Instructions to use CallMcMargin/gemma-3-12b-it-projection-abliterated-mlx-bf16-mxfp4-mixed-4-6 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use CallMcMargin/gemma-3-12b-it-projection-abliterated-mlx-bf16-mxfp4-mixed-4-6 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("CallMcMargin/gemma-3-12b-it-projection-abliterated-mlx-bf16-mxfp4-mixed-4-6") 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
- MLX LM
How to use CallMcMargin/gemma-3-12b-it-projection-abliterated-mlx-bf16-mxfp4-mixed-4-6 with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "CallMcMargin/gemma-3-12b-it-projection-abliterated-mlx-bf16-mxfp4-mixed-4-6"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "CallMcMargin/gemma-3-12b-it-projection-abliterated-mlx-bf16-mxfp4-mixed-4-6" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "CallMcMargin/gemma-3-12b-it-projection-abliterated-mlx-bf16-mxfp4-mixed-4-6", "messages": [ {"role": "user", "content": "Hello"} ] }'
- Xet hash:
- c4c7aa84fb94a696d78f2bde62590a25b6741b7365ed842f9310b5dcab71d95d
- Size of remote file:
- 33.4 MB
- SHA256:
- 4667f2089529e8e7657cfb6d1c19910ae71ff5f28aa7ab2ff2763330affad795
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