Instructions to use m-i/Qwen3.5-397B-A17B-Text-2.423bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use m-i/Qwen3.5-397B-A17B-Text-2.423bit with MLX:
# Make sure mlx-lm is installed # pip install --upgrade mlx-lm # Generate text with mlx-lm from mlx_lm import load, generate model, tokenizer = load("m-i/Qwen3.5-397B-A17B-Text-2.423bit") prompt = "Write a story about Einstein" messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, add_generation_prompt=True ) text = generate(model, tokenizer, prompt=prompt, verbose=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- LM Studio
- Pi new
How to use m-i/Qwen3.5-397B-A17B-Text-2.423bit with Pi:
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 the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "mlx-lm": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "m-i/Qwen3.5-397B-A17B-Text-2.423bit" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use m-i/Qwen3.5-397B-A17B-Text-2.423bit with 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
- MLX LM
How to use m-i/Qwen3.5-397B-A17B-Text-2.423bit with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "m-i/Qwen3.5-397B-A17B-Text-2.423bit"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "m-i/Qwen3.5-397B-A17B-Text-2.423bit" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "m-i/Qwen3.5-397B-A17B-Text-2.423bit", "messages": [ {"role": "user", "content": "Hello"} ] }'
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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Model size
396B params
Tensor type
BF16
路
U32 路
F32 路
Hardware compatibility
Log In to add your hardware
4-bit
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Base model
Qwen/Qwen3.5-397B-A17B