Call warmup_speculative at startup to pre-compile LpB kernels

The warmup_speculative() function was defined but never called.
Custom Metal kernels (LpB) require first-call compilation (~200ms).
Without warmup, the first speculative cycle is slow, dragging down
average TPS by 10-20% on short generations.

In mlx_bench testing: cold 48 TPS → warm 60 TPS for DFlash,
cold 39 TPS → warm 44 TPS for MTP.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

.mlx_typings/mlx/core/__init__.pyi

@@ -2396,7 +2396,7 @@ def degrees(a: array, /, *, stream: Stream | Device | None = ...) -> array:
         array: The angles in degrees.
     """
 
-def depends(inputs: array | Sequence[array], dependencies: array | Sequence[array]):
+def depends[T](inputs: T, dependencies: array | Sequence[array]) -> T:
     """
     Insert dependencies between arrays in the graph. The outputs are
     identical to ``inputs`` but with dependencies on ``dependencies``.

.mlx_typings/mlx/nn/__init__.pyi

@@ -1,9 +1,5 @@
-"""
-This type stub file was generated by pyright.
-"""
-
-from layers import *
-from utils import *
+from .layers import *
+from .utils import *
 
 from . import init as init
 from . import losses as losses

.mlx_typings/mlx/nn/layers/__init__.pyi

@@ -1,20 +1,16 @@
-"""
-This type stub file was generated by pyright.
-"""
-
-from activations import *
-from base import *
-from containers import *
-from convolution import *
-from convolution_transpose import *
-from distributed import *
-from dropout import *
-from embedding import *
-from linear import *
-from normalization import *
-from pooling import *
-from positional_encoding import *
-from quantized import *
-from recurrent import *
-from transformer import *
-from upsample import *
+from .activations import *
+from .base import *
+from .containers import *
+from .convolution import *
+from .convolution_transpose import *
+from .distributed import *
+from .dropout import *
+from .embedding import *
+from .linear import *
+from .normalization import *
+from .pooling import *
+from .positional_encoding import *
+from .quantized import *
+from .recurrent import *
+from .transformer import *
+from .upsample import *

.mlx_typings/mlx/nn/layers/base.pyi

@@ -53,7 +53,7 @@ class Module(dict):
         mx.eval(model.parameters())
     """
 
-    __call__: Callable
+    def __call__(self, *args: Any, **kwargs: Any) -> mx.array: ...
     def __init__(self) -> None:
         """Should be called by the subclasses of ``Module``."""
 

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

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

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

@@ -40,6 +40,10 @@ 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

@@ -88,6 +88,9 @@ 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

@@ -30,7 +30,7 @@ def str2bool(string):  # -> bool:
 def setup_arg_parser():  # -> ArgumentParser:
     """Set up and return the argument parser."""
 
-generation_stream = ...
+generation_stream: mx.Stream
 
 @contextlib.contextmanager
 def wired_limit(
@@ -254,48 +254,92 @@ 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]
     y: mx.array
-    logprobs: mx.array
+    logprobs: List[mx.array] | mx.array
     max_tokens: List[int]
     num_tokens: List[int]
     cache: List[Any]
-    def __len__(self):  # -> int:
-        ...
-    def filter(self, keep_idx: List[int]):  # -> None:
-        ...
-    def extend(self, other):  # -> None:
-        ...
+    samplers: List[Callable[[mx.array], mx.array] | None]
+    logits_processors: List[List[Callable[[mx.array, mx.array], mx.array]]]
+    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]: ...
 
 class BatchGenerator:
+    model: nn.Module
+    sampler: Callable[[mx.array], mx.array]
+    stop_tokens: set[int]
+    max_kv_size: Optional[int]
+    prefill_step_size: int
+    completion_batch_size: int
+    prefill_batch_size: int
+    unprocessed_prompts: List[Any]
+    active_batch: Optional[Batch]
+    prompt_progress_callback: Callable[[List[Tuple[int, int, int]]], None]
+    _stats: BatchStats
+    _next_count: int
+
     @dataclass
     class Response:
         uid: int
         token: int
         logprobs: mx.array
         finish_reason: Optional[str]
+        prompt_cache: Any
 
     def __init__(
         self,
-        model,
+        model: Any,
         max_tokens: int = ...,
-        stop_tokens: Optional[set] = ...,
+        stop_tokens: Optional[set[int]] = ...,
         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, max_tokens: Union[List[int], int, None] = ...
-    ):  # -> list[Any]:
-        ...
-    def stats(self):  # -> BatchStats:
-        ...
-    def next(self):  # -> list[Any]:
-        ...
+        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]]: ...
 
 def batch_generate(
     model,

.mlx_typings/mlx_lm/models/cache.pyi

@@ -88,8 +88,8 @@ def create_attention_mask(
 ) -> array | Literal["causal"] | None: ...
 
 class _BaseCache(Cache):
-    keys: mx.array
-    values: mx.array
+    keys: mx.array | None
+    values: mx.array | None
     offset: int
     @property
     def state(self) -> tuple[mx.array | None, mx.array | None]: ...
@@ -170,7 +170,15 @@ class KVCache(_BaseCache):
 
 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]:
@@ -260,29 +268,14 @@ class CacheList(_BaseCache):
         """
 
 class BatchKVCache(_BaseCache):
-    step = ...
-    def __init__(self, left_padding: List[int]) -> None:
-        """
-        The BatchKV cache expects inputs to be left-padded.
-
-        E.g. the following prompts:
-
-            [1, 3, 5]
-            [7]
-            [2, 6, 8, 9]
-
-        Should be padded like so:
-
-            [0, 1, 3, 5]
-            [0, 0, 0, 7]
-            [2, 6, 8, 9]
-
-        And ``left_padding`` specifies the amount of padding for each.
-        In this case, ``left_padding = [1, 3, 0]``.
-        """
-
-    def update_and_fetch(self, keys, values):  # -> tuple[array | Any, array | Any]:
-        ...
+    step: int
+    keys: array | None
+    values: array | None
+    offset: array
+    left_padding: array
+    _idx: int
+    def __init__(self, left_padding: List[int]) -> None: ...
+    def update_and_fetch(self, keys: array, values: array) -> tuple[array, array]: ...
     @property
     def state(
         self,
@@ -308,12 +301,21 @@ class BatchKVCache(_BaseCache):
         """
 
 class BatchRotatingKVCache(_BaseCache):
-    step = ...
-    def __init__(self, max_size, left_padding: List[int]) -> None: ...
-    def update_and_fetch(
-        self, keys, values
-    ):  # -> tuple[array | Any, array | Any] | tuple[array | Any, array | Any | None]:
-        ...
+    step: int
+    keys: array | None
+    values: array | None
+    offset: array
+    left_padding: array
+    max_size: int
+    _idx: int
+    _offset: int
+    rotated: bool
+    _lengths: array | None
+    def __init__(self, max_size: int, left_padding: List[int]) -> None: ...
+    def _trim(self, trim_size: int, v: array, append: array | None = ...) -> array: ...
+    def _update_in_place(self, keys: array, values: array) -> tuple[array, array]: ...
+    def _update_concat(self, keys: array, values: array) -> tuple[array, array]: ...
+    def update_and_fetch(self, keys: array, values: array) -> tuple[array, array]: ...
     @property
     def state(
         self,

.mlx_typings/mlx_lm/models/gated_delta.pyi

@@ -0,0 +1,35 @@
+from typing import Optional
+
+import mlx.core as mx
+
+def compute_g(A_log: mx.array, a: mx.array, dt_bias: mx.array) -> mx.array: ...
+def gated_delta_update(
+    q: mx.array,
+    k: mx.array,
+    v: mx.array,
+    a: mx.array,
+    b: mx.array,
+    A_log: mx.array,
+    dt_bias: mx.array,
+    state: Optional[mx.array] = ...,
+    mask: Optional[mx.array] = ...,
+    use_kernel: bool = ...,
+) -> tuple[mx.array, mx.array]: ...
+def gated_delta_ops(
+    q: mx.array,
+    k: mx.array,
+    v: mx.array,
+    g: mx.array,
+    beta: mx.array,
+    state: Optional[mx.array] = ...,
+    mask: Optional[mx.array] = ...,
+) -> tuple[mx.array, mx.array]: ...
+def gated_delta_kernel(
+    q: mx.array,
+    k: mx.array,
+    v: mx.array,
+    g: mx.array,
+    beta: mx.array,
+    state: mx.array,
+    mask: Optional[mx.array] = ...,
+) -> tuple[mx.array, mx.array]: ...

.mlx_typings/mlx_lm/models/nemotron_h.pyi

@@ -0,0 +1,154 @@
+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_next.pyi

@@ -5,6 +5,7 @@ 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):
@@ -99,6 +100,8 @@ 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__(
@@ -121,3 +124,4 @@ 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/rope_utils.pyi

@@ -0,0 +1,51 @@
+from typing import Any, Optional
+
+import mlx.nn as nn
+
+class YarnRoPE(nn.Module):
+    def __init__(
+        self,
+        dims: int,
+        traditional: bool = ...,
+        max_position_embeddings: int = ...,
+        base: float = ...,
+        scaling_factor: float = ...,
+        original_max_position_embeddings: int = ...,
+        beta_fast: float = ...,
+        beta_slow: float = ...,
+        mscale: float = ...,
+        mscale_all_dim: float = ...,
+    ) -> None: ...
+
+class Llama3RoPE(nn.Module):
+    def __init__(
+        self,
+        dims: int,
+        traditional: bool = ...,
+        max_position_embeddings: int = ...,
+        base: float = ...,
+        scaling_factor: float = ...,
+        original_max_position_embeddings: int = ...,
+        low_freq_factor: float = ...,
+        high_freq_factor: float = ...,
+    ) -> None: ...
+
+class SuScaledRoPE(nn.Module):
+    def __init__(
+        self,
+        dims: int,
+        traditional: bool = ...,
+        max_position_embeddings: int = ...,
+        base: float = ...,
+        short_factor: Any = ...,
+        long_factor: Any = ...,
+        original_max_position_embeddings: int = ...,
+    ) -> None: ...
+
+def initialize_rope(
+    dims: int,
+    base: float = ...,
+    traditional: bool = ...,
+    scaling_config: Optional[dict[str, Any]] = ...,
+    max_position_embeddings: Optional[int] = ...,
+) -> nn.Module: ...

.mlx_typings/mlx_lm/models/switch_layers.pyi

@@ -73,6 +73,9 @@ 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/tokenizer_utils.pyi

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

bench/eval_configs/models.toml

@@ -0,0 +1,292 @@
+# 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/exo_bench.py

@@ -24,6 +24,7 @@ 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
@@ -252,6 +253,12 @@ 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,
@@ -282,6 +289,10 @@ 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)
@@ -293,7 +304,9 @@ 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)
-    short_id, full_model_id = resolve_model_short_id(client, args.model)
+    short_id, full_model_id = resolve_model_short_id(
+        client, args.model, force_download=args.force_download
+    )
 
     tokenizer = load_tokenizer_for_bench(full_model_id)
     if tokenizer is None:
@@ -392,52 +405,129 @@ def main() -> int:
                 pp_tg_pairs = list(zip(pp_list, tg_list, strict=True))
 
             for pp, tg in pp_tg_pairs:
-                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
+                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
+                                ]
+                                per_req_tps = (
+                                    mean(valid_gen_tps) if valid_gen_tps else 0.0
+                                )
+                                agg_gen_tps = per_req_tps * concurrency
+                                logger.info(
+                                    f"[concurrent {concurrency}x]  "
+                                    f"agg_gen_tps={agg_gen_tps:.2f}  "
+                                    f"per_req_tps={per_req_tps:.2f}  "
+                                    f"errors={batch_errors}"
+                                )
+
+                    if runs:
+                        prompt_tps = mean(x["stats"]["prompt_tps"] for x in runs)
+                        per_req_tps = mean(x["stats"]["generation_tps"] for x in runs)
+                        gen_tps = per_req_tps * concurrency
+                        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
                         )
-                    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)
 
-                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)
+                        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

@@ -165,7 +165,9 @@ 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) -> tuple[str, str]:
+def resolve_model_short_id(
+    client: ExoClient, model_arg: str, *, force_download: bool = False
+) -> tuple[str, str]:
     models = client.request_json("GET", "/models") or {}
     data = models.get("data") or []
 
@@ -181,6 +183,16 @@ def resolve_model_short_id(client: ExoClient, model_arg: str) -> tuple[str, str]
             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}")
 
 
@@ -445,6 +457,11 @@ 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,

bench/parallel_requests.py

@@ -0,0 +1,255 @@
+# 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/pyproject.toml

@@ -11,6 +11,11 @@ dependencies = [
   "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",
 ]
 
 [build-system]

bench/run_lcb_tps_bench.sh

@@ -0,0 +1,91 @@
+#!/usr/bin/env bash
+#
+# Run exo_bench.py for each model/mode from bench_params.json.
+#
+# For each entry, runs with:
+#   --pp 800 (fixed, representative LCB prompt length)
+#   --tg <mean completion tokens from vLLM>
+#   --sharding tensor --instance-meta jaccl
+#   --min-nodes 1 --max-nodes 4
+#   --repeat 1
+#   --danger-delete-downloads
+#   --settle-timeout 300
+#
+# Results go to bench/eval_results/<model_dir>/tps_<mode>.json
+#
+# Usage:
+#   bash bench/run_lcb_tps_bench.sh              # run all
+#   bash bench/run_lcb_tps_bench.sh --dry-run    # show what would run
+
+set -euo pipefail
+cd "$(dirname "$0")"
+
+PARAMS_FILE="eval_results/bench_params.json"
+PP=800
+HOST="${EXO_HOST:-s9}"
+DRY_RUN=false
+
+if [[ "${1:-}" == "--dry-run" ]]; then
+    DRY_RUN=true
+fi
+
+if [[ ! -f "$PARAMS_FILE" ]]; then
+    echo "ERROR: $PARAMS_FILE not found. Run compute_bench_params.py first."
+    exit 1
+fi
+
+# Parse bench_params.json and run each entry
+python3 -c "
+import json, sys
+data = json.load(open('$PARAMS_FILE'))
+for entry in data:
+    mlx_id = entry['mlx_model_id']
+    mode = entry['mode']
+    tg = entry['bench_params']['tg']
+    vllm_name = entry['vllm_name']
+    # Output dir: replace / with _
+    out_dir = 'eval_results/' + mlx_id.replace('/', '_')
+    out_file = out_dir + '/tps_' + mode + '.json'
+    print(f'{mlx_id}\t{mode}\t{tg}\t{out_file}\t{vllm_name}')
+" | while IFS=$'\t' read -r model mode tg out_file vllm_name; do
+    out_dir="$(dirname "$out_file")"
+    mkdir -p "$out_dir"
+
+    echo ""
+    echo "============================================================"
+    echo "Model:  $model"
+    echo "Mode:   $mode"
+    echo "vLLM:   $vllm_name"
+    echo "PP:     $PP"
+    echo "TG:     $tg"
+    echo "Output: $out_file"
+    echo "============================================================"
+
+    if [[ -f "$out_file" ]]; then
+        echo "SKIP: $out_file already exists"
+        continue
+    fi
+
+    if [[ "$DRY_RUN" == "true" ]]; then
+        echo "DRY-RUN: would run exo_bench.py"
+        continue
+    fi
+
+    uv run python exo_bench.py \
+        --host "$HOST" \
+        --model "$model" \
+        --pp "$PP" \
+        --tg "$tg" \
+        --repeat 1 \
+        --sharding tensor \
+        --instance-meta jaccl \
+        --min-nodes 1 \
+        --max-nodes 4 \
+        --settle-timeout 300 \
+        --force-download \
+        --danger-delete-downloads \
+        --json-out "$out_file" || echo "FAILED: $model ($mode)"
+done
+
+echo ""
+echo "All benchmarks complete."

bench/vendor/lcb_testing_util.py

@@ -0,0 +1,593 @@
+# 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

dashboard/src/lib/components/ChatForm.svelte

@@ -1,9 +1,6 @@
 <script lang="ts">
   import {
     isLoading,
-    sendMessage,
-    generateImage,
-    editImage,
     editingImage,
     clearEditingImage,
     selectedChatModel,
@@ -28,7 +25,7 @@
     modelTasks?: Record<string, string[]>;
     modelCapabilities?: Record<string, string[]>;
     onSend?: () => void;
-    onAutoSend?: (
+    onAutoSend: (
       content: string,
       files?: {
         id: string;
@@ -216,49 +213,10 @@
     uploadedFiles = [];
     resetTextareaHeight();
 
-    // 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,
-      );
-    }
-
+    // Parent controls all send logic (including image routing,
+    // launching non-running models before sending, etc.)
+    onAutoSend(content, files);
     onSend?.();
-
-    // Refocus the textarea after sending
     setTimeout(() => textareaRef?.focus(), 10);
   }
 

dashboard/src/lib/components/ChatSidebar.svelte

@@ -18,12 +18,18 @@
     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());
@@ -53,6 +59,10 @@
   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) {
@@ -252,9 +262,7 @@
   }
 </script>
 
-<aside
-  class="flex flex-col h-full bg-exo-dark-gray border-r border-exo-yellow/10 {className}"
->
+{#snippet sidebarContent()}
   <!-- Header -->
   <div class="p-4">
     <button
@@ -591,4 +599,30 @@
       </button>
     </div>
   </div>
-</aside>
+{/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}

dashboard/src/lib/components/FamilyLogos.svelte

@@ -82,6 +82,12 @@
       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-[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-[72px] sm: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 rounded transition-all duration-200 cursor-pointer {selectedFamily ===
+    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 ===
     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

@@ -6,6 +6,12 @@
   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;
@@ -27,49 +33,96 @@
       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-6 pt-8 pb-4 bg-exo-dark-gray"
+  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"
 >
-  <!-- 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}
+  <!-- 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'}"
       >
-        <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}
+        {#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>
 
   <!-- Center: Logo (clickable to go home) -->
   <button
@@ -83,19 +136,55 @@
     <img
       src="/exo-logo.png"
       alt="EXO"
-      class="h-18 drop-shadow-[0_0_4px_rgba(255,215,0,0.3)]"
+      class="h-12 md:h-18 drop-shadow-[0_0_4px_rgba(255,215,0,0.3)]"
     />
   </button>
 
-  <!-- Right: Home + Downloads -->
+  <!-- Right: Home + Downloads + Mobile Right Toggle -->
   <nav
-    class="absolute right-6 top-1/2 -translate-y-1/2 flex items-center gap-4"
+    class="absolute right-4 md:right-6 top-1/2 -translate-y-1/2 flex items-center gap-2 md: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="text-sm text-white/70 hover:text-exo-yellow transition-colors tracking-wider uppercase flex items-center gap-2 cursor-pointer"
+        class="flex text-sm text-white/70 hover:text-exo-yellow transition-colors tracking-wider uppercase items-center gap-2 cursor-pointer"
         title="Back to topology view"
       >
         <svg
@@ -111,12 +200,12 @@
             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>
-        Home
+        <span class="hidden sm:inline">Home</span>
       </button>
     {/if}
     <a
       href="/#/downloads"
-      class="text-sm text-white/70 hover:text-exo-yellow transition-colors tracking-wider uppercase flex items-center gap-2 cursor-pointer"
+      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"
       title="View downloads overview"
     >
       {#if downloadProgress}
@@ -168,7 +257,7 @@
           <path d="M5 21h14" />
         </svg>
       {/if}
-      Downloads
+      <span class="hidden sm:inline">Downloads</span>
     </a>
   </nav>
 </header>

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

@@ -580,6 +580,8 @@ 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>({
@@ -1245,6 +1247,30 @@ 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);
@@ -1551,6 +1577,7 @@ 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":
@@ -1766,6 +1793,14 @@ class AppStore {
             this.persistConversation(targetConversationId);
           }
         },
+        {
+          generation_stats: (data) => {
+            const stats = data as { generation_tps: number };
+            if (stats.generation_tps > 0) {
+              this.tps = stats.generation_tps;
+            }
+          },
+        },
       );
 
       // Final update
@@ -1963,6 +1998,14 @@ class AppStore {
             this.persistConversation(targetConversationId);
           }
         },
+        {
+          generation_stats: (data) => {
+            const stats = data as { generation_tps: number };
+            if (stats.generation_tps > 0) {
+              this.tps = stats.generation_tps;
+            }
+          },
+        },
       );
 
       // Final cleanup of the message (if conversation still exists)
@@ -2370,7 +2413,7 @@ class AppStore {
 
       let streamedContent = "";
       let streamedThinking = "";
-
+      let serverTpsReceived = false;
       interface ChatCompletionChunk {
         choices?: Array<{
           delta?: { content?: string; reasoning_content?: string };
@@ -2435,7 +2478,6 @@ class AppStore {
             tokenCount += 1;
             this.totalTokens = tokenCount;
 
-            // Update real-time TPS during streaming
             if (firstTokenTime !== null && tokenCount > 1) {
               const elapsed = performance.now() - firstTokenTime;
               this.tps = (tokenCount / elapsed) * 1000;
@@ -2486,16 +2528,24 @@ class AppStore {
               startedAt: this.prefillProgress?.startedAt ?? performance.now(),
             };
           },
+          generation_stats: (data) => {
+            const stats = data as { generation_tps: number };
+
+            if (stats.generation_tps > 0) {
+              this.tps = stats.generation_tps;
+              serverTpsReceived = true;
+            }
+          },
         },
       );
 
       // Clear prefill progress after stream ends
       this.prefillProgress = null;
 
-      // Calculate final TPS
-      if (firstTokenTime !== null && tokenCount > 1) {
+      // Use server-side TPS if available, otherwise fall back to client-side
+      if (!serverTpsReceived && firstTokenTime !== null && tokenCount > 1) {
         const totalGenerationTime = performance.now() - firstTokenTime;
-        this.tps = (tokenCount / totalGenerationTime) * 1000; // tokens per second
+        this.tps = (tokenCount / totalGenerationTime) * 1000;
       }
 
       // Final cleanup of the message (if conversation still exists)
@@ -3282,6 +3332,18 @@ 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

dashboard/src/routes/+page.svelte

@@ -42,6 +42,9 @@
     setSelectedChatModel,
     selectedChatModel,
     sendMessage,
+    generateImage,
+    editImage,
+    editingImage,
     messages,
     debugMode,
     toggleDebugMode,
@@ -49,6 +52,12 @@
     toggleTopologyOnlyMode,
     chatSidebarVisible,
     toggleChatSidebarVisible,
+    mobileChatSidebarOpen,
+    toggleMobileChatSidebar,
+    setMobileChatSidebarOpen,
+    mobileRightSidebarOpen,
+    toggleMobileRightSidebar,
+    setMobileRightSidebarOpen,
     nodeThunderbolt,
     nodeRdmaCtl,
     thunderboltBridgeCycles,
@@ -79,6 +88,8 @@
   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());
@@ -826,6 +837,52 @@
     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";
 
@@ -1519,7 +1576,11 @@
           downloadKind
         ] as Record<string, unknown>;
 
-        if (downloadKind !== "DownloadOngoing") continue;
+        if (
+          downloadKind !== "DownloadOngoing" &&
+          downloadKind !== "DownloadPending"
+        )
+          continue;
         if (!downloadPayload) continue;
 
         const downloadModelId = extractModelIdFromDownload(downloadPayload);
@@ -1534,9 +1595,38 @@
           if (downloadModelId !== modelId) continue;
         }
 
-        isDownloading = true;
+        // 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);
+        }
 
-        const progress = parseDownloadProgress(downloadPayload);
         if (progress) {
           // Sum all values across nodes - each node downloads independently
           totalBytes += progress.totalBytes;
@@ -1688,7 +1778,11 @@
           }
         }
 
-        if (downloadKind !== "DownloadOngoing") continue;
+        if (
+          downloadKind !== "DownloadOngoing" &&
+          downloadKind !== "DownloadPending"
+        )
+          continue;
         if (!downloadPayload) continue;
 
         // Check if this download is for this instance's model
@@ -1698,9 +1792,37 @@
           downloadModelId &&
           downloadModelId === instanceModelId
         ) {
-          isDownloading = true;
+          // 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);
+          }
 
-          const progress = parseDownloadProgress(downloadPayload);
           if (progress) {
             // Sum all values across nodes - each node downloads independently
             totalBytes += progress.totalBytes;
@@ -2778,7 +2900,7 @@
         // Running model is same or better tier — use it directly
         setSelectedChatModel(bestRunning.id);
         if (!chatStarted) createConversation();
-        sendMessage(content, files);
+        routeMessage(content, files);
         return;
       }
     }
@@ -2795,7 +2917,7 @@
     if (hasRunningInstance(autoModel.id)) {
       setSelectedChatModel(autoModel.id);
       if (!chatStarted) createConversation();
-      sendMessage(content, files);
+      routeMessage(content, files);
       return;
     }
 
@@ -2948,7 +3070,7 @@
       if (pendingAutoMessage) {
         const msg = pendingAutoMessage;
         pendingAutoMessage = null;
-        sendMessage(msg.content, msg.files);
+        routeMessage(msg.content, msg.files);
       }
       return;
     }
@@ -3027,7 +3149,7 @@
     // Model is selected and running — send directly
     if (model && hasRunningInstance(model)) {
       chatLaunchState = "ready";
-      sendMessage(content, files, null);
+      routeMessage(content, files);
       return;
     }
 
@@ -4604,16 +4726,35 @@
       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) -->
+    <!-- Left: Conversation History Sidebar (hidden in topology-only mode, welcome state, or when toggled off) - Desktop only -->
     {#if !topologyOnlyEnabled && sidebarVisible}
       <div
-        class="w-80 flex-shrink-0 border-r border-exo-yellow/10"
+        class="hidden md:block w-80 flex-shrink-0 border-r border-exo-yellow/10"
         role="complementary"
         aria-label="Conversation history"
       >
@@ -4923,11 +5064,33 @@
           </div>
         </div>
 
-        <!-- Right Sidebar: Instance Controls (wider on welcome page for better visibility) -->
+        <!-- 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 -->
         <aside
-          class="w-80 border-l border-exo-yellow/10 bg-exo-dark-gray flex flex-col flex-shrink-0"
+          class="hidden md:flex w-80 border-l border-exo-yellow/10 bg-exo-dark-gray 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">
@@ -5809,7 +5972,7 @@
               {/if}
             </div>
           </div>
-        </aside>
+        {/snippet}
       </div>
     {:else}
       <!-- CHAT STATE: Chat + Mini-Map -->
@@ -6022,10 +6185,10 @@
           {/if}
         </div>
 
-        <!-- Right: Mini-Map Sidebar -->
+        <!-- Right: Mini-Map Sidebar - Desktop only -->
         {#if minimized}
           <aside
-            class="w-80 border-l border-exo-yellow/20 bg-exo-dark-gray flex flex-col flex-shrink-0 overflow-y-auto"
+            class="hidden md:flex w-80 border-l border-exo-yellow/20 bg-exo-dark-gray flex-col flex-shrink-0 overflow-y-auto"
             in:fly={{ x: 100, duration: 400, easing: cubicInOut }}
             aria-label="Cluster topology"
           >

flake.nix

@@ -117,12 +117,13 @@
               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;
+                inherit uvLockMlxVersion uvLockMlxRev;
               };
               default = self'.packages.exo;
             }

nix/mlx.nix

@@ -11,6 +11,7 @@
 , fmt
 , python313Packages
 , uvLockMlxVersion
+, uvLockMlxRev
 }:
 
 assert stdenv.isDarwin;
@@ -41,15 +42,13 @@ let
 
   mlx = stdenv.mkDerivation rec {
     pname = "mlx";
-    version = let v = "0.30.7.dev20260225+257d5692"; 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;
+    version = uvLockMlxVersion;
     pyproject = true;
 
     src = fetchFromGitHub {
       owner = "rltakashige";
       repo = "mlx-jaccl-fix-small-recv";
-      rev = "257d5692fc7af6bba3b8afaeb63c549b7d1e43d5";
+      rev = uvLockMlxRev;
       hash = "sha256-GosFIWxIB48Egb1MqJrR3xhsUsQeWdRk5rV93USY6wQ=";
     };
 

pyproject.toml

@@ -25,8 +25,7 @@ dependencies = [
   "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",
+  "mflux==0.16.9",
   "python-multipart>=0.0.21",
   "msgspec>=0.19.0",
   "zstandard>=0.23.0",
@@ -62,7 +61,7 @@ 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/ml-explore/mlx-lm", rev = "834fac934c4e04de9b3d723e2b9287a2c60cfd4a" }
+mlx-lm = { git = "https://github.com/ml-explore/mlx-lm", branch = "main" }
 # 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 }
@@ -125,6 +124,7 @@ extend-exclude = [
   "shared/protobufs/**",
   "*mlx_typings/**",
   "rust/exo_pyo3_bindings/**",
+  "bench/vendor/**",
 ]
 
 [tool.ruff.lint]

python/parts.nix

@@ -43,6 +43,27 @@
             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;
@@ -96,13 +117,16 @@
         "lib/python3.13/site-packages/nvidia*"
       ];
 
-      exoVenv = (pythonSet.mkVirtualEnv "exo-env" workspace.deps.default).overrideAttrs {
+      # 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 {
         venvIgnoreCollisions = venvCollisionPaths;
       };
 
       # Virtual environment with dev dependencies for testing
       testVenv = (pythonSet.mkVirtualEnv "exo-test-env" (
-        workspace.deps.default // {
+        exoDeps // {
           exo = [ "dev" ]; # Include pytest, pytest-asyncio, pytest-env
         }
       )).overrideAttrs {
@@ -158,6 +182,7 @@
           exo-test-env = testVenv;
         } // {
         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

@@ -1,6 +1,7 @@
 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

@@ -1,6 +1,7 @@
 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

@@ -1,6 +1,7 @@
 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

@@ -1,6 +1,7 @@
 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

@@ -1,6 +1,7 @@
 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

@@ -1,6 +1,7 @@
 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

@@ -1,6 +1,7 @@
 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

@@ -1,6 +1,7 @@
 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

@@ -1,6 +1,7 @@
 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

@@ -1,6 +1,7 @@
 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

@@ -1,6 +1,7 @@
 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

@@ -1,6 +1,7 @@
 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

@@ -1,6 +1,7 @@
 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

@@ -1,6 +1,7 @@
 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

@@ -1,6 +1,7 @@
 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

@@ -1,6 +1,7 @@
 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"

resources/inference_model_cards/mlx-community--Kimi-K2.5.toml

@@ -1,6 +1,7 @@
 model_id = "mlx-community/Kimi-K2.5"
 n_layers = 61
 hidden_size = 7168
+num_key_value_heads = 64
 supports_tensor = true
 tasks = ["TextGeneration"]
 family = "kimi"

resources/inference_model_cards/mlx-community--Llama-3.1-Nemotron-70B-Instruct-HF-4bit.toml

@@ -0,0 +1,12 @@
+model_id = "mlx-community/Llama-3.1-Nemotron-70B-Instruct-HF-4bit"
+n_layers = 80
+hidden_size = 8192
+supports_tensor = true
+tasks = ["TextGeneration"]
+family = "llama"
+quantization = "4bit"
+base_model = "NVIDIA Llama-3.1-Nemotron-70B-Instruct"
+capabilities = ["text"]
+
+[storage_size]
+in_bytes = 39688355840

resources/inference_model_cards/mlx-community--Llama-3.1-Nemotron-70B-Instruct-HF-8bit.toml

@@ -0,0 +1,12 @@
+model_id = "mlx-community/Llama-3.1-Nemotron-70B-Instruct-HF-8bit"
+n_layers = 80
+hidden_size = 8192
+supports_tensor = true
+tasks = ["TextGeneration"]
+family = "llama"
+quantization = "8bit"
+base_model = "NVIDIA Llama-3.1-Nemotron-70B-Instruct"
+capabilities = ["text"]
+
+[storage_size]
+in_bytes = 74964549632

resources/inference_model_cards/mlx-community--Llama-3.1-Nemotron-70B-Instruct-HF-bf16.toml

@@ -0,0 +1,12 @@
+model_id = "mlx-community/Llama-3.1-Nemotron-70B-Instruct-HF-bf16"
+n_layers = 80
+hidden_size = 8192
+supports_tensor = true
+tasks = ["TextGeneration"]
+family = "llama"
+quantization = "bf16"
+base_model = "NVIDIA Llama-3.1-Nemotron-70B-Instruct"
+capabilities = ["text"]
+
+[storage_size]
+in_bytes = 141107412992

resources/inference_model_cards/mlx-community--Llama-3.1-Nemotron-Nano-4B-v1.1-4bit.toml

@@ -0,0 +1,12 @@
+model_id = "mlx-community/Llama-3.1-Nemotron-Nano-4B-v1.1-4bit"
+n_layers = 32
+hidden_size = 3072
+supports_tensor = true
+tasks = ["TextGeneration"]
+family = "llama"
+quantization = "4bit"
+base_model = "NVIDIA Llama-3.1-Nemotron-Nano-4B-v1.1"
+capabilities = ["text"]
+
+[storage_size]
+in_bytes = 2538706944

resources/inference_model_cards/mlx-community--Llama-3.1-Nemotron-Nano-4B-v1.1-8bit.toml

@@ -0,0 +1,12 @@
+model_id = "mlx-community/Llama-3.1-Nemotron-Nano-4B-v1.1-8bit"
+n_layers = 32
+hidden_size = 3072
+supports_tensor = true
+tasks = ["TextGeneration"]
+family = "llama"
+quantization = "8bit"
+base_model = "NVIDIA Llama-3.1-Nemotron-Nano-4B-v1.1"
+capabilities = ["text"]
+
+[storage_size]
+in_bytes = 4794980352

resources/inference_model_cards/mlx-community--Llama-3.1-Nemotron-Nano-4B-v1.1-bf16.toml

@@ -0,0 +1,12 @@
+model_id = "mlx-community/Llama-3.1-Nemotron-Nano-4B-v1.1-bf16"
+n_layers = 32
+hidden_size = 3072
+supports_tensor = true
+tasks = ["TextGeneration"]
+family = "llama"
+quantization = "bf16"
+base_model = "NVIDIA Llama-3.1-Nemotron-Nano-4B-v1.1"
+capabilities = ["text"]
+
+[storage_size]
+in_bytes = 9025492992

resources/inference_model_cards/mlx-community--Llama-3.2-1B-Instruct-4bit.toml

@@ -1,6 +1,7 @@
 model_id = "mlx-community/Llama-3.2-1B-Instruct-4bit"
 n_layers = 16
 hidden_size = 2048
+num_key_value_heads = 8
 supports_tensor = true
 tasks = ["TextGeneration"]
 family = "llama"

resources/inference_model_cards/mlx-community--Llama-3.2-3B-Instruct-4bit.toml

@@ -1,6 +1,7 @@
 model_id = "mlx-community/Llama-3.2-3B-Instruct-4bit"
 n_layers = 28
 hidden_size = 3072
+num_key_value_heads = 8
 supports_tensor = true
 tasks = ["TextGeneration"]
 family = "llama"

resources/inference_model_cards/mlx-community--Llama-3.2-3B-Instruct-8bit.toml

@@ -1,6 +1,7 @@
 model_id = "mlx-community/Llama-3.2-3B-Instruct-8bit"
 n_layers = 28
 hidden_size = 3072
+num_key_value_heads = 8
 supports_tensor = true
 tasks = ["TextGeneration"]
 family = "llama"

resources/inference_model_cards/mlx-community--Llama-3.3-70B-Instruct-4bit.toml

@@ -1,6 +1,7 @@
 model_id = "mlx-community/Llama-3.3-70B-Instruct-4bit"
 n_layers = 80
 hidden_size = 8192
+num_key_value_heads = 8
 supports_tensor = true
 tasks = ["TextGeneration"]
 family = "llama"

resources/inference_model_cards/mlx-community--Llama-3.3-70B-Instruct-8bit.toml

@@ -1,6 +1,7 @@
 model_id = "mlx-community/Llama-3.3-70B-Instruct-8bit"
 n_layers = 80
 hidden_size = 8192
+num_key_value_heads = 8
 supports_tensor = true
 tasks = ["TextGeneration"]
 family = "llama"

resources/inference_model_cards/mlx-community--Meta-Llama-3.1-70B-Instruct-4bit.toml

@@ -1,6 +1,7 @@
 model_id = "mlx-community/Meta-Llama-3.1-70B-Instruct-4bit"
 n_layers = 80
 hidden_size = 8192
+num_key_value_heads = 8
 supports_tensor = true
 tasks = ["TextGeneration"]
 family = "llama"

resources/inference_model_cards/mlx-community--Meta-Llama-3.1-8B-Instruct-4bit.toml

@@ -1,6 +1,7 @@
 model_id = "mlx-community/Meta-Llama-3.1-8B-Instruct-4bit"
 n_layers = 32
 hidden_size = 4096
+num_key_value_heads = 8
 supports_tensor = true
 tasks = ["TextGeneration"]
 family = "llama"

resources/inference_model_cards/mlx-community--Meta-Llama-3.1-8B-Instruct-8bit.toml

@@ -1,6 +1,7 @@
 model_id = "mlx-community/Meta-Llama-3.1-8B-Instruct-8bit"
 n_layers = 32
 hidden_size = 4096
+num_key_value_heads = 8
 supports_tensor = true
 tasks = ["TextGeneration"]
 family = "llama"

resources/inference_model_cards/mlx-community--Meta-Llama-3.1-8B-Instruct-bf16.toml

@@ -1,6 +1,7 @@
 model_id = "mlx-community/Meta-Llama-3.1-8B-Instruct-bf16"
 n_layers = 32
 hidden_size = 4096
+num_key_value_heads = 8
 supports_tensor = true
 tasks = ["TextGeneration"]
 family = "llama"

resources/inference_model_cards/mlx-community--MiniMax-M2.1-3bit.toml

@@ -1,6 +1,7 @@
 model_id = "mlx-community/MiniMax-M2.1-3bit"
 n_layers = 61
 hidden_size = 3072
+num_key_value_heads = 8
 supports_tensor = true
 tasks = ["TextGeneration"]
 family = "minimax"

resources/inference_model_cards/mlx-community--MiniMax-M2.1-8bit.toml

@@ -1,6 +1,7 @@
 model_id = "mlx-community/MiniMax-M2.1-8bit"
 n_layers = 61
 hidden_size = 3072
+num_key_value_heads = 8
 supports_tensor = true
 tasks = ["TextGeneration"]
 family = "minimax"

resources/inference_model_cards/mlx-community--MiniMax-M2.5-4bit.toml

@@ -1,6 +1,7 @@
 model_id = "mlx-community/MiniMax-M2.5-4bit"
 n_layers = 62
 hidden_size = 3072
+num_key_value_heads = 8
 supports_tensor = true
 tasks = ["TextGeneration"]
 family = "minimax"

resources/inference_model_cards/mlx-community--MiniMax-M2.5-6bit.toml

@@ -1,6 +1,7 @@
 model_id = "mlx-community/MiniMax-M2.5-6bit"
 n_layers = 62
 hidden_size = 3072
+num_key_value_heads = 8
 supports_tensor = true
 tasks = ["TextGeneration"]
 family = "minimax"

resources/inference_model_cards/mlx-community--MiniMax-M2.5-8bit.toml

@@ -1,6 +1,7 @@
 model_id = "mlx-community/MiniMax-M2.5-8bit"
 n_layers = 62
 hidden_size = 3072
+num_key_value_heads = 8
 supports_tensor = true
 tasks = ["TextGeneration"]
 family = "minimax"

resources/inference_model_cards/mlx-community--NVIDIA-Nemotron-3-Nano-30B-A3B-MLX-4Bit.toml

@@ -0,0 +1,12 @@
+model_id = "mlx-community/NVIDIA-Nemotron-3-Nano-30B-A3B-MLX-4Bit"
+n_layers = 52
+hidden_size = 2688
+supports_tensor = true
+tasks = ["TextGeneration"]
+family = "nemotron"
+quantization = "4bit"
+base_model = "NVIDIA Nemotron-3-Nano-30B-A3B"
+capabilities = ["text"]
+
+[storage_size]
+in_bytes = 17775342336

resources/inference_model_cards/mlx-community--NVIDIA-Nemotron-3-Nano-30B-A3B-MLX-5Bit.toml

@@ -0,0 +1,12 @@
+model_id = "mlx-community/NVIDIA-Nemotron-3-Nano-30B-A3B-MLX-5Bit"
+n_layers = 52
+hidden_size = 2688
+supports_tensor = true
+tasks = ["TextGeneration"]
+family = "nemotron"
+quantization = "5bit"
+base_model = "NVIDIA Nemotron-3-Nano-30B-A3B"
+capabilities = ["text"]
+
+[storage_size]
+in_bytes = 21721476864

resources/inference_model_cards/mlx-community--NVIDIA-Nemotron-3-Nano-30B-A3B-MLX-6Bit.toml

@@ -0,0 +1,12 @@
+model_id = "mlx-community/NVIDIA-Nemotron-3-Nano-30B-A3B-MLX-6Bit"
+n_layers = 52
+hidden_size = 2688
+supports_tensor = true
+tasks = ["TextGeneration"]
+family = "nemotron"
+quantization = "6bit"
+base_model = "NVIDIA Nemotron-3-Nano-30B-A3B"
+capabilities = ["text"]
+
+[storage_size]
+in_bytes = 25667611392

resources/inference_model_cards/mlx-community--NVIDIA-Nemotron-3-Nano-30B-A3B-MLX-8Bit.toml

@@ -0,0 +1,12 @@
+model_id = "mlx-community/NVIDIA-Nemotron-3-Nano-30B-A3B-MLX-8Bit"
+n_layers = 52
+hidden_size = 2688
+supports_tensor = true
+tasks = ["TextGeneration"]
+family = "nemotron"
+quantization = "8bit"
+base_model = "NVIDIA Nemotron-3-Nano-30B-A3B"
+capabilities = ["text"]
+
+[storage_size]
+in_bytes = 33559880448

resources/inference_model_cards/mlx-community--NVIDIA-Nemotron-3-Nano-30B-A3B-MLX-BF16.toml

@@ -0,0 +1,12 @@
+model_id = "mlx-community/NVIDIA-Nemotron-3-Nano-30B-A3B-MLX-BF16"
+n_layers = 52
+hidden_size = 2688
+supports_tensor = true
+tasks = ["TextGeneration"]
+family = "nemotron"
+quantization = "bf16"
+base_model = "NVIDIA Nemotron-3-Nano-30B-A3B"
+capabilities = ["text"]
+
+[storage_size]
+in_bytes = 63155889408

resources/inference_model_cards/mlx-community--NVIDIA-Nemotron-3-Nano-30B-A3B-MLX-MXFP4.toml

@@ -0,0 +1,12 @@
+model_id = "mlx-community/NVIDIA-Nemotron-3-Nano-30B-A3B-MLX-MXFP4"
+n_layers = 52
+hidden_size = 2688
+supports_tensor = true
+tasks = ["TextGeneration"]
+family = "nemotron"
+quantization = "4bit"
+base_model = "NVIDIA Nemotron-3-Nano-30B-A3B"
+capabilities = ["text"]
+
+[storage_size]
+in_bytes = 16788808704

resources/inference_model_cards/mlx-community--NVIDIA-Nemotron-3-Nano-30B-A3B-NVFP4.toml

@@ -0,0 +1,12 @@
+model_id = "mlx-community/NVIDIA-Nemotron-3-Nano-30B-A3B-NVFP4"
+n_layers = 52
+hidden_size = 2688
+supports_tensor = true
+tasks = ["TextGeneration"]
+family = "nemotron"
+quantization = "4bit"
+base_model = "NVIDIA Nemotron-3-Nano-30B-A3B"
+capabilities = ["text"]
+
+[storage_size]
+in_bytes = 19323906944

resources/inference_model_cards/mlx-community--NVIDIA-Nemotron-Nano-9B-v2-4bits.toml

@@ -0,0 +1,12 @@
+model_id = "mlx-community/NVIDIA-Nemotron-Nano-9B-v2-4bits"
+n_layers = 56
+hidden_size = 4480
+supports_tensor = true
+tasks = ["TextGeneration"]
+family = "nemotron"
+quantization = "4bit"
+base_model = "NVIDIA Nemotron-Nano-9B-v2"
+capabilities = ["text"]
+
+[storage_size]
+in_bytes = 5002791936

resources/inference_model_cards/mlx-community--NVIDIA-Nemotron-Nano-9B-v2-6bit.toml

@@ -0,0 +1,12 @@
+model_id = "mlx-community/NVIDIA-Nemotron-Nano-9B-v2-6bit"
+n_layers = 56
+hidden_size = 4480
+supports_tensor = true
+tasks = ["TextGeneration"]
+family = "nemotron"
+quantization = "6bit"
+base_model = "NVIDIA Nemotron-Nano-9B-v2"
+capabilities = ["text"]
+
+[storage_size]
+in_bytes = 7224298496

resources/inference_model_cards/mlx-community--Qwen3-0.6B-4bit.toml

@@ -1,6 +1,7 @@
 model_id = "mlx-community/Qwen3-0.6B-4bit"
 n_layers = 28
 hidden_size = 1024
+num_key_value_heads = 8
 supports_tensor = false
 tasks = ["TextGeneration"]
 family = "qwen"

resources/inference_model_cards/mlx-community--Qwen3-0.6B-8bit.toml

@@ -1,6 +1,7 @@
 model_id = "mlx-community/Qwen3-0.6B-8bit"
 n_layers = 28
 hidden_size = 1024
+num_key_value_heads = 8
 supports_tensor = false
 tasks = ["TextGeneration"]
 family = "qwen"

resources/inference_model_cards/mlx-community--Qwen3-235B-A22B-Instruct-2507-4bit.toml

@@ -1,6 +1,7 @@
 model_id = "mlx-community/Qwen3-235B-A22B-Instruct-2507-4bit"
 n_layers = 94
 hidden_size = 4096
+num_key_value_heads = 4
 supports_tensor = true
 tasks = ["TextGeneration"]
 family = "qwen"

resources/inference_model_cards/mlx-community--Qwen3-235B-A22B-Instruct-2507-8bit.toml

@@ -1,6 +1,7 @@
 model_id = "mlx-community/Qwen3-235B-A22B-Instruct-2507-8bit"
 n_layers = 94
 hidden_size = 4096
+num_key_value_heads = 4
 supports_tensor = true
 tasks = ["TextGeneration"]
 family = "qwen"

resources/inference_model_cards/mlx-community--Qwen3-30B-A3B-4bit.toml

@@ -1,6 +1,7 @@
 model_id = "mlx-community/Qwen3-30B-A3B-4bit"
 n_layers = 48
 hidden_size = 2048
+num_key_value_heads = 4
 supports_tensor = true
 tasks = ["TextGeneration"]
 family = "qwen"

resources/inference_model_cards/mlx-community--Qwen3-30B-A3B-8bit.toml

@@ -1,6 +1,7 @@
 model_id = "mlx-community/Qwen3-30B-A3B-8bit"
 n_layers = 48
 hidden_size = 2048
+num_key_value_heads = 4
 supports_tensor = true
 tasks = ["TextGeneration"]
 family = "qwen"

resources/inference_model_cards/mlx-community--Qwen3-Coder-480B-A35B-Instruct-4bit.toml

@@ -1,6 +1,7 @@
 model_id = "mlx-community/Qwen3-Coder-480B-A35B-Instruct-4bit"
 n_layers = 62
 hidden_size = 6144
+num_key_value_heads = 8
 supports_tensor = true
 tasks = ["TextGeneration"]
 family = "qwen"

resources/inference_model_cards/mlx-community--Qwen3-Coder-480B-A35B-Instruct-8bit.toml

@@ -1,6 +1,7 @@
 model_id = "mlx-community/Qwen3-Coder-480B-A35B-Instruct-8bit"
 n_layers = 62
 hidden_size = 6144
+num_key_value_heads = 8
 supports_tensor = true
 tasks = ["TextGeneration"]
 family = "qwen"

resources/inference_model_cards/mlx-community--Qwen3-Coder-Next-4bit.toml

@@ -1,6 +1,7 @@
 model_id = "mlx-community/Qwen3-Coder-Next-4bit"
 n_layers = 48
 hidden_size = 2048
+num_key_value_heads = 2
 supports_tensor = true
 tasks = ["TextGeneration"]
 family = "qwen"

resources/inference_model_cards/mlx-community--Qwen3-Coder-Next-5bit.toml

@@ -1,6 +1,7 @@
 model_id = "mlx-community/Qwen3-Coder-Next-5bit"
 n_layers = 48
 hidden_size = 2048
+num_key_value_heads = 2
 supports_tensor = true
 tasks = ["TextGeneration"]
 family = "qwen"

resources/inference_model_cards/mlx-community--Qwen3-Coder-Next-6bit.toml

@@ -1,6 +1,7 @@
 model_id = "mlx-community/Qwen3-Coder-Next-6bit"
 n_layers = 48
 hidden_size = 2048
+num_key_value_heads = 2
 supports_tensor = true
 tasks = ["TextGeneration"]
 family = "qwen"

resources/inference_model_cards/mlx-community--Qwen3-Coder-Next-8bit.toml

@@ -1,6 +1,7 @@
 model_id = "mlx-community/Qwen3-Coder-Next-8bit"
 n_layers = 48
 hidden_size = 2048
+num_key_value_heads = 2
 supports_tensor = true
 tasks = ["TextGeneration"]
 family = "qwen"

resources/inference_model_cards/mlx-community--Qwen3-Coder-Next-bf16.toml

@@ -1,6 +1,7 @@
 model_id = "mlx-community/Qwen3-Coder-Next-bf16"
 n_layers = 48
 hidden_size = 2048
+num_key_value_heads = 2
 supports_tensor = true
 tasks = ["TextGeneration"]
 family = "qwen"

resources/inference_model_cards/mlx-community--Qwen3-Next-80B-A3B-Instruct-4bit.toml

@@ -1,6 +1,7 @@
 model_id = "mlx-community/Qwen3-Next-80B-A3B-Instruct-4bit"
 n_layers = 48
 hidden_size = 2048
+num_key_value_heads = 2
 supports_tensor = true
 tasks = ["TextGeneration"]
 family = "qwen"

resources/inference_model_cards/mlx-community--Qwen3-Next-80B-A3B-Instruct-8bit.toml

@@ -1,6 +1,7 @@
 model_id = "mlx-community/Qwen3-Next-80B-A3B-Instruct-8bit"
 n_layers = 48
 hidden_size = 2048
+num_key_value_heads = 2
 supports_tensor = true
 tasks = ["TextGeneration"]
 family = "qwen"

resources/inference_model_cards/mlx-community--Qwen3-Next-80B-A3B-Thinking-4bit.toml

@@ -1,6 +1,7 @@
 model_id = "mlx-community/Qwen3-Next-80B-A3B-Thinking-4bit"
 n_layers = 48
 hidden_size = 2048
+num_key_value_heads = 2
 supports_tensor = true
 tasks = ["TextGeneration"]
 family = "qwen"

resources/inference_model_cards/mlx-community--Qwen3-Next-80B-A3B-Thinking-8bit.toml

@@ -1,6 +1,7 @@
 model_id = "mlx-community/Qwen3-Next-80B-A3B-Thinking-8bit"
 n_layers = 48
 hidden_size = 2048
+num_key_value_heads = 2
 supports_tensor = true
 tasks = ["TextGeneration"]
 family = "qwen"

resources/inference_model_cards/mlx-community--Qwen3.5-122B-A10B-4bit.toml

@@ -1,6 +1,7 @@
 model_id = "mlx-community/Qwen3.5-122B-A10B-4bit"
 n_layers = 48
 hidden_size = 3072
+num_key_value_heads = 2
 supports_tensor = true
 tasks = ["TextGeneration"]
 family = "qwen"

resources/inference_model_cards/mlx-community--Qwen3.5-122B-A10B-6bit.toml

@@ -1,6 +1,7 @@
 model_id = "mlx-community/Qwen3.5-122B-A10B-6bit"
 n_layers = 48
 hidden_size = 3072
+num_key_value_heads = 2
 supports_tensor = true
 tasks = ["TextGeneration"]
 family = "qwen"