Instructions to use TendernessChen/Fin-R1-mlx-fp16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use TendernessChen/Fin-R1-mlx-fp16 with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] hf download TendernessChen/Fin-R1-mlx-fp16 --local-dir Fin-R1-mlx-fp16
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
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Download README.md from TendernessChen/Fin-R1-mlx-fp16: direct link, hf CLI and curl.
- Browser
- Download file 815 Bytes
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https://huggingface.co/TendernessChen/Fin-R1-mlx-fp16/resolve/main/README.md
- Command line
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hf download hf://TendernessChen/Fin-R1-mlx-fp16/README.md
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curl -L -o README.md https://huggingface.co/TendernessChen/Fin-R1-mlx-fp16/resolve/main/README.md
815 Bytes
| base_model: SUFE-AIFLM-Lab/Fin-R1 | |
| tags: | |
| - mlx | |
| # TendernessChen/Fin-R1-mlx-fp16 | |
| The Model [TendernessChen/Fin-R1-mlx-fp16](https://huggingface.co/TendernessChen/Fin-R1-mlx-fp16) was converted to MLX format from [SUFE-AIFLM-Lab/Fin-R1](https://huggingface.co/SUFE-AIFLM-Lab/Fin-R1) using mlx-lm version **0.22.1**. | |
| ## Use with mlx | |
| ```bash | |
| pip install mlx-lm | |
| ``` | |
| ```python | |
| from mlx_lm import load, generate | |
| model, tokenizer = load("TendernessChen/Fin-R1-mlx-fp16") | |
| prompt="hello" | |
| if hasattr(tokenizer, "apply_chat_template") and tokenizer.chat_template is not None: | |
| messages = [{"role": "user", "content": prompt}] | |
| prompt = tokenizer.apply_chat_template( | |
| messages, tokenize=False, add_generation_prompt=True | |
| ) | |
| response = generate(model, tokenizer, prompt=prompt, verbose=True) | |
| ``` | |