Feature Extraction
Transformers
Safetensors
llama
binary-code-similarity
assembly
code
representation-learning
Instructions to use mhosseina96/Llama-3.2-1B-LENA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mhosseina96/Llama-3.2-1B-LENA with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="mhosseina96/Llama-3.2-1B-LENA")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("mhosseina96/Llama-3.2-1B-LENA", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 810876231e26ea55a7cf80488c661e337298208953208adaf34b50b1adc1b919
- Size of remote file:
- 2.47 GB
- SHA256:
- 9d5a804c31605138540f4081718823f73ef51a180f22fb819643f19c3fa89ca2
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