Instructions to use zecanard/Gemopus-4-31B-it-MLX-3bit-mixed_3_6 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use zecanard/Gemopus-4-31B-it-MLX-3bit-mixed_3_6 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("zecanard/Gemopus-4-31B-it-MLX-3bit-mixed_3_6") 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 zecanard/Gemopus-4-31B-it-MLX-3bit-mixed_3_6 with Pi:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "zecanard/Gemopus-4-31B-it-MLX-3bit-mixed_3_6"
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": "zecanard/Gemopus-4-31B-it-MLX-3bit-mixed_3_6" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use zecanard/Gemopus-4-31B-it-MLX-3bit-mixed_3_6 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 "zecanard/Gemopus-4-31B-it-MLX-3bit-mixed_3_6"
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 zecanard/Gemopus-4-31B-it-MLX-3bit-mixed_3_6
Run Hermes
hermes
- MLX LM
How to use zecanard/Gemopus-4-31B-it-MLX-3bit-mixed_3_6 with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "zecanard/Gemopus-4-31B-it-MLX-3bit-mixed_3_6"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "zecanard/Gemopus-4-31B-it-MLX-3bit-mixed_3_6" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "zecanard/Gemopus-4-31B-it-MLX-3bit-mixed_3_6", "messages": [ {"role": "user", "content": "Hello"} ] }'
🦆 zecanard/Gemopus-4-31B-it-MLX-3bit-mixed_3_6
This model was converted to MLX from Jackrong/Gemopus-4-31B-it using mlx-vlm version 0.6.3.
Please refer to the original model card for more details.
🌟 Quality
Mixed-precision quantized language model with an effective 4.256 bits per weight. Combines the size and speed benefits of a 3-bit quant with higher precision where it matters most.
mlx_vlm.convert --quantize --q-group-size 32 --quant-predicate mixed_3_6
🛠️ Customizations
This quant includes a bugfix for tools calling. It is aware of the current date, and also enables thinking (if available). You may disable this behavior by deleting the following line from the chat template, or changing true to false:
{%- set enable_thinking = true %}
You may need to adjust your environment’s Reasoning Section Parsing to recognize <|channel>thought as the Start String, and <channel|> as the End String.
🖥️ Use with mlx
pip install -U mlx-vlm
mlx_vlm.generate --model zecanard/Gemopus-4-31B-it-MLX-3bit-mixed_3_6 --max-tokens 100 --temperature 0 --prompt "Describe this image." --image <path_to_image>
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