Instructions to use spicyneuron/Qwen3.5-397B-A17B-MLX-3.5bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use spicyneuron/Qwen3.5-397B-A17B-MLX-3.5bit 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-3.5bit")

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 spicyneuron/Qwen3.5-397B-A17B-MLX-3.5bit 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-3.5bit"

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-3.5bit"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use spicyneuron/Qwen3.5-397B-A17B-MLX-3.5bit 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-3.5bit"

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-3.5bit

Run Hermes

hermes

MLX LM

How to use spicyneuron/Qwen3.5-397B-A17B-MLX-3.5bit 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-3.5bit"

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-3.5bit"
# 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-3.5bit",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Qwen3.5-397B-A17B optimized for MLX!

Mixed-precision quantization balances throughput, accuracy, and memory.
Better quality than a 4-bit baseline but requires 20% less memory.
Fixed chat template allows more reliable prompt caching.
This version does NOT support vision (image input).

Also available as a smaller 129GB version: https://huggingface.co/spicyneuron/Qwen3.5-397B-A17B-MLX-2.6bit

Usage

# Start server at http://localhost:8080/v1/chat/completions
uvx --from mlx-lm mlx_lm.server \
  --host 127.0.0.1 \
  --port 8080 \
  --model spicyneuron/Qwen3.5-397B-A17B-MLX-3.5bit

Methodology

Quantized with a mlx-lm fork, 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.
More tolerant layers like MoE experts get lower precision.

Benchmarks

metric	lmstudio-community 4 bit	2.6 bit	3.5 bit
perplexity	3.919 ± 0.019	3.852 ± 0.018	3.919 ± 0.019
hellaswag	0.594 ± 0.022	0.598 ± 0.022	0.622 ± 0.022
piqa	0.798 ± 0.018	0.802 ± 0.018	0.804 ± 0.018
winogrande	0.744 ± 0.02	0.718 ± 0.02	0.746 ± 0.019
p1024/g512 prompt	490.702	489.545	479.453
p1024/g512 gen	39.192	38.398	35.547
p1024/g512 mem	225.095	131.523	179.842

Downloads last month: 143

Safetensors

Model size

396B params

Tensor type

BF16

U32

F32

MLX

Hardware compatibility

3-bit

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