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:
- a9e52b53bba9465a632f2ae8dba203fe3def9f8f8dd3a9a4cb93c08a40017833
- Size of remote file:
- 168 MB
- SHA256:
- 9545b6d7a7e6264a24345479185b62ff581dfb54bcc074c23e14973e75d1ba02
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