Instructions to use RESMP-DEV/LFM2.5-Encoder-350M-Code-MXFP8-GPTQ 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-Code-MXFP8-GPTQ with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] hf download RESMP-DEV/LFM2.5-Encoder-350M-Code-MXFP8-GPTQ --local-dir LFM2.5-Encoder-350M-Code-MXFP8-GPTQ
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
Download embedding_config.json from RESMP-DEV/LFM2.5-Encoder-350M-Code-MXFP8-GPTQ: direct link, hf CLI and curl.
- Browser
- Download file 144 Bytes
-
https://huggingface.co/RESMP-DEV/LFM2.5-Encoder-350M-Code-MXFP8-GPTQ/resolve/main/embedding_config.json
- Command line
-
hf download hf://RESMP-DEV/LFM2.5-Encoder-350M-Code-MXFP8-GPTQ/embedding_config.json
-
curl -L -o embedding_config.json https://huggingface.co/RESMP-DEV/LFM2.5-Encoder-350M-Code-MXFP8-GPTQ/resolve/main/embedding_config.json
144 Bytes
| { | |
| "pooling": "attention_mask_mean", | |
| "normalize": true, | |
| "dimensions": 1024, | |
| "query_prefix": "query: ", | |
| "passage_prefix": "passage: " | |
| } | |