Instructions to use nblinh63/07d7800a-5441-44b2-8e4b-5b1f2fb61a61 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use nblinh63/07d7800a-5441-44b2-8e4b-5b1f2fb61a61 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/Meta-Llama-3.1-8B-Instruct") model = PeftModel.from_pretrained(base_model, "nblinh63/07d7800a-5441-44b2-8e4b-5b1f2fb61a61") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a371bab0f2bc88d5dd38e6ce7dcf009635b987ff3e9c400887a2b7a73eba728f
- Size of remote file:
- 6.78 kB
- SHA256:
- 963bb75ed3513466042b3e8527caf560f56d60b2bdc2001ca2ecd327b76ca765
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