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)# 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
- Xet hash:
- 0fffa3d261a4b6936a8e602654a41a81d404ec984cd3d120d228f2f1bafd1d4b
- Size of remote file:
- 459 MB
- SHA256:
- 993c5ccf3c2d4153d748e4738dba907b393bf521eb086601b87adcac75b46b87
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