Instructions to use snsnc/Qwopus3.5-9B-Coder-MLX with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use snsnc/Qwopus3.5-9B-Coder-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("snsnc/Qwopus3.5-9B-Coder-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 snsnc/Qwopus3.5-9B-Coder-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 "snsnc/Qwopus3.5-9B-Coder-MLX"
Configure the model in Pi
# Install Pi: npm install -g @earendil-works/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "mlx-lm": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "snsnc/Qwopus3.5-9B-Coder-MLX" } ] } } }Run Pi
# Start Pi in your project directory: pi
- MLX LM
How to use snsnc/Qwopus3.5-9B-Coder-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 "snsnc/Qwopus3.5-9B-Coder-MLX"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "snsnc/Qwopus3.5-9B-Coder-MLX" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "snsnc/Qwopus3.5-9B-Coder-MLX", "messages": [ {"role": "user", "content": "Hello"} ] }' - Hermes Agent
How to use snsnc/Qwopus3.5-9B-Coder-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 "snsnc/Qwopus3.5-9B-Coder-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 snsnc/Qwopus3.5-9B-Coder-MLX
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use snsnc/Qwopus3.5-9B-Coder-MLX with OpenClaw:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "snsnc/Qwopus3.5-9B-Coder-MLX"
Configure OpenClaw
# Install OpenClaw: npm install -g openclaw@latest # Register the local server and set it as the default model: openclaw onboard --non-interactive --mode local \ --auth-choice custom-api-key \ --custom-base-url http://127.0.0.1:8080/v1 \ --custom-model-id "snsnc/Qwopus3.5-9B-Coder-MLX" \ --custom-provider-id mlx-lm \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
Update README
Browse files
README.md
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---
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language: en
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library_name: mlx
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pipeline_tag: text-generation
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tags:
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- mlx
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---
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```bash
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pip install mlx-lm
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```
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```python
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model, tokenizer = load("snsnc/Qwopus3.5-9B-Coder-MLX")
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prompt = "
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if tokenizer.chat_template is not None:
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messages = [{"role": "user", "content": prompt}]
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prompt = tokenizer.apply_chat_template(
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messages,
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)
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response = generate(model, tokenizer, prompt=prompt, verbose=True)
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```
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---
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language: en
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license: apache-2.0
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library_name: mlx
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pipeline_tag: text-generation
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base_model: Jackrong/Qwopus3.5-9B-Coder
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tags:
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- mlx
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- qwen3_5
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- code
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- agent
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- tool-use
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- text-generation
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---
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# Qwopus3.5-9B-Coder-MLX
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MLX conversion of [`Jackrong/Qwopus3.5-9B-Coder`](https://huggingface.co/Jackrong/Qwopus3.5-9B-Coder) for Apple Silicon.
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Original upstream weights, converted to MLX format only. No additional fine-tuning or architecture changes.
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## Use with mlx-lm
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```bash
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pip install -U mlx-lm
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```
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```python
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model, tokenizer = load("snsnc/Qwopus3.5-9B-Coder-MLX")
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prompt = "Write a short Python function that returns the factorial of a number."
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if tokenizer.chat_template is not None:
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messages = [{"role": "user", "content": prompt}]
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prompt = tokenizer.apply_chat_template(
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messages,
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add_generation_prompt=True,
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return_dict=False,
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)
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response = generate(model, tokenizer, prompt=prompt, verbose=True)
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print(response)
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```
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## Use with mlx-serve
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```bash
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pip install -U mlx-serve
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```
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```bash
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mlx-serve \
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--model hf://snsnc/Qwopus3.5-9B-Coder-MLX \
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--serve \
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--host 0.0.0.0 \
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--port 11234
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```
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## Upstream
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- Original model: [`Jackrong/Qwopus3.5-9B-Coder`](https://huggingface.co/Jackrong/Qwopus3.5-9B-Coder)
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