Instructions to use spicyneuron/Qwen3.5-397B-A17B-MLX-2.6bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use spicyneuron/Qwen3.5-397B-A17B-MLX-2.6bit 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("spicyneuron/Qwen3.5-397B-A17B-MLX-2.6bit")

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

How to use spicyneuron/Qwen3.5-397B-A17B-MLX-2.6bit with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "spicyneuron/Qwen3.5-397B-A17B-MLX-2.6bit"

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": "spicyneuron/Qwen3.5-397B-A17B-MLX-2.6bit"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use spicyneuron/Qwen3.5-397B-A17B-MLX-2.6bit 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 "spicyneuron/Qwen3.5-397B-A17B-MLX-2.6bit"

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 spicyneuron/Qwen3.5-397B-A17B-MLX-2.6bit

Run Hermes

hermes

MLX LM

How to use spicyneuron/Qwen3.5-397B-A17B-MLX-2.6bit with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "spicyneuron/Qwen3.5-397B-A17B-MLX-2.6bit"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "spicyneuron/Qwen3.5-397B-A17B-MLX-2.6bit"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "spicyneuron/Qwen3.5-397B-A17B-MLX-2.6bit",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

spicyneuron commited on Apr 10

Commit

dc77505

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1 Parent(s): 6154b8c

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@@ -29,7 +29,7 @@ uvx --from mlx-lm mlx_lm.server \
 # Methodology
-Quantized using a custom script inspired by Unsloth/AesSedai/ubergarm style mixed-precision GGUFs.
 MLX quantization options differ than llama.cpp, but the principles are the same:
 - Sensitive layers like MoE routing, attention, and output embeddings get higher precision.

 # Methodology
+Quantized with a [mlx-lm fork](https://github.com/ml-explore/mlx-lm/pull/922), drawing inspiration from Unsloth/AesSedai/ubergarm style mixed-precision GGUFs.
 MLX quantization options differ than llama.cpp, but the principles are the same:
 - Sensitive layers like MoE routing, attention, and output embeddings get higher precision.