Instructions to use RESMP-DEV/LFM2.5-Encoder-350M-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-350M-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-350M-Code-BF16", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("RESMP-DEV/LFM2.5-Encoder-350M-Code-BF16", trust_remote_code=True) model = AutoModel.from_pretrained("RESMP-DEV/LFM2.5-Encoder-350M-Code-BF16", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download MODIFICATIONS.md from RESMP-DEV/LFM2.5-Encoder-350M-Code-BF16: direct link, hf CLI and curl.
- Browser
- Download file 369 Bytes
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https://huggingface.co/RESMP-DEV/LFM2.5-Encoder-350M-Code-BF16/resolve/main/MODIFICATIONS.md
- Command line
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hf download hf://RESMP-DEV/LFM2.5-Encoder-350M-Code-BF16/MODIFICATIONS.md
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curl -L -o MODIFICATIONS.md https://huggingface.co/RESMP-DEV/LFM2.5-Encoder-350M-Code-BF16/resolve/main/MODIFICATIONS.md
369 Bytes
| # Modifications by RESMP.DEV | |
| This is a derivative of the identified LiquidAI LFM2.5 Encoder checkpoint, not an official LiquidAI release. RESMP.DEV removed the masked-language-model head and contrastively fine-tuned the complete encoder body for code retrieval using the procedure and corpus hashes in `training_report.json`. The resulting weights are stored in BF16. | |