Instructions to use RESMP-DEV/LFM2.5-Encoder-350M-MLX-FP32 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use RESMP-DEV/LFM2.5-Encoder-350M-MLX-FP32 with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] hf download RESMP-DEV/LFM2.5-Encoder-350M-MLX-FP32 --local-dir LFM2.5-Encoder-350M-MLX-FP32
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
Download pyproject.toml from RESMP-DEV/LFM2.5-Encoder-350M-MLX-FP32: direct link, hf CLI and curl.
- Browser
- Download file 259 Bytes
-
https://huggingface.co/RESMP-DEV/LFM2.5-Encoder-350M-MLX-FP32/resolve/main/pyproject.toml
- Command line
-
hf download hf://RESMP-DEV/LFM2.5-Encoder-350M-MLX-FP32/pyproject.toml
-
curl -L -o pyproject.toml https://huggingface.co/RESMP-DEV/LFM2.5-Encoder-350M-MLX-FP32/resolve/main/pyproject.toml
259 Bytes
| [project] | |
| name = "lfm2-5-encoder-350m-mlx-fp32" | |
| version = "0.1.0" | |
| requires-python = ">=3.11" | |
| dependencies = ["mlx==0.32.0"] | |
| [project.optional-dependencies] | |
| validate = [ | |
| "numpy==2.3.5", | |
| "safetensors==0.8.0", | |
| "torch==2.12.1", | |
| "transformers==5.12.1", | |
| ] | |