Instructions to use jedisct1/Qwopus3.6-27B-Coder-4bit.mlx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use jedisct1/Qwopus3.6-27B-Coder-4bit.mlx 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("jedisct1/Qwopus3.6-27B-Coder-4bit.mlx") 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 Settings
- LM Studio
- Pi
How to use jedisct1/Qwopus3.6-27B-Coder-4bit.mlx with Pi:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "jedisct1/Qwopus3.6-27B-Coder-4bit.mlx"
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": "jedisct1/Qwopus3.6-27B-Coder-4bit.mlx" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use jedisct1/Qwopus3.6-27B-Coder-4bit.mlx 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 "jedisct1/Qwopus3.6-27B-Coder-4bit.mlx"
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 jedisct1/Qwopus3.6-27B-Coder-4bit.mlx
Run Hermes
hermes
- MLX LM
How to use jedisct1/Qwopus3.6-27B-Coder-4bit.mlx with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "jedisct1/Qwopus3.6-27B-Coder-4bit.mlx"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "jedisct1/Qwopus3.6-27B-Coder-4bit.mlx" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "jedisct1/Qwopus3.6-27B-Coder-4bit.mlx", "messages": [ {"role": "user", "content": "Hello"} ] }'
Qwopus3.6-27B-Coder 4-bit MLX
This is an oMLX OptiQ 4-bit package of Jackrong/Qwopus3.6-27B-Coder, built from source revision f22041be82998a006c9fde642c7900fb42aff94e.
This variant does not include the source model's MTP tensors. The MTP config fields are normalized to zero so it loads as the plain text-generation model.
The tokenizer includes the qwen3_coder tool parser setting and a patched chat template for OpenAI-style tool calls. The template was adjusted for Swival-style coding-agent work, including array arguments for command tools and read-before-delete/edit behavior.
Local Validation
Validated on June 12, 2026 with oMLX:
- Static artifact check passed: oQ4 quantization, no MTP tensors, matching
chat_template.jinjaand embeddedtokenizer_config.jsontemplate. - Direct OpenAI-compatible tool-call smoke passed for scalar string arguments and argv-array arguments.
- Swival tool-calling gate passed 5/5 cases with all tools enabled: edit by line number, read/edit/test, batch read/write, read/delete, and argv-form command execution.
Use it through oMLX with model id:
Qwopus3.6-27B-Coder-4bit.mlx
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