Instructions to use dzanbek/d88783a6-4c53-475a-8545-bf69a0286e0c with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use dzanbek/d88783a6-4c53-475a-8545-bf69a0286e0c with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("scb10x/llama-3-typhoon-v1.5-8b-instruct") model = PeftModel.from_pretrained(base_model, "dzanbek/d88783a6-4c53-475a-8545-bf69a0286e0c") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a2251c6bff2c6c00b2fb78308145dd8b18dab1926ac9430862919148bd324ccf
- Size of remote file:
- 17.2 MB
- SHA256:
- 2539802b4016767e5475c0fa774895f4c33683f7c2ed9df643a61279e9ef1bd2
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