Instructions to use mlx-community/Fathom-R1-14B-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use mlx-community/Fathom-R1-14B-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("mlx-community/Fathom-R1-14B-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
- LM Studio
- MLX LM
How to use mlx-community/Fathom-R1-14B-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 "mlx-community/Fathom-R1-14B-4bit"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "mlx-community/Fathom-R1-14B-4bit" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "mlx-community/Fathom-R1-14B-4bit", "messages": [ {"role": "user", "content": "Hello"} ] }'
| license: mit | |
| library_name: mlx | |
| datasets: | |
| - FractalAIResearch/Fathom-V0.4-SFT-Shortest-Chains | |
| - FractalAIResearch/Fathom-V0.6-Iterative-Curriculum-Learning | |
| base_model: FractalAIResearch/Fathom-R1-14B | |
| pipeline_tag: text-generation | |
| tags: | |
| - mlx | |
| # mlx-community/Fathom-R1-14B-4bit | |
| This model [mlx-community/Fathom-R1-14B-4bit](https://huggingface.co/mlx-community/Fathom-R1-14B-4bit) was | |
| converted to MLX format from [FractalAIResearch/Fathom-R1-14B](https://huggingface.co/FractalAIResearch/Fathom-R1-14B) | |
| using mlx-lm version **0.25.0**. | |
| ## Use with mlx | |
| ```bash | |
| pip install mlx-lm | |
| ``` | |
| ```python | |
| from mlx_lm import load, generate | |
| model, tokenizer = load("mlx-community/Fathom-R1-14B-4bit") | |
| prompt = "hello" | |
| if tokenizer.chat_template is not None: | |
| messages = [{"role": "user", "content": prompt}] | |
| prompt = tokenizer.apply_chat_template( | |
| messages, add_generation_prompt=True | |
| ) | |
| response = generate(model, tokenizer, prompt=prompt, verbose=True) | |
| ``` | |