Instructions to use RESMP-DEV/LFM2.5-Encoder-230M-Code-BF16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RESMP-DEV/LFM2.5-Encoder-230M-Code-BF16 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="RESMP-DEV/LFM2.5-Encoder-230M-Code-BF16", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("RESMP-DEV/LFM2.5-Encoder-230M-Code-BF16", trust_remote_code=True) model = AutoModel.from_pretrained("RESMP-DEV/LFM2.5-Encoder-230M-Code-BF16", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download embedding_config.json from RESMP-DEV/LFM2.5-Encoder-230M-Code-BF16: direct link, hf CLI and curl.
- Browser
- Download file 144 Bytes
-
https://huggingface.co/RESMP-DEV/LFM2.5-Encoder-230M-Code-BF16/resolve/main/embedding_config.json
- Command line
-
hf download hf://RESMP-DEV/LFM2.5-Encoder-230M-Code-BF16/embedding_config.json
-
curl -L -o embedding_config.json https://huggingface.co/RESMP-DEV/LFM2.5-Encoder-230M-Code-BF16/resolve/main/embedding_config.json
144 Bytes
| { | |
| "pooling": "attention_mask_mean", | |
| "normalize": true, | |
| "dimensions": 1024, | |
| "query_prefix": "query: ", | |
| "passage_prefix": "passage: " | |
| } | |