m-i/Qwen3.5-397B-A17B-2.592bit

This model was converted to MLX format from Qwen/Qwen3.5-397B-A17B using mlx-vlm version 0.4.4. Refer to the original model card for more details on the model.

Use with mlx

pip install -U mlx-vlm

On Macs with 128GiB of RAM - https://github.com/ml-explore/mlx-lm#large-models :

sudo sysctl iogpu.wired_limit_mb=130000

Also, mlx-vlm supports TurboQuant

# Server with TurboQuant
mlx_vlm server \
  --model  m-i/Qwen3.5-397B-A17B-2.592bit\
  --kv-bits 3.5 \
  --kv-quant-scheme turboquant

python -m mlx_vlm.generate --model m-i/Qwen3.5-397B-A17B-2.592bit --max-tokens 100 --temperature 0.0 --prompt "Describe this image." --image <path_to_image>

Quantization Predicate

Main difference compared to m-i/Qwen3.5-397B-A17B-2.416bit is here the vision is untouched. Some parts have a slightly better quant param.

import mlx.core as mx
mx.set_default_device(mx.cpu)
from mlx_vlm import convert

def qwen397b_predicate(path: str, module):
    """
    Multi-bit quantization predicate for Qwen/Qwen3.5-397B-A17B.

    Maps GGUF _exps patterns (MoE experts) to HF switch_mlp paths.
    """
    # ── SKIP: Critical for numerical stability ─────────────────────────────
    if any(kw in path for kw in [
        "layernorm", "mlp.gate.", "shared_expert_gate.",
        "A_log", "dt_bias", "conv1d", ".bias", ".scales",
        "vision",
    ]):
        return False

    # ── 2-bit: MoE routed experts (switch_mlp) ← THIS HANDLES _exps ───────
    # GGUF: ffn_{gate,up,down}_exps.weight → HF: switch_mlp.{gate,up,down}_proj.weight
    if "switch_mlp" in path:
        if any(proj in path for proj in ["gate_proj", "up_proj"]):
            return {"group_size": 128, "bits": 2, "mode": "affine"}  # IQ2_XXS equivalent
        if "down_proj" in path:
            # Optional: use 3-bit for down_proj to mirror IQ2_S > IQ2_XXS
            return {"group_size": 32, "bits": 2, "mode": "affine"}

    # ── 4-bit: Token embeddings ────────────────────────────────────────────
    if "embed_tokens" in path:
        return {"group_size": 32, "bits": 4, "mode": "affine"}

    # ── 5-bit: Shared expert gate/up ───────────────────────────────────────
    if "shared_expert" in path and any(p in path for p in ["gate_proj", "up_proj"]):
        return {"group_size": 128, "bits": 5, "mode": "affine"}

    # ── 6-bit: Shared expert down ──────────────────────────────────────────
    if "shared_expert" in path and "down_proj" in path:
        return {"group_size": 32, "bits": 6, "mode": "affine"}

    # ── 5-bit: Linear/full attention projections ───────────────────────────
    if "linear_attn" in path and any(p in path for p in ["in_proj", "out_proj"]):
        return {"group_size": 128, "bits": 5, "mode": "affine"}
    if "self_attn" in path and any(p in path for p in ["q_proj", "k_proj", "v_proj", "o_proj"]):
        return {"group_size": 128, "bits": 5, "mode": "affine"}

    # ── 6-8-bit: SSM dynamics, lm_head & fallback ─────────────────────────────────────
    if any(kw in path for kw in ["in_proj_a", "in_proj_b", "ssm_alpha", "ssm_beta"]):
        return {"group_size": 32, "bits": 6, "mode": "affine"}
    if "lm_head" in path:
        return {"group_size": 128, "bits": 8, "mode": "affine"}

    return {"group_size": 128, "bits": 6, "mode": "affine"}

repo = "Qwen/Qwen3.5-397B-A17B"
upload_repo = "m-i/Qwen3.5-397B-A17B-2.592bit"

convert(repo, quantize=True, upload_repo=upload_repo, quant_predicate=qwen397b_predicate, )