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
- Xet hash:
- 664572ebcb5e47da16c652350a22fa7703ecfa47c2cf5c0f40775e7d13a09d90
- Size of remote file:
- 709 MB
- SHA256:
- 525d4ace3592b894d713b8c61a5875dd103b09ec6faa5760bfa0711404056f62
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