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
-
https://huggingface.co/TendernessChen/Fin-R1-mlx-fp16/resolve/main/README.md
- Command line
-
hf download hf://TendernessChen/Fin-R1-mlx-fp16/README.md
-
curl -L -o README.md https://huggingface.co/TendernessChen/Fin-R1-mlx-fp16/resolve/main/README.md
815 Bytes
metadata
base_model: SUFE-AIFLM-Lab/Fin-R1
tags:
- mlx
TendernessChen/Fin-R1-mlx-fp16
The Model TendernessChen/Fin-R1-mlx-fp16 was converted to MLX format from SUFE-AIFLM-Lab/Fin-R1 using mlx-lm version 0.22.1.
Use with mlx
pip install mlx-lm
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)