Instructions to use GusLovesMath/LlaMATH-3-8B-Instruct-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use GusLovesMath/LlaMATH-3-8B-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("GusLovesMath/LlaMATH-3-8B-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 Settings
- LM Studio
- MLX LM
How to use GusLovesMath/LlaMATH-3-8B-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 "GusLovesMath/LlaMATH-3-8B-Instruct-4bit"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "GusLovesMath/LlaMATH-3-8B-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": "GusLovesMath/LlaMATH-3-8B-Instruct-4bit", "messages": [ {"role": "user", "content": "Hello"} ] }'
| language: | |
| - en | |
| license: other | |
| tags: | |
| - meta | |
| - pytorch | |
| - llama | |
| - llama-3 | |
| - mlx | |
| - mlx | |
| pipeline_tag: text-generation | |
| license_name: llama3 | |
| license_link: LICENSE | |
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| # GusLovesMath/LlaMATH-3-8B-Instruct-4bit | |
| This model was converted to MLX format from [`mlx-community/Meta-Llama-3-8B-Instruct-4bit`]() using mlx-lm version **0.12.1**. | |
| Refer to the [original model card](https://huggingface.co/mlx-community/Meta-Llama-3-8B-Instruct-4bit) for more details on the model. | |
| ## Use with mlx | |
| ```bash | |
| pip install mlx-lm | |
| ``` | |
| ```python | |
| from mlx_lm import load, generate | |
| model, tokenizer = load("GusLovesMath/LlaMATH-3-8B-Instruct-4bit") | |
| response = generate(model, tokenizer, prompt="hello", verbose=True) | |
| ``` | |