Instructions to use RESMP-DEV/LFM2.5-Encoder-230M-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-230M-Code-MXFP8-GPTQ with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir LFM2.5-Encoder-230M-Code-MXFP8-GPTQ RESMP-DEV/LFM2.5-Encoder-230M-Code-MXFP8-GPTQ
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
Download modules.json from RESMP-DEV/LFM2.5-Encoder-230M-Code-MXFP8-GPTQ: direct link, hf CLI and curl.
- Browser
- Download file 230 Bytes
-
https://huggingface.co/RESMP-DEV/LFM2.5-Encoder-230M-Code-MXFP8-GPTQ/resolve/main/modules.json
- Command line
-
hf download hf://RESMP-DEV/LFM2.5-Encoder-230M-Code-MXFP8-GPTQ/modules.json
-
curl -L -o modules.json https://huggingface.co/RESMP-DEV/LFM2.5-Encoder-230M-Code-MXFP8-GPTQ/resolve/main/modules.json
230 Bytes
| [ | |
| { | |
| "idx": 0, | |
| "name": "0", | |
| "path": "", | |
| "type": "sentence_transformers.models.Transformer" | |
| }, | |
| { | |
| "idx": 1, | |
| "name": "1", | |
| "path": "1_Pooling", | |
| "type": "sentence_transformers.models.Pooling" | |
| } | |
| ] | |