Instructions to use Aivesa/d8b02868-3f22-4fc4-bf4b-cd5c2a0ad840 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Aivesa/d8b02868-3f22-4fc4-bf4b-cd5c2a0ad840 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("HuggingFaceH4/tiny-random-LlamaForCausalLM") model = PeftModel.from_pretrained(base_model, "Aivesa/d8b02868-3f22-4fc4-bf4b-cd5c2a0ad840") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e8d6bf8503c91b71f237d8fb62b5ba731cfd7cd591aed3fe26424f3ac69cfa42
- Size of remote file:
- 27 kB
- SHA256:
- 4325df93bbc14a519e2f8529d79377b0205e212fb7fe058d4778012b38229ed9
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