Instructions to use GavinChan1105/Llama-3-8B-sft-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use GavinChan1105/Llama-3-8B-sft-lora with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.1-8B") model = PeftModel.from_pretrained(base_model, "GavinChan1105/Llama-3-8B-sft-lora") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from GavinChan1105/Llama-3-8B-sft-lora: direct link, hf CLI and curl.
- Browser
- Download file 6.78 kB
-
https://huggingface.co/GavinChan1105/Llama-3-8B-sft-lora/resolve/main/training_args.bin
- Command line
-
hf download hf://GavinChan1105/Llama-3-8B-sft-lora/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/GavinChan1105/Llama-3-8B-sft-lora/resolve/main/training_args.bin
6.78 kB
- Xet hash:
- bf5d0a6b72f8f4a432f5f709379a6fd985f6742a92508caa68ebaed96fce9800
- Size of remote file:
- 6.78 kB
- SHA256:
- 3265451191e0c5881de6af00d2c0588b95f2cca64a36da7bf80e38bd0d1a1bb2
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