How to use from
Hermes Agent
Start the MLX server
# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "m-i/Qwen3.5-397B-A17B-Text-2.423bit"
Configure Hermes
# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default m-i/Qwen3.5-397B-A17B-Text-2.423bit
Run Hermes
hermes
Quick Links

m-i/Qwen3.5-397B-A17B-Text-2.423bit [text only]

This model m-i/Qwen3.5-397B-A17B-Text-2.423bit was converted to MLX format from Qwen/Qwen3.5-397B-A17B using mlx-lm version 0.31.0.

Parameters

Try lower temp than the one recommended for full precision. --temp 0.5 or lower.

quant predicate

def qwen397b_predicate(path: str, module, ):
    # MLP projection layers are typically largest and most robust to aggressive quantization
    if any(proj in path for proj in ["down_proj"]):
        return {"group_size": 64, "bits": 2, "mode": "affine"}
    if any(proj in path for proj in [ "up_proj", "gate_proj"]):
        return {"group_size": 128, "bits": 2, "mode": "affine"}

    if "lm_head" in path:
        return {"group_size": 128, "bits": 6, "mode": "affine"}

    if "embed_tokens" in path:
        return {"group_size": 128, "bits": 8, "mode": "affine"}

    # All other weights: attention projections, norms, etc.
    return {"group_size": 32, "bits": 5, "mode": "affine"}

Use with mlx

pip install mlx-lm
from mlx_lm import load, generate

model, tokenizer = load("m-i/Qwen3.5-397B-A17B-Text-2.423bit")

prompt = "hello"

if tokenizer.chat_template is not None:
    messages = [{"role": "user", "content": prompt}]
    prompt = tokenizer.apply_chat_template(
        messages, add_generation_prompt=True, return_dict=False,
    )

response = generate(model, tokenizer, prompt=prompt, verbose=True)
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