-4 flex items-center justify-between gap-4"
           >
             <div class="min-w-0 flex-1">
               <a
                 href="#/traces/{trace.taskId}"
-                class="text-sm font-mono transition-colors truncate block {isSelected
-                  ? 'text-exo-yellow'
-                  : 'text-white hover:text-exo-yellow'}"
-                onclick={(e) => e.stopPropagation()}
+                class="text-sm font-mono text-white hover:text-exo-yellow transition-colors truncate block"
               >
                 {trace.taskId}
               </a>
@@ -243,11 +160,7 @@
                 )}
               </div>
             </div>
-            <!-- svelte-ignore a11y_click_events_have_key_events -->
-            <div
-              class="flex items-center gap-2 shrink-0"
-              onclick={(e) => e.stopPropagation()}
-            >
+            <div class="flex items-center gap-2 shrink-0">
               <a
                 href="#/traces/{trace.taskId}"
                 class="text-xs font-mono text-exo-light-gray hover:text-exo-yellow transition-colors uppercase border border-exo-medium-gray/40 px-2 py-1 rounded"

docs/api.md

@@ -4,7 +4,7 @@ This document describes the REST API exposed by the **EXO** service, as implemen
 
 `src/exo/master/api.py`
 
-The API is used to manage model instances in the cluster, inspect cluster state, and perform inference using multiple API-compatible interfaces.
+The API is used to manage model instances in the cluster, inspect cluster state, and perform inference using an OpenAI-compatible interface.
 
 Base URL example:
 
@@ -144,59 +144,8 @@ Placement result.
 
 Returns the list of available models and their metadata.
 
-**Query parameters:**
-
-* `status`: string (optional) - Filter by `downloaded` to show only downloaded models
-
 **Response:**
-Array of model descriptors including `is_custom` field for custom HuggingFace models.
-
-### Add Custom Model
-
-**POST** `/models/add`
-
-Add a custom model from HuggingFace hub.
-
-**Request body (example):**
-
-```json
-{
-  "model_id": "mlx-community/my-custom-model"
-}
-```
-
-**Response:**
-Model descriptor for the added model.
-
-**Security note:**
-Models with `trust_remote_code` enabled in their configuration require explicit opt-in (default is false) for security.
-
-### Delete Custom Model
-
-**DELETE** `/models/custom/{model_id}`
-
-Delete a user-added custom model card.
-
-**Path parameters:**
-
-* `model_id`: string, ID of the custom model to delete
-
-**Response:**
-Confirmation JSON with deleted model ID.
-
-### Search Models
-
-**GET** `/models/search`
-
-Search HuggingFace Hub for mlx-community models.
-
-**Query parameters:**
-
-* `query`: string (optional) - Search query
-* `limit`: integer (default: 20) - Maximum number of results
-
-**Response:**
-Array of HuggingFace model search results.
+Array of model descriptors.
 
 ## 4. Inference / Chat Completions
 
@@ -219,123 +168,9 @@ Executes a chat completion request using an OpenAI-compatible schema. Supports s
 }
 ```
 
-**Request parameters:**
-
-* `model`: string, required - Model ID to use
-* `messages`: array, required - Conversation messages
-* `stream`: boolean (default: false) - Enable streaming responses
-* `max_tokens`: integer (optional) - Maximum tokens to generate
-* `temperature`: float (optional) - Sampling temperature
-* `top_p`: float (optional) - Nucleus sampling parameter
-* `top_k`: integer (optional) - Top-k sampling parameter
-* `stop`: string or array (optional) - Stop sequences
-* `seed`: integer (optional) - Random seed for reproducibility
-* `enable_thinking`: boolean (optional) - Enable thinking mode for capable models (DeepSeek V3.1, Qwen3, GLM-4.7)
-* `tools`: array (optional) - Tool definitions for function calling
-* `logprobs`: boolean (optional) - Return log probabilities
-* `top_logprobs`: integer (optional) - Number of top log probabilities to return
-
 **Response:**
 OpenAI-compatible chat completion response.
 
-**Streaming response format:**
-When `stream=true`, returns Server-Sent Events (SSE) with format:
-
-```
-data: {"id":"...","object":"chat.completion","created":...,"model":"...","choices":[...]}
-
-data: [DONE]
-```
-
-**Non-streaming response includes usage statistics:**
-
-```json
-{
-  "id": "...",
-  "object": "chat.completion",
-  "created": 1234567890,
-  "model": "llama-3.2-1b",
-  "choices": [{
-    "index": 0,
-    "message": {
-      "role": "assistant",
-      "content": "Hello! How can I help you?"
-    },
-    "finish_reason": "stop"
-  }],
-  "usage": {
-    "prompt_tokens": 15,
-    "completion_tokens": 8,
-    "total_tokens": 23
-  }
-}
-```
-
-**Cancellation:**
-You can cancel an active generation by closing the HTTP connection. The server detects the disconnection and stops processing.
-
-### Claude Messages API
-
-**POST** `/v1/messages`
-
-Executes a chat completion request using the Claude Messages API format. Supports streaming and non-streaming modes.
-
-**Request body (example):**
-
-```json
-{
-  "model": "llama-3.2-1b",
-  "messages": [
-    { "role": "user", "content": "Hello" }
-  ],
-  "max_tokens": 1024,
-  "stream": false
-}
-```
-
-**Streaming response format:**
-When `stream=true`, returns Server-Sent Events with Claude-specific event types:
-
-* `message_start` - Message generation started
-* `content_block_start` - Content block started
-* `content_block_delta` - Incremental content chunk
-* `content_block_stop` - Content block completed
-* `message_delta` - Message metadata updates
-* `message_stop` - Message generation completed
-
-**Response:**
-Claude-compatible messages response.
-
-### OpenAI Responses API
-
-**POST** `/v1/responses`
-
-Executes a chat completion request using the OpenAI Responses API format. Supports streaming and non-streaming modes.
-
-**Request body (example):**
-
-```json
-{
-  "model": "llama-3.2-1b",
-  "messages": [
-    { "role": "user", "content": "Hello" }
-  ],
-  "stream": false
-}
-```
-
-**Streaming response format:**
-When `stream=true`, returns Server-Sent Events with response-specific event types:
-
-* `response.created` - Response generation started
-* `response.in_progress` - Response is being generated
-* `response.output_item.added` - New output item added
-* `response.output_item.done` - Output item completed
-* `response.done` - Response generation completed
-
-**Response:**
-OpenAI Responses API-compatible response.
-
 ### Benchmarked Chat Completions
 
 **POST** `/bench/chat/completions`
@@ -346,177 +181,26 @@ Same as `/v1/chat/completions`, but also returns performance and generation stat
 Same schema as `/v1/chat/completions`.
 
 **Response:**
-Chat completion plus benchmarking metrics including:
+Chat completion plus benchmarking metrics.
 
-* `prompt_tps` - Tokens per second during prompt processing
-* `generation_tps` - Tokens per second during generation
-* `prompt_tokens` - Number of prompt tokens
-* `generation_tokens` - Number of generated tokens
-* `peak_memory_usage` - Peak memory used during generation
-
-### Cancel Command
-
-**POST** `/v1/cancel/{command_id}`
-
-Cancels an active generation command (text or image). Notifies workers and closes the stream.
-
-**Path parameters:**
-
-* `command_id`: string, ID of the command to cancel
-
-**Response (example):**
-
-```json
-{
-  "message": "Command cancelled.",
-  "command_id": "cmd-abc-123"
-}
-```
-
-Returns 404 if the command is not found or already completed.
-
-## 5. Ollama API Compatibility
-
-EXO provides Ollama API compatibility for tools like OpenWebUI.
-
-### Ollama Chat
-
-**POST** `/ollama/api/chat`
-**POST** `/ollama/api/api/chat` (alias)
-**POST** `/ollama/api/v1/chat` (alias)
-
-Execute a chat request using Ollama API format.
-
-**Request body (example):**
-
-```json
-{
-  "model": "llama-3.2-1b",
-  "messages": [
-    { "role": "user", "content": "Hello" }
-  ],
-  "stream": false
-}
-```
-
-**Response:**
-Ollama-compatible chat response.
-
-### Ollama Generate
-
-**POST** `/ollama/api/generate`
-
-Execute a text generation request using Ollama API format.
-
-**Request body (example):**
-
-```json
-{
-  "model": "llama-3.2-1b",
-  "prompt": "Hello",
-  "stream": false
-}
-```
-
-**Response:**
-Ollama-compatible generation response.
-
-### Ollama Tags
-
-**GET** `/ollama/api/tags`
-**GET** `/ollama/api/api/tags` (alias)
-**GET** `/ollama/api/v1/tags` (alias)
-
-Returns list of downloaded models in Ollama tags format.
-
-**Response:**
-Array of model tags with metadata.
-
-### Ollama Show
-
-**POST** `/ollama/api/show`
-
-Returns model information in Ollama show format.
-
-**Request body:**
-
-```json
-{
-  "name": "llama-3.2-1b"
-}
-```
-
-**Response:**
-Model details including modelfile and family.
-
-### Ollama PS
-
-**GET** `/ollama/api/ps`
-
-Returns list of running models (active instances).
-
-**Response:**
-Array of active model instances.
-
-### Ollama Version
-
-**GET** `/ollama/api/version`
-**HEAD** `/ollama/` (alias)
-**HEAD** `/ollama/api/version` (alias)
-
-Returns version information for Ollama API compatibility.
-
-**Response:**
-
-```json
-{
-  "version": "exo v1.0"
-}
-```
-
-## 6. Image Generation & Editing
+## 5. Image Generation & Editing
 
 ### Image Generation
 
 **POST** `/v1/images/generations`
 
-Executes an image generation request using an OpenAI-compatible schema with additional advanced_params. Supports both streaming and non-streaming modes.
+Executes an image generation request using an OpenAI-compatible schema with additional advanced_params.
 
 **Request body (example):**
 
 ```json
 {
   "prompt": "a robot playing chess",
-  "model": "exolabs/FLUX.1-dev",
-  "n": 1,
-  "size": "1024x1024",
+  "model": "flux-dev",
   "stream": false,
-  "response_format": "b64_json"
 }
 ```
 
-**Request parameters:**
-
-* `prompt`: string, required - Text description of the image
-* `model`: string, required - Image model ID
-* `n`: integer (default: 1) - Number of images to generate
-* `size`: string (default: "auto") - Image dimensions. Supported sizes:
-  - `512x512`
-  - `768x768`
-  - `1024x768`
-  - `768x1024`
-  - `1024x1024`
-  - `1024x1536`
-  - `1536x1024`
-  - `1024x1365`
-  - `1365x1024`
-* `stream`: boolean (default: false) - Enable streaming for partial images
-* `partial_images`: integer (default: 0) - Number of partial images to stream during generation
-* `response_format`: string (default: "b64_json") - Either `url` or `b64_json`
-* `quality`: string (default: "medium") - Either `high`, `medium`, or `low`
-* `output_format`: string (default: "png") - Either `png`, `jpeg`, or `webp`
-* `advanced_params`: object (optional) - Advanced generation parameters
-
 **Advanced Parameters (`advanced_params`):**
 
 | Parameter | Type | Constraints | Description |
@@ -526,54 +210,8 @@ Executes an image generation request using an OpenAI-compatible schema with addi
 | `guidance` | float | 1.0-20.0 | Classifier-free guidance scale |
 | `negative_prompt` | string | - | Text describing what to avoid in the image |
 
-**Non-streaming response:**
-
-```json
-{
-  "created": 1234567890,
-  "data": [
-    {
-      "b64_json": "iVBORw0KGgoAAAANSUhEUgAA...",
-      "url": null
-    }
-  ]
-}
-```
-
-**Streaming response format:**
-When `stream=true` and `partial_images > 0`, returns Server-Sent Events:
-
-```
-data: {"type":"partial","image_index":0,"partial_index":1,"total_partials":5,"format":"png","data":{"b64_json":"..."}}
-
-data: {"type":"final","image_index":0,"format":"png","data":{"b64_json":"..."}}
-
-data: [DONE]
-```
-
-### Image Editing
-
-**POST** `/v1/images/edits`
-
-Executes an image editing request (img2img) using FLUX.1-Kontext-dev or similar models.
-
-**Request (multipart/form-data):**
-
-* `image`: file, required - Input image to edit
-* `prompt`: string, required - Text description of desired changes
-* `model`: string, required - Image editing model ID (e.g., `exolabs/FLUX.1-Kontext-dev`)
-* `n`: integer (default: 1) - Number of edited images to generate
-* `size`: string (optional) - Output image dimensions
-* `response_format`: string (default: "b64_json") - Either `url` or `b64_json`
-* `input_fidelity`: string (default: "low") - Either `low` or `high` - Controls how closely the output follows the input image
-* `stream`: string (default: "false") - Enable streaming
-* `partial_images`: string (default: "0") - Number of partial images to stream
-* `quality`: string (default: "medium") - Either `high`, `medium`, or `low`
-* `output_format`: string (default: "png") - Either `png`, `jpeg`, or `webp`
-* `advanced_params`: string (optional) - JSON-encoded advanced parameters
-
 **Response:**
-Same format as `/v1/images/generations`.
+OpenAI-compatible image generation response.
 
 ### Benchmarked Image Generation
 
@@ -585,15 +223,16 @@ Same as `/v1/images/generations`, but also returns generation statistics.
 Same schema as `/v1/images/generations`.
 
 **Response:**
-Image generation plus benchmarking metrics including:
+Image generation plus benchmarking metrics.
 
-* `seconds_per_step` - Average time per denoising step
-* `total_generation_time` - Total generation time
-* `num_inference_steps` - Number of inference steps used
-* `num_images` - Number of images generated
-* `image_width` - Output image width
-* `image_height` - Output image height
-* `peak_memory_usage` - Peak memory used during generation
+### Image Editing
+
+**POST** `/v1/images/edits`
+
+Executes an image editing request using an OpenAI-compatible schema with additional advanced_params (same as `/v1/images/generations`).
+
+**Response:**
+Same format as `/v1/images/generations`.
 
 ### Benchmarked Image Editing
 
@@ -607,127 +246,36 @@ Same schema as `/v1/images/edits`.
 **Response:**
 Same format as `/bench/images/generations`, including `generation_stats`.
 
-### List Images
-
-**GET** `/images`
-
-List all stored images.
-
-**Response:**
-Array of image metadata including URLs and expiration times.
-
-### Get Image
-
-**GET** `/images/{image_id}`
-
-Retrieve a stored image by ID.
-
-**Path parameters:**
-
-* `image_id`: string, ID of the image
-
-**Response:**
-Image file with appropriate content type.
-
-## 7. Complete Endpoint Summary
+## 6. Complete Endpoint Summary
 
 ```
-# General
 GET     /node_id
 GET     /state
 GET     /events
 
-# Instance Management
 POST    /instance
 GET     /instance/{instance_id}
 DELETE  /instance/{instance_id}
+
 GET     /instance/previews
 GET     /instance/placement
 POST    /place_instance
 
-# Models
 GET     /models
 GET     /v1/models
-POST    /models/add
-DELETE  /models/custom/{model_id}
-GET     /models/search
 
-# Text Generation (OpenAI Chat Completions)
 POST    /v1/chat/completions
 POST    /bench/chat/completions
 
-# Text Generation (Claude Messages API)
-POST    /v1/messages
-
-# Text Generation (OpenAI Responses API)
-POST    /v1/responses
-
-# Text Generation (Ollama API)
-POST    /ollama/api/chat
-POST    /ollama/api/api/chat
-POST    /ollama/api/v1/chat
-POST    /ollama/api/generate
-GET     /ollama/api/tags
-GET     /ollama/api/api/tags
-GET     /ollama/api/v1/tags
-POST    /ollama/api/show
-GET     /ollama/api/ps
-GET     /ollama/api/version
-HEAD    /ollama/
-HEAD    /ollama/api/version
-
-# Command Control
-POST    /v1/cancel/{command_id}
-
-# Image Generation
 POST    /v1/images/generations
 POST    /bench/images/generations
 POST    /v1/images/edits
 POST    /bench/images/edits
-GET     /images
-GET     /images/{image_id}
 ```
 
-## 8. Notes
+## 7. Notes
 
-### API Compatibility
-
-EXO provides multiple API-compatible interfaces:
-
-* **OpenAI Chat Completions API** - Compatible with OpenAI clients and tools
-* **Claude Messages API** - Compatible with Anthropic's Claude API format
-* **OpenAI Responses API** - Compatible with OpenAI's Responses API format
-* **Ollama API** - Compatible with Ollama and tools like OpenWebUI
-
-Existing OpenAI, Claude, or Ollama clients can be pointed to EXO by changing the base URL.
-
-### Custom Models
-
-You can add custom models from HuggingFace using the `/models/add` endpoint. Custom models are identified by the `is_custom` field in model list responses.
-
-**Security:** Models requiring `trust_remote_code` must be explicitly enabled (default is false) for security. Only enable this if you trust the model's remote code.
-
-### Usage Statistics
-
-Chat completion responses include usage statistics with:
-
-* `prompt_tokens` - Number of tokens in the prompt
-* `completion_tokens` - Number of tokens generated
-* `total_tokens` - Sum of prompt and completion tokens
-
-### Request Cancellation
-
-You can cancel active requests by:
-
-1. Closing the HTTP connection (for streaming requests)
-2. Calling `/v1/cancel/{command_id}` (for any request)
-
-The server detects cancellation and stops processing immediately.
-
-### Instance Placement
-
-The instance placement endpoints allow you to plan and preview cluster allocations before creating instances. This helps optimize resource usage across nodes.
-
-### Observability
-
-The `/events` and `/state` endpoints are primarily intended for operational visibility and debugging.
+* The `/v1/chat/completions` endpoint is compatible with the OpenAI Chat API format, so existing OpenAI clients can be pointed to EXO by changing the base URL.
+* The `/v1/images/generations` and `/v1/images/edits` endpoints are compatible with the OpenAI Images API format.
+* The instance placement endpoints allow you to plan and preview cluster allocations before actually creating instances.
+* The `/events` and `/state` endpoints are primarily intended for operational visibility and debugging.

docs/architecture.md

@@ -28,26 +28,6 @@ There are currently 5 major systems:
     
     Implements a distributed algorithm for master election in unstable networking conditions
 
-## API Layer
-
-The API system uses multiple adapters to support multiple API formats, converting them to a single request / response type.
-
-### Adapter Pattern
-
-Adapters convert between external API formats and EXO's internal types:
-
-```
-Chat Completions → [adapter] → TextGenerationTaskParams → Application
-Claude Messages  → [adapter] → TextGenerationTaskParams → Application
-Responses API    → [adapter] → TextGenerationTaskParams → Application
-Ollama API       → [adapter] → TextGenerationTaskParams → Application
-```
-
-Each adapter implements two key functions:
-1. **Request conversion**: Converts API-specific requests to `TextGenerationTaskParams`
-2. **Response generation**: Converts internal `TokenChunk` streams back to API-specific formats (streaming and non-streaming)
-
-
 ## Topics
 
 There are currently 5 topics:

flake.nix

@@ -51,8 +51,8 @@
   };
 
   nixConfig = {
-    extra-trusted-public-keys = "exo.cachix.org-1:okq7hl624TBeAR3kV+g39dUFSiaZgLRkLsFBCuJ2NZI= cache.nixos-cuda.org:74DUi4Ye579gUqzH4ziL9IyiJBlDpMRn9MBN8oNan9M=";
-    extra-substituters = "https://exo.cachix.org https://cache.nixos-cuda.org";
+    extra-trusted-public-keys = "exo.cachix.org-1:okq7hl624TBeAR3kV+g39dUFSiaZgLRkLsFBCuJ2NZI=";
+    extra-substituters = "https://exo.cachix.org";
   };
 
   outputs =
@@ -76,8 +76,6 @@
         let
           # Use pinned nixpkgs for swift-format (swift is broken on x86_64-linux in newer nixpkgs)
           pkgsSwift = import inputs.nixpkgs-swift { inherit system; };
-
-          pkgsCuda = import ./nix/cuda-pkgs.nix { nixpkgs = inputs.nixpkgs; inherit system; };
         in
         {
           # Allow unfree for metal-toolchain (needed for Darwin Metal packages)
@@ -110,141 +108,68 @@
                 package = pkgsSwift.swiftPackages.swift-format;
               };
               shfmt.enable = true;
-              taplo.enable = true;
             };
           };
 
-          packages = lib.optionalAttrs pkgs.stdenv.hostPlatform.isDarwin
-            (
-              let
-                uvLock = builtins.fromTOML (builtins.readFile ./uv.lock);
-                mlxPackage = builtins.head (builtins.filter (p: p.name == "mlx" && p.source ? git) uvLock.package);
-                uvLockMlxVersion = mlxPackage.version;
-                uvLockMlxRev = builtins.elemAt (builtins.split "#" mlxPackage.source.git) 2;
-              in
-              {
-                metal-toolchain = pkgs.callPackage ./nix/metal-toolchain.nix { };
-                mlx = pkgs.callPackage ./nix/mlx.nix {
-                  inherit (self'.packages) metal-toolchain;
-                  inherit uvLockMlxVersion uvLockMlxRev;
-                };
-                default = self'.packages.exo;
-              }
-            ) // lib.optionalAttrs (pkgsCuda != null) {
-            torch-cuda = pkgsCuda.python313Packages.torch;
-            vllm-cuda = pkgsCuda.python313Packages.vllm;
-
-            # Smoke test script for verifying vLLM + CUDA GPU setup
-            vllm-check = pkgs.writeShellApplication {
-              name = "vllm-check";
-              runtimeInputs = [
-                (pkgsCuda.python313.withPackages (ps: [ ps.torch ps.vllm ]))
-              ];
-              # On non-NixOS hosts, NVIDIA driver libraries live in /usr/lib and must be
-              # LD_PRELOAD'd individually (adding the whole dir causes SIGILL from conflicts).
-              # These are: CUDA driver, NVML, and the PTX JIT compiler (for flash attention).
-              # libnvJitLink comes from the nix CUDA toolkit via LD_LIBRARY_PATH.
-              text = ''
-                for dir in /usr/lib/aarch64-linux-gnu /usr/lib/x86_64-linux-gnu /usr/lib; do
-                  if [ -e "$dir/libcuda.so.1" ]; then
-                    NVIDIA_LIBS="$dir/libcuda.so.1"
-                    for lib in libnvidia-ml.so.1 libnvidia-ptxjitcompiler.so.1; do
-                      [ -e "$dir/$lib" ] && NVIDIA_LIBS="$NVIDIA_LIBS:$dir/$lib"
-                    done
-                    export LD_PRELOAD="$NVIDIA_LIBS''${LD_PRELOAD:+:$LD_PRELOAD}"
-                    break
-                  fi
-                done
-                export LD_LIBRARY_PATH="${pkgsCuda.cudaPackages.libnvjitlink}/lib''${LD_LIBRARY_PATH:+:$LD_LIBRARY_PATH}"
-                exec python ${inputs.self + /tests/test_vllm_smoke.py}
-              '';
-            };
-
-            # exo with CUDA torch + vLLM — wraps the uv2nix-built package with host driver libs
-            exo-cuda = pkgs.writeShellApplication {
-              name = "exo-cuda";
-              runtimeInputs = [ self'.packages.exo-cuda-unwrapped ];
-              text = ''
-                for dir in /usr/lib/aarch64-linux-gnu /usr/lib/x86_64-linux-gnu /usr/lib; do
-                  if [ -e "$dir/libcuda.so.1" ]; then
-                    NVIDIA_LIBS="$dir/libcuda.so.1"
-                    for lib in libnvidia-ml.so.1 libnvidia-ptxjitcompiler.so.1; do
-                      [ -e "$dir/$lib" ] && NVIDIA_LIBS="$NVIDIA_LIBS:$dir/$lib"
-                    done
-                    export LD_PRELOAD="$NVIDIA_LIBS''${LD_PRELOAD:+:$LD_PRELOAD}"
-                    break
-                  fi
-                done
-                export LD_LIBRARY_PATH="${pkgsCuda.stdenv.cc.cc.lib}/lib:${pkgsCuda.cudaPackages.libnvjitlink}/lib''${LD_LIBRARY_PATH:+:$LD_LIBRARY_PATH}"
-                exec exo-cuda "$@"
-              '';
-            };
-          };
-
-          # CUDA development shell with torch + vLLM (aarch64-linux only)
-          devShells = lib.optionalAttrs (pkgsCuda != null)
+          packages = lib.optionalAttrs pkgs.stdenv.hostPlatform.isDarwin (
+            let
+              uvLock = builtins.fromTOML (builtins.readFile ./uv.lock);
+              mlxPackage = builtins.head (builtins.filter (p: p.name == "mlx" && p.source ? git) uvLock.package);
+              uvLockMlxVersion = mlxPackage.version;
+            in
             {
-              cuda = pkgs.mkShell {
-                packages = [
-                  (pkgsCuda.python313.withPackages (ps: [
-                    ps.torch
-                    ps.vllm
-                  ]))
-                  pkgs.uv
-                  pkgs.just
-                ];
-
-                shellHook = ''
-                  echo "CUDA dev shell with torch + vLLM"
-                  python -c "import torch; print(f'PyTorch {torch.__version__}, CUDA: {torch.cuda.is_available()}')" 2>/dev/null || true
-                '';
+              metal-toolchain = pkgs.callPackage ./nix/metal-toolchain.nix { };
+              mlx = pkgs.callPackage ./nix/mlx.nix {
+                inherit (self'.packages) metal-toolchain;
+                inherit uvLockMlxVersion;
               };
-            } // {
+              default = self'.packages.exo;
+            }
+          );
 
-            default = with pkgs; pkgs.mkShell {
-              inputsFrom = [ self'.checks.cargo-build ];
+          devShells.default = with pkgs; pkgs.mkShell {
+            inputsFrom = [ self'.checks.cargo-build ];
 
-              packages =
-                [
-                  # FORMATTING
-                  config.treefmt.build.wrapper
+            packages =
+              [
+                # FORMATTING
+                config.treefmt.build.wrapper
 
-                  # PYTHON
-                  python313
-                  uv
-                  ruff
-                  basedpyright
+                # PYTHON
+                python313
+                uv
+                ruff
+                basedpyright
 
-                  # RUST
-                  config.rust.toolchain
-                  maturin
+                # RUST
+                config.rust.toolchain
+                maturin
 
-                  # NIX
-                  nixpkgs-fmt
+                # NIX
+                nixpkgs-fmt
 
-                  # SVELTE
-                  nodejs
+                # SVELTE
+                nodejs
 
-                  # MISC
-                  just
-                  jq
-                ]
-                ++ lib.optionals stdenv.isLinux [
-                  unixtools.ifconfig
-                ]
-                ++ lib.optionals stdenv.isDarwin [
-                  macmon
-                ];
+                # MISC
+                just
+                jq
+              ]
+              ++ lib.optionals stdenv.isLinux [
+                unixtools.ifconfig
+              ]
+              ++ lib.optionals stdenv.isDarwin [
+                macmon
+              ];
 
-              OPENSSL_NO_VENDOR = "1";
+            OPENSSL_NO_VENDOR = "1";
 
-              shellHook = ''
-                export LD_LIBRARY_PATH="$LD_LIBRARY_PATH:${python313}/lib"
-                ${lib.optionalString stdenv.isLinux ''
-                  export LD_LIBRARY_PATH="${openssl.out}/lib:$LD_LIBRARY_PATH"
-                ''}
-              '';
-            };
+            shellHook = ''
+              export LD_LIBRARY_PATH="$LD_LIBRARY_PATH:${python313}/lib"
+              ${lib.optionalString stdenv.isLinux ''
+                export LD_LIBRARY_PATH="${openssl.out}/lib:$LD_LIBRARY_PATH"
+              ''}
+            '';
           };
         };
     };

justfile

@@ -15,39 +15,6 @@ check:
 sync:
     uv sync --all-packages
 
-sync-cuda:
-    #!/usr/bin/env bash
-    set -euo pipefail
-    if command -v nvidia-smi &>/dev/null; then
-        arch=$(nvidia-smi --query-gpu=compute_cap --format=csv,noheader | head -1 | tr -d ' ')
-        export TORCH_CUDA_ARCH_LIST="$arch"
-        export FLASHINFER_CUDA_ARCH_LIST="${arch}a"
-        export VLLM_TARGET_DEVICE=cuda
-    fi
-    uv pip install "cmake>=3.26.1" ninja "packaging>=24.2" "setuptools>=77.0.3,<81.0.0" "setuptools-scm>=8.0" wheel jinja2
-    find ~/.cache/uv/git-v0 -name CMakeCache.txt -delete 2>/dev/null || true
-    uv sync --extra cuda
-
-build-flashinfer-sm121:
-    #!/usr/bin/env bash
-    set -euo pipefail
-    FLASHINFER_VERSION="v0.6.6"
-    BUILD_DIR="/tmp/flashinfer-build"
-    EXO_VENV="$(pwd)/.venv"
-    rm -rf "$BUILD_DIR"
-    git clone --depth 1 --branch "$FLASHINFER_VERSION" --recurse-submodules --shallow-submodules https://github.com/flashinfer-ai/flashinfer.git "$BUILD_DIR"
-    cd "$BUILD_DIR"
-    export FLASHINFER_CUDA_ARCH_LIST="12.1a"
-    export TORCH_CUDA_ARCH_LIST="12.1"
-    export FLASHINFER_ENABLE_AOT=1
-    UV="$(command -v uv || echo "$HOME/.local/bin/uv")"
-    VIRTUAL_ENV="$EXO_VENV" "$UV" pip install . --no-build-isolation
-    echo "Building AOT cubin wheel..."
-    cd flashinfer-cubin && VIRTUAL_ENV="$EXO_VENV" "$UV" pip install . --no-build-isolation && cd ..
-    echo "Building JIT cache wheel..."
-    cd flashinfer-jit-cache && VIRTUAL_ENV="$EXO_VENV" "$UV" pip install . --no-build-isolation && cd ..
-    echo "FlashInfer built from source with SM121 AOT kernels"
-
 sync-clean:
     uv sync --all-packages --force-reinstall --no-cache
 

nix/cuda-pkgs.nix

@@ -1,102 +0,0 @@
-{ nixpkgs, system }:
-let
-  pkgs = import nixpkgs { inherit system; };
-in
-if system == "aarch64-linux" then
-  import nixpkgs
-  {
-    inherit system;
-    config = {
-      allowUnfree = true;
-      allowBroken = true;
-      allowUnsupportedSystem = true;
-      cudaSupport = true;
-      cudaCapabilities = [ "12.1" ];
-    };
-    overlays = [
-      (final: prev:
-        let
-          cudaCompatStub = cfinal: cprev: {
-            cuda_compat = prev.runCommand "cuda13.0-cuda_compat-stub" { } "mkdir -p $out";
-          };
-        in
-        {
-          cudaPackages = prev.cudaPackages_13.overrideScope cudaCompatStub // {
-            override = args:
-              (prev.cudaPackages_13.override args).overrideScope cudaCompatStub;
-          };
-
-          pythonPackagesExtensions = prev.pythonPackagesExtensions ++ [
-            (pyFinal: pyPrev: {
-              fastsafetensors = pyFinal.buildPythonPackage {
-                pname = "fastsafetensors";
-                version = "0.2.2";
-                src = prev.fetchFromGitHub {
-                  owner = "foundation-model-stack";
-                  repo = "fastsafetensors";
-                  rev = "v0.2.2";
-                  hash = "";
-                };
-                pyproject = true;
-                build-system = [
-                  pyFinal.setuptools
-                  pyFinal.pybind11
-                ];
-                buildInputs = [
-                  final.cudaPackages.cuda_cudart
-                  final.cudaPackages.cuda_nvml_dev
-                ];
-                nativeBuildInputs = [
-                  final.cudaPackages.cuda_nvcc
-                ];
-                dependencies = [
-                  pyFinal.typer
-                ];
-                env.CUDA_HOME = "${final.cudaPackages.cuda_nvcc}";
-                pythonImportsCheck = [ "fastsafetensors" ];
-              };
-              cupy = pyPrev.cupy.override {
-                cudaPackages = final.cudaPackages;
-              };
-
-              bitsandbytes = pyPrev.bitsandbytes.overrideAttrs (old: {
-                preConfigure = (old.preConfigure or "") + ''
-                  export CXXFLAGS="''${CXXFLAGS:-} -I${final.cudaPackages.cuda_crt}/include"
-                  export CUDAFLAGS="''${CUDAFLAGS:-} -I${final.cudaPackages.cuda_crt}/include"
-                  export CMAKE_CUDA_FLAGS="''${CMAKE_CUDA_FLAGS:-} -I${final.cudaPackages.cuda_crt}/include"
-                '';
-                buildInputs = (old.buildInputs or [ ]) ++ [ final.cudaPackages.cuda_crt ];
-              });
-
-              vllm = pyPrev.vllm.overrideAttrs (old: {
-                buildInputs = (old.buildInputs or [ ]) ++ [
-                  final.cuda_cccl_with_prefix
-                  final.cudaPackages.cuda_crt
-                ];
-                preConfigure = (old.preConfigure or "") + ''
-                  export CXXFLAGS="''${CXXFLAGS:-} -I${final.cuda_cccl_with_prefix}/include -I${final.cudaPackages.cuda_crt}/include"
-                  export CUDAFLAGS="''${CUDAFLAGS:-} -I${final.cuda_cccl_with_prefix}/include -I${final.cudaPackages.cuda_crt}/include"
-                '';
-              });
-            })
-          ];
-
-          magma-cuda-static = prev.magma-cuda-static.overrideAttrs (old: {
-            postPatch = (old.postPatch or "") + ''
-              sed -i '/err = cudaGetDeviceProperties( &prop, dev );/a\        int clock_khz = 0; cudaDeviceGetAttribute(\&clock_khz, cudaDevAttrClockRate, dev);' interface_cuda/interface.cpp
-              sed -i 's/prop\.clockRate/clock_khz/g' interface_cuda/interface.cpp
-            '';
-          });
-
-          cuda_cccl_with_prefix = prev.runCommand "cuda13.0-cuda_cccl-with-cccl-prefix" { } ''
-            mkdir -p $out/include
-            ln -s ${final.cudaPackages.cuda_cccl}/include $out/include/cccl
-          '';
-
-          opencv = prev.opencv.override { enableCuda = false; };
-          opencv4 = prev.opencv4.override { enableCuda = false; };
-        })
-    ];
-  }
-else
-  null

nix/mlx.nix

@@ -11,7 +11,6 @@
 , fmt
 , python313Packages
 , uvLockMlxVersion
-, uvLockMlxRev
 }:
 
 assert stdenv.isDarwin;
@@ -42,14 +41,16 @@ let
 
   mlx = stdenv.mkDerivation rec {
     pname = "mlx";
-    version = uvLockMlxVersion;
+    version = let v = "0.30.7.dev20260220+13998a05"; in
+      assert v == uvLockMlxVersion || throw "MLX version mismatch: nix/mlx.nix has ${v} but uv.lock has ${uvLockMlxVersion}. Update both the version and hash in nix/mlx.nix.";
+      v;
     pyproject = true;
 
     src = fetchFromGitHub {
       owner = "rltakashige";
       repo = "mlx-jaccl-fix-small-recv";
-      rev = uvLockMlxRev;
-      hash = "sha256-GosFIWxIB48Egb1MqJrR3xhsUsQeWdRk5rV93USY6wQ=";
+      rev = "13998a054715edcdc93618fb1496c79c7c25ff7c";
+      hash = "sha256-fAqA3hFwNBx7FcoGnhQsIFpAIRbC2EerACm4Fvne0Cc=";
     };
 
     patches = [

pyproject.toml

@@ -1,34 +1,35 @@
 [project]
 name = "exo"
-version = "0.3.68"
+version = "0.3.0"
 description = "Exo"
 readme = "README.md"
 requires-python = ">=3.13"
 dependencies = [
-  "aiofiles>=24.1.0",
-  "aiohttp>=3.12.14",
-  "types-aiofiles>=24.1.0.20250708",
-  "pydantic>=2.11.7",
-  "fastapi>=0.116.1",
-  "filelock>=3.18.0",
-  "rustworkx>=0.17.1",
-  "huggingface-hub>=0.33.4",
-  "psutil>=7.0.0",
-  "loguru>=0.7.3",
-  "exo_pyo3_bindings",                       # rust bindings
-  "anyio==4.11.0",
-  "mlx; sys_platform == 'darwin'",
-  "mlx==0.30.6; sys_platform == 'linux'",
-  "mlx-lm",
-  "tiktoken>=0.12.0",                        # required for kimi k2 tokenizer
-  "hypercorn>=0.18.0",
-  "openai-harmony>=0.0.8",
-  "httpx>=0.28.1",
-  "tomlkit>=0.14.0",
-  "mflux==0.16.9; sys_platform == 'darwin'",
-  "python-multipart>=0.0.21",
-  "msgspec>=0.19.0",
-  "zstandard>=0.23.0",
+    "aiofiles>=24.1.0",
+    "aiohttp>=3.12.14",
+    "types-aiofiles>=24.1.0.20250708",
+    "pydantic>=2.11.7",
+    "fastapi>=0.116.1",
+    "filelock>=3.18.0",
+    "rustworkx>=0.17.1",
+    "huggingface-hub>=0.33.4",
+    "psutil>=7.0.0",
+    "loguru>=0.7.3",
+    "exo_pyo3_bindings", # rust bindings
+    "anyio==4.11.0",
+    "mlx; sys_platform == 'darwin'",
+    "mlx[cpu]==0.30.6; sys_platform == 'linux'",
+    "mlx-lm==0.30.7",
+    "tiktoken>=0.12.0", # required for kimi k2 tokenizer
+    "hypercorn>=0.18.0",
+    "openai-harmony>=0.0.8",
+    "httpx>=0.28.1",
+    "tomlkit>=0.14.0",
+    "pillow>=11.0,<12.0", # compatibility with mflux
+    "mflux==0.15.5",
+    "python-multipart>=0.0.21",
+    "msgspec>=0.19.0",
+    "zstandard>=0.23.0",
 ]
 
 [project.scripts]
@@ -37,44 +38,38 @@ exo = "exo.main:main"
 # dependencies only required for development
 [dependency-groups]
 dev = [
-  "basedpyright>=1.29.0",
-  "pyinstaller>=6.17.0",
-  "pytest>=8.4.0",
-  "pytest-asyncio>=1.0.0",
-  "pytest-env",
-  "ruff>=0.11.13",
+    "basedpyright>=1.29.0",
+    "pyinstaller>=6.17.0",
+    "pytest>=8.4.0",
+    "pytest-asyncio>=1.0.0",
+    "pytest-env",
+    "ruff>=0.11.13",
 ]
 
+# mlx[cuda] requires a newer version of mlx. the ideal on linux is: default to mlx[cpu] unless[cuda] specified.
 [project.optional-dependencies]
-cuda = [
-  "torch>=2.10.0; sys_platform == 'linux'",
-  "vllm>=0.13.0; sys_platform == 'linux'",
-  "mlx-cuda-13==0.30.6; sys_platform == 'linux'",
-  "fastsafetensors>=0.1.10; sys_platform == 'linux'",
-]
+# cuda = [
+#     "mlx[cuda]==0.26.3",
+# ]
 
 ###
 # workspace configuration
 ###
 
 [tool.uv.workspace]
-members = ["rust/exo_pyo3_bindings", "bench"]
+members = [
+    "rust/exo_pyo3_bindings",
+    "bench",
+]
 
 [tool.uv.sources]
 exo_pyo3_bindings = { workspace = true }
 mlx = { git = "https://github.com/rltakashige/mlx-jaccl-fix-small-recv.git", branch = "address-rdma-gpu-locks", marker = "sys_platform == 'darwin'" }
-mlx-lm = { git = "https://github.com/rltakashige/mlx-lm", branch = "leo/eval-left-padding-in-batched-rotation" }
-torch = [{ index = "pytorch-cu130", marker = "sys_platform == 'linux'" }]
-vllm = { git = "https://github.com/hmellor/vllm.git", branch = "transformers-v5" }
+#mlx-lm = { git = "https://github.com/davidmcc73/mlx-lm", branch = "stable" }
 # Uncomment to use local mlx/mlx-lm development versions:
 # mlx = { path = "/Users/Shared/mlx", editable=true }
 # mlx-lm = { path = "/Users/Shared/mlx-lm", editable=true }
 
-[[tool.uv.index]]
-name = "pytorch-cu130"
-url = "https://download.pytorch.org/whl/cu130"
-explicit = true
-
 [build-system]
 requires = ["uv_build>=0.8.9,<0.9.0"]
 build-backend = "uv_build"
@@ -100,24 +95,8 @@ reportUnnecessaryTypeIgnoreComment = "error"
 pythonVersion = "3.13"
 pythonPlatform = "Darwin"
 
-exclude = [
-  "**/.venv",
-  "**/venv",
-  "**/__pycache__",
-  "**/exo_scripts",
-  "**/.direnv",
-  "**/rust",
-  "**/.github",
-  "**/vllm_patches",
-  "**/engines/vllm",
-]
+exclude = ["**/.venv", "**/venv", "**/__pycache__", "**/exo_scripts", "**/.direnv", "**/rust", "**/.github"]
 stubPath = ".mlx_typings"
-extraPaths = [".cuda_typings"]
-
-[[tool.basedpyright.executionEnvironments]]
-root = "src/exo/worker/runner/vllm_inference"
-extraPaths = ["src", ".cuda_typings"]
-reportMissingModuleSource = false
 
 [[tool.basedpyright.executionEnvironments]]
 root = "src"
@@ -131,34 +110,16 @@ root = "src"
 required-version = ">=0.8.6"
 prerelease = "allow"
 environments = [
-  "sys_platform == 'darwin'",
-  "sys_platform == 'linux' and platform_machine == 'aarch64'",
+    "sys_platform == 'darwin'",
+    "sys_platform == 'linux'",
 ]
-no-binary-package = ["vllm", "flashinfer-python"]
-no-build-isolation-package = ["vllm", "flashinfer-python"]
-extra-build-dependencies = { vllm = [
-  "cmake>=3.26.1",
-  "ninja",
-  "packaging>=24.2",
-  "setuptools>=77.0.3,<81.0.0",
-  "setuptools-scm>=8.0",
-  "wheel",
-  "jinja2",
-] }
-conflicts = [[{ package = "exo", extra = "cuda" }, { package = "exo-bench" }]]
 
 ###
 # ruff configuration
 ###
 
 [tool.ruff]
-extend-exclude = [
-  "shared/protobufs/**",
-  "*mlx_typings/**",
-  "*cuda_typings/**",
-  "rust/exo_pyo3_bindings/**",
-  "bench/vendor/**",
-]
+extend-exclude = ["shared/protobufs/**", "*mlx_typings/**", "rust/exo_pyo3_bindings/**"]
 
 [tool.ruff.lint]
 extend-select = ["I", "N", "B", "A", "PIE", "SIM"]
@@ -166,7 +127,13 @@ extend-select = ["I", "N", "B", "A", "PIE", "SIM"]
 [tool.pytest.ini_options]
 pythonpath = "."
 asyncio_mode = "auto"
-markers = ["slow: marks tests as slow (deselected by default)"]
-env = ["EXO_TESTS=1"]
+markers = [
+    "slow: marks tests as slow (deselected by default)"
+]
+env = [
+  "EXO_TESTS=1"
+]
 addopts = "-m 'not slow' --ignore=tests/start_distributed_test.py"
-filterwarnings = ["ignore:builtin type Swig:DeprecationWarning"]
+filterwarnings = [
+    "ignore:builtin type Swig:DeprecationWarning",
+]

python/parts.nix

@@ -3,7 +3,6 @@
   perSystem =
     { config, self', pkgs, lib, system, ... }:
     let
-      pkgsCuda = import ../nix/cuda-pkgs.nix { nixpkgs = inputs.nixpkgs; inherit system; };
       # Load workspace from uv.lock
       workspace = inputs.uv2nix.lib.workspace.loadWorkspace {
         workspaceRoot = inputs.self;
@@ -44,27 +43,6 @@
             final.setuptools
           ];
         });
-        # rouge-score and sacrebleu don't declare setuptools as a build dependency
-        rouge-score = prev.rouge-score.overrideAttrs (old: {
-          nativeBuildInputs = (old.nativeBuildInputs or [ ]) ++ [
-            final.setuptools
-          ];
-        });
-        sacrebleu = prev.sacrebleu.overrideAttrs (old: {
-          nativeBuildInputs = (old.nativeBuildInputs or [ ]) ++ [
-            final.setuptools
-          ];
-        });
-        sqlitedict = prev.sqlitedict.overrideAttrs (old: {
-          nativeBuildInputs = (old.nativeBuildInputs or [ ]) ++ [
-            final.setuptools
-          ];
-        });
-        word2number = prev.word2number.overrideAttrs (old: {
-          nativeBuildInputs = (old.nativeBuildInputs or [ ]) ++ [
-            final.setuptools
-          ];
-        });
       } // lib.optionalAttrs pkgs.stdenv.hostPlatform.isDarwin {
         # Use our pure Nix-built MLX with Metal support (macOS only)
         mlx = self'.packages.mlx;
@@ -100,18 +78,16 @@
           }
         );
 
-      baseOverlays = [
-        inputs.pyproject-build-systems.overlays.default
-        overlay
-        exoOverlay
-        buildSystemsOverlay
-        linuxOverlay
-      ];
-
       pythonSet = (pkgs.callPackage inputs.pyproject-nix.build.packages {
         inherit python;
       }).overrideScope (
-        lib.composeManyExtensions baseOverlays
+        lib.composeManyExtensions [
+          inputs.pyproject-build-systems.overlays.default
+          overlay
+          exoOverlay
+          buildSystemsOverlay
+          linuxOverlay
+        ]
       );
       # mlx-cpu and mlx-cuda-13 both ship mlx/ site-packages files; keep first.
       # mlx-cpu/mlx-cuda-13 and nvidia-cudnn-cu12/cu13 ship overlapping files.
@@ -120,16 +96,13 @@
         "lib/python3.13/site-packages/nvidia*"
       ];
 
-      # Exclude bench deps from main env (bench has its own benchVenv)
-      exoDeps = removeAttrs workspace.deps.default [ "exo-bench" ];
-
-      exoVenv = (pythonSet.mkVirtualEnv "exo-env" exoDeps).overrideAttrs {
+      exoVenv = (pythonSet.mkVirtualEnv "exo-env" workspace.deps.default).overrideAttrs {
         venvIgnoreCollisions = venvCollisionPaths;
       };
 
       # Virtual environment with dev dependencies for testing
       testVenv = (pythonSet.mkVirtualEnv "exo-test-env" (
-        exoDeps // {
+        workspace.deps.default // {
           exo = [ "dev" ]; # Include pytest, pytest-asyncio, pytest-env
         }
       )).overrideAttrs {
@@ -175,39 +148,6 @@
             --set EXO_RESOURCES_DIR ${inputs.self + /resources} \
             ${lib.optionalString pkgs.stdenv.hostPlatform.isDarwin "--prefix PATH : ${pkgs.macmon}/bin"}
         '';
-
-      vllmEnv = pkgsCuda.python313.withPackages (ps: [ ps.vllm ps.fastsafetensors ]);
-
-      vllmSite = pkgs.runCommand "vllm-site-filtered" { } ''
-        mkdir -p $out
-        for pkg in ${vllmEnv}/${python.sitePackages}/*; do
-          name=$(basename "$pkg")
-          case "$name" in
-            anyio*|pydantic*) ;;
-            *) ln -s "$pkg" "$out/$name" ;;
-          esac
-        done
-      '';
-
-      exoCudaDeps = exoDeps // {
-        mlx-cuda-13 = [ ];
-      };
-
-      exoCudaVenv = (pythonSet.mkVirtualEnv "exo-cuda-env" exoCudaDeps).overrideAttrs {
-        venvIgnoreCollisions = venvCollisionPaths;
-      };
-
-      exoCudaPackage = pkgs.runCommand "exo-cuda"
-        {
-          nativeBuildInputs = [ pkgs.makeWrapper ];
-        }
-        ''
-          mkdir -p $out/bin
-          makeWrapper ${exoCudaVenv}/bin/exo $out/bin/exo-cuda \
-            --set EXO_DASHBOARD_DIR ${self'.packages.dashboard} \
-            --set EXO_RESOURCES_DIR ${inputs.self + /resources} \
-            --prefix PYTHONPATH : "${vllmSite}"
-        '';
     in
     {
       # Python package only available on macOS (requires MLX/Metal)
@@ -216,11 +156,8 @@
           exo = exoPackage;
           # Test environment for running pytest outside of Nix sandbox (needs GPU access)
           exo-test-env = testVenv;
-        } // lib.optionalAttrs (pkgsCuda != null) {
-        exo-cuda-unwrapped = exoCudaPackage;
-      } // {
+        } // {
         exo-bench = mkBenchScript "exo-bench" (inputs.self + /bench/exo_bench.py);
-        exo-eval = mkBenchScript "exo-eval" (inputs.self + /bench/exo_eval.py);
         exo-eval-tool-calls = mkBenchScript "exo-eval-tool-calls" (inputs.self + /bench/eval_tool_calls.py);
         exo-get-all-models-on-cluster = mkSimplePythonScript "exo-get-all-models-on-cluster" (inputs.self + /tests/get_all_models_on_cluster.py);
       };

resources/inference_model_cards/mlx-community--DeepSeek-V3.1-4bit.toml

@@ -1,7 +1,6 @@
 model_id = "mlx-community/DeepSeek-V3.1-4bit"
 n_layers = 61
 hidden_size = 7168
-num_key_value_heads = 128
 supports_tensor = true
 tasks = ["TextGeneration"]
 family = "deepseek"

resources/inference_model_cards/mlx-community--DeepSeek-V3.1-8bit.toml

@@ -1,7 +1,6 @@
 model_id = "mlx-community/DeepSeek-V3.1-8bit"
 n_layers = 61
 hidden_size = 7168
-num_key_value_heads = 128
 supports_tensor = true
 tasks = ["TextGeneration"]
 family = "deepseek"

resources/inference_model_cards/mlx-community--GLM-4.5-Air-8bit.toml

@@ -1,7 +1,6 @@
 model_id = "mlx-community/GLM-4.5-Air-8bit"
 n_layers = 46
 hidden_size = 4096
-num_key_value_heads = 8
 supports_tensor = false
 tasks = ["TextGeneration"]
 family = "glm"

resources/inference_model_cards/mlx-community--GLM-4.5-Air-bf16.toml

@@ -1,7 +1,6 @@
 model_id = "mlx-community/GLM-4.5-Air-bf16"
 n_layers = 46
 hidden_size = 4096
-num_key_value_heads = 8
 supports_tensor = true
 tasks = ["TextGeneration"]
 family = "glm"

resources/inference_model_cards/mlx-community--GLM-4.7-4bit.toml

@@ -1,7 +1,6 @@
 model_id = "mlx-community/GLM-4.7-4bit"
 n_layers = 91
 hidden_size = 5120
-num_key_value_heads = 8
 supports_tensor = true
 tasks = ["TextGeneration"]
 family = "glm"

resources/inference_model_cards/mlx-community--GLM-4.7-6bit.toml

@@ -1,7 +1,6 @@
 model_id = "mlx-community/GLM-4.7-6bit"
 n_layers = 91
 hidden_size = 5120
-num_key_value_heads = 8
 supports_tensor = true
 tasks = ["TextGeneration"]
 family = "glm"

resources/inference_model_cards/mlx-community--GLM-4.7-8bit-gs32.toml

@@ -1,7 +1,6 @@
 model_id = "mlx-community/GLM-4.7-8bit-gs32"
 n_layers = 91
 hidden_size = 5120
-num_key_value_heads = 8
 supports_tensor = true
 tasks = ["TextGeneration"]
 family = "glm"

resources/inference_model_cards/mlx-community--GLM-4.7-Flash-4bit.toml

@@ -1,7 +1,6 @@
 model_id = "mlx-community/GLM-4.7-Flash-4bit"
 n_layers = 47
 hidden_size = 2048
-num_key_value_heads = 20
 supports_tensor = true
 tasks = ["TextGeneration"]
 family = "glm"

resources/inference_model_cards/mlx-community--GLM-4.7-Flash-5bit.toml

@@ -1,7 +1,6 @@
 model_id = "mlx-community/GLM-4.7-Flash-5bit"
 n_layers = 47
 hidden_size = 2048
-num_key_value_heads = 20
 supports_tensor = true
 tasks = ["TextGeneration"]
 family = "glm"

resources/inference_model_cards/mlx-community--GLM-4.7-Flash-6bit.toml

@@ -1,7 +1,6 @@
 model_id = "mlx-community/GLM-4.7-Flash-6bit"
 n_layers = 47
 hidden_size = 2048
-num_key_value_heads = 20
 supports_tensor = true
 tasks = ["TextGeneration"]
 family = "glm"

resources/inference_model_cards/mlx-community--GLM-4.7-Flash-8bit.toml

@@ -1,7 +1,6 @@
 model_id = "mlx-community/GLM-4.7-Flash-8bit"
 n_layers = 47
 hidden_size = 2048
-num_key_value_heads = 20
 supports_tensor = true
 tasks = ["TextGeneration"]
 family = "glm"

resources/inference_model_cards/mlx-community--GLM-5-8bit.toml

@@ -1,7 +1,6 @@
 model_id = "mlx-community/GLM-5-8bit-MXFP8"
 n_layers = 78
 hidden_size = 6144
-num_key_value_heads = 64
 supports_tensor = true
 tasks = ["TextGeneration"]
 family = "glm"

resources/inference_model_cards/mlx-community--GLM-5-MXFP4-Q8.toml

@@ -1,7 +1,6 @@
 model_id = "mlx-community/GLM-5-MXFP4-Q8"
 n_layers = 78
 hidden_size = 6144
-num_key_value_heads = 64
 supports_tensor = true
 tasks = ["TextGeneration"]
 family = "glm"

resources/inference_model_cards/mlx-community--GLM-5-bf16.toml

@@ -1,7 +1,6 @@
 model_id = "mlx-community/GLM-5"
 n_layers = 78
 hidden_size = 6144
-num_key_value_heads = 64
 supports_tensor = true
 tasks = ["TextGeneration"]
 family = "glm"

resources/inference_model_cards/mlx-community--Kimi-K2-Instruct-4bit.toml

@@ -1,7 +1,6 @@
 model_id = "mlx-community/Kimi-K2-Instruct-4bit"
 n_layers = 61
 hidden_size = 7168
-num_key_value_heads = 64
 supports_tensor = true
 tasks = ["TextGeneration"]
 family = "kimi"

resources/inference_model_cards/mlx-community--Kimi-K2-Thinking.toml

@@ -1,7 +1,6 @@
 model_id = "mlx-community/Kimi-K2-Thinking"
 n_layers = 61
 hidden_size = 7168
-num_key_value_heads = 64
 supports_tensor = true
 tasks = ["TextGeneration"]
 family = "kimi"