flush old addrs

Mb premature auto merge

.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/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/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):

Cargo.toml

@@ -1,6 +1,11 @@
 [workspace]
 resolver = "3"
-members = ["rust/networking", "rust/exo_pyo3_bindings", "rust/util"]
+members = [
+  "rust/networking",
+  "rust/exo_pyo3_bindings",
+  "rust/util",
+  "rust/babblerd",
+]
 
 [workspace.package]
 version = "0.0.1"
@@ -43,7 +48,6 @@ log = "0.4"
 
 # networking
 libp2p = "0.56"
-libp2p-tcp = "0.44"
 
 [workspace.lints.rust]
 static_mut_refs = "warn"      # Or use "warn" instead of deny

bench/exo_bench.py

@@ -496,20 +496,28 @@ def main() -> int:
                                 all_rows.append(row)
 
                             if batch_results:
-                                agg_gen_tps = sum(
+                                valid_gen_tps = [
                                     x["stats"]["generation_tps"]
                                     for x, _ in batch_results
                                     if x["stats"]["generation_tps"] > 0
+                                ]
+                                agg_gen_tps = (
+                                    mean(valid_gen_tps) if valid_gen_tps else 0.0
                                 )
+                                gen_tps = agg_gen_tps / concurrency
                                 logger.info(
                                     f"[concurrent {concurrency}x]  "
                                     f"agg_gen_tps={agg_gen_tps:.2f}  "
+                                    f"gen_tps={gen_tps:.2f}  "
                                     f"errors={batch_errors}"
                                 )
 
                     if runs:
                         prompt_tps = mean(x["stats"]["prompt_tps"] for x in runs)
-                        gen_tps = mean(x["stats"]["generation_tps"] for x in runs)
+                        gen_tps = mean(
+                            x["stats"]["generation_tps"] / x["concurrency"]
+                            for x in runs
+                        )
                         ptok = mean(x["stats"]["prompt_tokens"] for x in runs)
                         gtok = mean(x["stats"]["generation_tokens"] for x in runs)
                         peak = mean(

bench/exo_eval.py

@@ -85,9 +85,6 @@ _MC_PATTERNS: list[re.Pattern[str]] = [
 # Code extraction: last ```python ... ``` block (AA regex)
 _CODE_BLOCK_RE = re.compile(r"```(?:python|Python)?\s*\n(.*?)```", re.DOTALL)
 
-# LCB-compatible extraction mode (--lcb-compat): use line-based extraction
-# matching official LiveCodeBench extract_code() behavior.
-
 
 # ---------------------------------------------------------------------------
 # Model config loading
@@ -151,22 +148,12 @@ def extract_boxed_answer(text: str) -> str | None:
     return matches[-1].strip() if matches else None
 
 
-def extract_code_block(text: str, preserve_indent: bool = False, lcb_compat: bool = False) -> str | None:
+def extract_code_block(text: str, preserve_indent: bool = False) -> str | None:
     """Extract the last Python code block from markdown response.
 
     If preserve_indent is True, only strip trailing whitespace (keeps leading
     indentation intact — needed for HumanEval function-body completions).
-
-    If lcb_compat is True, use the official LiveCodeBench extract_code() logic:
-    line-based search for ```, extract between last two backtick lines.
     """
-    if lcb_compat:
-        lines = text.split("\n")
-        backtick_lines = [i for i, line in enumerate(lines) if "```" in line]
-        if len(backtick_lines) < 2:
-            return ""
-        return "\n".join(lines[backtick_lines[-2] + 1 : backtick_lines[-1]])
-
     matches = _CODE_BLOCK_RE.findall(text)
     if matches:
         raw = matches[-1]
@@ -397,7 +384,7 @@ _LCB_WITH_STARTER = (
     "### Format: You will use the following starter code to write the "
     "solution to the problem and enclose your code within delimiters.\n"
     "```python\n{starter_code}\n```\n\n"
-    "### Answer: (use the provided format with backticks)\n\n"
+    "### Answer: (use the provided format with backticks)\n"
 )
 
 _LCB_WITHOUT_STARTER = (
@@ -408,7 +395,7 @@ _LCB_WITHOUT_STARTER = (
     "python program runs, it reads the inputs, runs the algorithm and "
     "writes output to STDOUT.\n"
     "```python\n# YOUR CODE HERE\n```\n\n"
-    "### Answer: (use the provided format with backticks)\n\n"
+    "### Answer: (use the provided format with backticks)\n"
 )
 
 
@@ -542,11 +529,6 @@ async def _call_api(
     system_message: str | None = None,
     reasoning_effort: str | None = None,
     top_p: float | None = None,
-    enable_thinking: bool | None = None,
-    top_k: int | None = None,
-    min_p: float | None = None,
-    repetition_penalty: float | None = None,
-    repetition_context_size: int | None = None,
 ) -> ApiResult:
     messages = []
     if system_message:
@@ -563,15 +545,6 @@ async def _call_api(
         body["reasoning_effort"] = reasoning_effort
     if top_p is not None:
         body["top_p"] = top_p
-    if enable_thinking is not None:
-        body["enable_thinking"] = enable_thinking
-    if top_k is not None:
-        body["top_k"] = top_k
-    if min_p is not None:
-        body["min_p"] = min_p
-    if repetition_penalty is not None:
-        body["repetition_penalty"] = repetition_penalty
-        body["repetition_context_size"] = repetition_context_size or 64
 
     resp = await client.post(
         f"{base_url}/v1/chat/completions",
@@ -604,11 +577,6 @@ async def call_with_retries(
     system_message: str | None = None,
     reasoning_effort: str | None = None,
     top_p: float | None = None,
-    enable_thinking: bool | None = None,
-    top_k: int | None = None,
-    min_p: float | None = None,
-    repetition_penalty: float | None = None,
-    repetition_context_size: int | None = None,
 ) -> ApiResult | None:
     for attempt in range(MAX_RETRIES):
         try:
@@ -623,11 +591,6 @@ async def call_with_retries(
                 system_message,
                 reasoning_effort,
                 top_p,
-                enable_thinking,
-                top_k,
-                min_p,
-                repetition_penalty,
-                repetition_context_size,
             )
         except Exception as e:
             if attempt < MAX_RETRIES - 1:
@@ -658,14 +621,6 @@ async def evaluate_benchmark(
     reasoning_effort: str | None = None,
     top_p: float | None = None,
     difficulty: str | None = None,
-    start_index: int | None = None,
-    end_index: int | None = None,
-    lcb_compat: bool = False,
-    enable_thinking: bool | None = None,
-    top_k: int | None = None,
-    min_p: float | None = None,
-    repetition_penalty: float | None = None,
-    repetition_context_size: int | None = None,
 ) -> list[QuestionResult]:
     """Run a benchmark. Returns per-question results."""
     import datasets
@@ -680,8 +635,6 @@ async def evaluate_benchmark(
                 data_files="hf://datasets/livecodebench/code_generation_lite/*.jsonl",
                 split="train",
             )
-            # Sort by question_id to match official LCB runner ordering
-            ds = ds.sort("question_id")
         else:
             ds = datasets.load_dataset(
                 config.dataset_name,
@@ -698,12 +651,6 @@ async def evaluate_benchmark(
         ds = ds.filter(lambda x: x["difficulty"] == difficulty)
         logger.info(f"Filtered to {len(ds)} {difficulty} problems")
 
-    if start_index is not None or end_index is not None:
-        si = start_index or 0
-        ei = end_index or len(ds)
-        ds = ds.select(range(si, min(ei, len(ds))))
-        logger.info(f"Sliced to [{si}:{ei}] → {len(ds)} problems")
-
     total = len(ds)
     if limit and limit < total:
         ds = ds.select(range(limit))
@@ -761,11 +708,6 @@ async def evaluate_benchmark(
                 system_message=system_msg,
                 reasoning_effort=reasoning_effort,
                 top_p=top_p,
-                enable_thinking=enable_thinking,
-                top_k=top_k,
-                min_p=min_p,
-                repetition_penalty=repetition_penalty,
-                repetition_context_size=repetition_context_size,
             )
             elapsed = time.monotonic() - t0
 
@@ -817,9 +759,8 @@ async def evaluate_benchmark(
             elif config.kind == "code":
                 # HumanEval needs preserved indentation (function body completion)
                 keep_indent = benchmark_name == "humaneval"
-                code = extract_code_block(response, preserve_indent=keep_indent,
-                                          lcb_compat=(lcb_compat and benchmark_name == "livecodebench"))
-                if not code:
+                code = extract_code_block(response, preserve_indent=keep_indent)
+                if code is None:
                     result = QuestionResult(
                         question_id=idx,
                         prompt=prompt,
@@ -1152,18 +1093,6 @@ def main() -> int:
         default=None,
         help="Max questions per benchmark (for fast iteration).",
     )
-    ap.add_argument(
-        "--start-index",
-        type=int,
-        default=None,
-        help="Start index for problem range (inclusive). Applied after difficulty filter.",
-    )
-    ap.add_argument(
-        "--end-index",
-        type=int,
-        default=None,
-        help="End index for problem range (exclusive). Applied after difficulty filter.",
-    )
 
     reasoning_group = ap.add_mutually_exclusive_group()
     reasoning_group.add_argument(
@@ -1226,26 +1155,6 @@ def main() -> int:
         action="store_true",
         help="Skip exo instance management (assumes model is already running).",
     )
-    ap.add_argument(
-        "--lcb-compat",
-        action="store_true",
-        help="Use LiveCodeBench-compatible code extraction and prompt format.",
-    )
-    ap.add_argument(
-        "--enable-thinking",
-        action="store_true",
-        default=False,
-        help="Send enable_thinking=true in API request (for Qwen/DeepSeek thinking mode).",
-    )
-    ap.add_argument("--top-k", type=int, default=None, help="Override top_k sampling.")
-    ap.add_argument("--min-p", type=float, default=None, help="Override min_p sampling.")
-    ap.add_argument(
-        "--repetition-penalty", type=float, default=None, help="Override repetition_penalty."
-    )
-    ap.add_argument(
-        "--repetition-context-size", type=int, default=None,
-        help="Context window for repetition penalty (default: 64 when repetition_penalty is set).",
-    )
 
     args, _ = ap.parse_known_args()
 
@@ -1377,29 +1286,12 @@ def main() -> int:
         reasoning_effort = str(cfg["reasoning_effort"])
     else:
         reasoning_effort = "high" if is_reasoning else None
-
-    enable_thinking = True if args.enable_thinking else None
-
-    top_k: int | None = args.top_k
-    min_p: float | None = args.min_p
-    repetition_penalty: float | None = args.repetition_penalty
-    repetition_context_size: int | None = args.repetition_context_size
-
     base_url = f"http://{args.host}:{args.port}"
 
     logger.info(f"Model: {full_model_id}")
-    extra_params = ""
-    if top_p is not None:
-        extra_params += f"top_p={top_p}, "
-    if top_k is not None:
-        extra_params += f"top_k={top_k}, "
-    if min_p is not None:
-        extra_params += f"min_p={min_p}, "
-    if repetition_penalty is not None:
-        extra_params += f"repetition_penalty={repetition_penalty}, "
     logger.info(
         f"Settings: temperature={temperature}, max_tokens={max_tokens}, "
-        + extra_params
+        + (f"top_p={top_p}, " if top_p is not None else "")
         + f"reasoning={'yes' if is_reasoning else 'no'}"
         + (f", reasoning_effort={reasoning_effort}" if reasoning_effort else "")
     )
@@ -1425,14 +1317,6 @@ def main() -> int:
                             reasoning_effort=reasoning_effort,
                             top_p=top_p,
                             difficulty=args.difficulty,
-                            start_index=args.start_index,
-                            end_index=args.end_index,
-                            lcb_compat=args.lcb_compat,
-                            enable_thinking=enable_thinking,
-                            top_k=top_k,
-                            min_p=min_p,
-                            repetition_penalty=repetition_penalty,
-                            repetition_context_size=repetition_context_size,
                         )
                     )
                     if results:
@@ -1463,14 +1347,6 @@ def main() -> int:
                         reasoning_effort=reasoning_effort,
                         top_p=top_p,
                         difficulty=args.difficulty,
-                        start_index=args.start_index,
-                        end_index=args.end_index,
-                        lcb_compat=args.lcb_compat,
-                        enable_thinking=enable_thinking,
-                        top_k=top_k,
-                        min_p=min_p,
-                        repetition_penalty=repetition_penalty,
-                        repetition_context_size=repetition_context_size,
                     )
                 )
                 if results:

bench/test_mlx_bandwidth.py

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

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/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/stores/app.svelte.ts

@@ -1577,6 +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":

dashboard/src/routes/+page.svelte

@@ -42,6 +42,9 @@
     setSelectedChatModel,
     selectedChatModel,
     sendMessage,
+    generateImage,
+    editImage,
+    editingImage,
     messages,
     debugMode,
     toggleDebugMode,
@@ -834,6 +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";
 
@@ -1527,7 +1576,11 @@
           downloadKind
         ] as Record<string, unknown>;
 
-        if (downloadKind !== "DownloadOngoing") continue;
+        if (
+          downloadKind !== "DownloadOngoing" &&
+          downloadKind !== "DownloadPending"
+        )
+          continue;
         if (!downloadPayload) continue;
 
         const downloadModelId = extractModelIdFromDownload(downloadPayload);
@@ -1542,9 +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;
@@ -1696,7 +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
@@ -1706,9 +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;
@@ -2786,7 +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;
       }
     }
@@ -2803,7 +2917,7 @@
     if (hasRunningInstance(autoModel.id)) {
       setSelectedChatModel(autoModel.id);
       if (!chatStarted) createConversation();
-      sendMessage(content, files);
+      routeMessage(content, files);
       return;
     }
 
@@ -2956,7 +3070,7 @@
       if (pendingAutoMessage) {
         const msg = pendingAutoMessage;
         pendingAutoMessage = null;
-        sendMessage(msg.content, msg.files);
+        routeMessage(msg.content, msg.files);
       }
       return;
     }
@@ -3035,7 +3149,7 @@
     // Model is selected and running — send directly
     if (model && hasRunningInstance(model)) {
       chatLaunchState = "ready";
-      sendMessage(content, files, null);
+      routeMessage(content, files);
       return;
     }
 

flake.nix

@@ -112,17 +112,20 @@
             };
           };
 
-          packages = lib.optionalAttrs pkgs.stdenv.hostPlatform.isDarwin (
+          packages = {
+            babeld = pkgs.callPackage ./nix/babeld.nix { };
+          } // lib.optionalAttrs pkgs.stdenv.hostPlatform.isDarwin (
             let
               uvLock = builtins.fromTOML (builtins.readFile ./uv.lock);
               mlxPackage = builtins.head (builtins.filter (p: p.name == "mlx" && p.source ? git) uvLock.package);
               uvLockMlxVersion = mlxPackage.version;
+              uvLockMlxRev = builtins.elemAt (builtins.split "#" mlxPackage.source.git) 2;
             in
             {
               metal-toolchain = pkgs.callPackage ./nix/metal-toolchain.nix { };
               mlx = pkgs.callPackage ./nix/mlx.nix {
                 inherit (self'.packages) metal-toolchain;
-                inherit uvLockMlxVersion;
+                inherit uvLockMlxVersion uvLockMlxRev;
               };
               default = self'.packages.exo;
             }
@@ -156,9 +159,6 @@
                 just
                 jq
               ]
-              ++ lib.optionals stdenv.isLinux [
-                unixtools.ifconfig
-              ]
               ++ lib.optionals stdenv.isDarwin [
                 macmon
               ];

nix/babeld.nix

@@ -0,0 +1,25 @@
+{ stdenv
+, lib
+, fetchurl
+}:
+stdenv.mkDerivation rec {
+  pname = "babeld";
+  version = "1.13.1";
+
+  src = fetchurl {
+    url = "https://www.irif.fr/~jch/software/files/${pname}-${version}.tar.gz";
+    hash = "sha256-FfJNJtoMz8Bzq83vAwnygeRoTyqnESb4JlcsTIRejdk=";
+  };
+
+  outputs = [
+    "out"
+    "man"
+  ];
+
+  makeFlags = [
+    "PREFIX=${placeholder "out"}"
+    "ETCDIR=${placeholder "out"}/etc"
+  ]
+  ++ lib.optional stdenv.isDarwin "LDLIBS=''";
+}
+

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

@@ -62,7 +62,7 @@ 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/rltakashige/mlx-lm", branch = "fix/float32-logprobs" }
+mlx-lm = { git = "https://github.com/rltakashige/mlx-lm", branch = "leo/eval-left-padding-in-batched-rotation" }
 # 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 }

python/parts.nix

@@ -185,6 +185,7 @@
         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);
+        exo-mlx-bandwidth-test = mkPythonScript "exo-mlx-bandwidth-test" (inputs.self + /bench/test_mlx_bandwidth.py);
       };
 
       checks = {

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"

resources/inference_model_cards/mlx-community--Qwen3.5-122B-A10B-8bit.toml

@@ -1,6 +1,7 @@
 model_id = "mlx-community/Qwen3.5-122B-A10B-8bit"
 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-bf16.toml

@@ -1,6 +1,7 @@
 model_id = "mlx-community/Qwen3.5-122B-A10B-bf16"
 n_layers = 48
 hidden_size = 3072
+num_key_value_heads = 2
 supports_tensor = true
 tasks = ["TextGeneration"]
 family = "qwen"

resources/inference_model_cards/mlx-community--Qwen3.5-27B-4bit.toml

@@ -1,6 +1,7 @@
 model_id = "mlx-community/Qwen3.5-27B-4bit"
 n_layers = 64
 hidden_size = 5120
+num_key_value_heads = 4
 supports_tensor = true
 tasks = ["TextGeneration"]
 family = "qwen"

resources/inference_model_cards/mlx-community--Qwen3.5-27B-8bit.toml

@@ -1,6 +1,7 @@
 model_id = "mlx-community/Qwen3.5-27B-8bit"
 n_layers = 64
 hidden_size = 5120
+num_key_value_heads = 4
 supports_tensor = true
 tasks = ["TextGeneration"]
 family = "qwen"

resources/inference_model_cards/mlx-community--Qwen3.5-2B-MLX-8bit.toml

@@ -1,6 +1,7 @@
 model_id = "mlx-community/Qwen3.5-2B-MLX-8bit"
 n_layers = 24
 hidden_size = 2048
+num_key_value_heads = 2
 supports_tensor = true
 tasks = ["TextGeneration"]
 family = "qwen"

resources/inference_model_cards/mlx-community--Qwen3.5-35B-A3B-4bit.toml

@@ -1,6 +1,7 @@
 model_id = "mlx-community/Qwen3.5-35B-A3B-4bit"
 n_layers = 40
 hidden_size = 2048
+num_key_value_heads = 2
 supports_tensor = true
 tasks = ["TextGeneration"]
 family = "qwen"

resources/inference_model_cards/mlx-community--Qwen3.5-35B-A3B-8bit.toml

@@ -1,6 +1,7 @@
 model_id = "mlx-community/Qwen3.5-35B-A3B-8bit"
 n_layers = 40
 hidden_size = 2048
+num_key_value_heads = 2
 supports_tensor = true
 tasks = ["TextGeneration"]
 family = "qwen"

resources/inference_model_cards/mlx-community--Qwen3.5-397B-A17B-4bit.toml

@@ -1,6 +1,7 @@
 model_id = "mlx-community/Qwen3.5-397B-A17B-4bit"
 n_layers = 60
 hidden_size = 4096
+num_key_value_heads = 2
 supports_tensor = true
 tasks = ["TextGeneration"]
 family = "qwen"

resources/inference_model_cards/mlx-community--Qwen3.5-397B-A17B-6bit.toml

@@ -1,6 +1,7 @@
 model_id = "mlx-community/Qwen3.5-397B-A17B-6bit"
 n_layers = 60
 hidden_size = 4096
+num_key_value_heads = 2
 supports_tensor = true
 tasks = ["TextGeneration"]
 family = "qwen"

resources/inference_model_cards/mlx-community--Qwen3.5-397B-A17B-8bit.toml

@@ -1,6 +1,7 @@
 model_id = "mlx-community/Qwen3.5-397B-A17B-8bit"
 n_layers = 60
 hidden_size = 4096
+num_key_value_heads = 2
 supports_tensor = true
 tasks = ["TextGeneration"]
 family = "qwen"

resources/inference_model_cards/mlx-community--Qwen3.5-9B-4bit.toml

@@ -1,6 +1,7 @@
 model_id = "mlx-community/Qwen3.5-9B-4bit"
 n_layers = 32
 hidden_size = 4096
+num_key_value_heads = 4
 supports_tensor = true
 tasks = ["TextGeneration"]
 family = "qwen"

resources/inference_model_cards/mlx-community--Qwen3.5-9B-8bit.toml

@@ -1,6 +1,7 @@
 model_id = "mlx-community/Qwen3.5-9B-8bit"
 n_layers = 32
 hidden_size = 4096
+num_key_value_heads = 4
 supports_tensor = true
 tasks = ["TextGeneration"]
 family = "qwen"

resources/inference_model_cards/mlx-community--Step-3.5-Flash-4bit.toml

@@ -1,6 +1,7 @@
 model_id = "mlx-community/Step-3.5-Flash-4bit"
 n_layers = 45
 hidden_size = 4096
+num_key_value_heads = 8
 supports_tensor = true
 tasks = ["TextGeneration"]
 family = "step"

resources/inference_model_cards/mlx-community--Step-3.5-Flash-6bit.toml

@@ -1,6 +1,7 @@
 model_id = "mlx-community/Step-3.5-Flash-6bit"
 n_layers = 45
 hidden_size = 4096
+num_key_value_heads = 8
 supports_tensor = true
 tasks = ["TextGeneration"]
 family = "step"