Instructions to use mlx-community/Qwen3-Coder-480B-A35B-Instruct-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mlx-community/Qwen3-Coder-480B-A35B-Instruct-4bit 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("mlx-community/Qwen3-Coder-480B-A35B-Instruct-4bit")

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 mlx-community/Qwen3-Coder-480B-A35B-Instruct-4bit with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "mlx-community/Qwen3-Coder-480B-A35B-Instruct-4bit"

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": "mlx-community/Qwen3-Coder-480B-A35B-Instruct-4bit"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use mlx-community/Qwen3-Coder-480B-A35B-Instruct-4bit 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 "mlx-community/Qwen3-Coder-480B-A35B-Instruct-4bit"

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 mlx-community/Qwen3-Coder-480B-A35B-Instruct-4bit

Run Hermes

hermes

MLX LM

How to use mlx-community/Qwen3-Coder-480B-A35B-Instruct-4bit with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "mlx-community/Qwen3-Coder-480B-A35B-Instruct-4bit"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "mlx-community/Qwen3-Coder-480B-A35B-Instruct-4bit"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "mlx-community/Qwen3-Coder-480B-A35B-Instruct-4bit",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Anyone get this working with a server on MacOS yet?

by pbrass - opened Jul 23, 2025

Discussion

pbrass

MLX Community org Jul 23, 2025

I tried running it with lmstudio and it errors out on the "safe" stringfilter in the prompt template. If I remove that, it errors out on some other XML thing. I tried running it with mlx_lm.server but it just 404s every request. If anyone has it working please let me know.

Thanks!

tsureshkumar

Jul 24, 2025

noe. neither me. stops here

Fetching 73 files: 100%|████████████████████████████████████████████████████████| 73/73 [00:00<00:00, 11559.79it/s]
/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/multiprocessing/resource_tracker.py:254: UserWarning: resource_tracker: There appear to be 1 leaked semaphore objects to clean up at shutdown
warnings.warn('resource_tracker: There appear to be %d '

version tried:

Name Version Build Channel

mlx 0.26.5 py311hef3d267_0 conda-forge
mlx-lm 0.26.0 pyhd8ed1ab_0 conda-forge
mlx-metal 0.26.5 pypi_0 pypi
mlx-vlm 0.1.22 pypi_0 pypi

zhibo7

MLX Community org Jul 26, 2025

I'm day-dreaming about the model Qwen3-coder-30B-A3B-4bit-DWQ so I can run it locally :-)

yaddatrance

Sep 7, 2025

Running on an m3 Ultra 512GB give me:

mlx_lm.generate --model mlx-community/Qwen3-Coder-480B-A35B-Instruct-4bit
Fetching 73 files: 100%|███████████████████████████████████████████████████████████████████████████████████████████| 73/73 [00:00<00:00, 331727.19it/s]

Hello! How can I help you today?

Prompt: 9 tokens, 40.850 tokens-per-sec
Generation: 10 tokens, 29.229 tokens-per-sec
Peak memory: 270.170 GB

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