Instructions to use coastalcph/llama3-joint-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use coastalcph/llama3-joint-lora with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("NousResearch/Meta-Llama-3-8B") model = PeftModel.from_pretrained(base_model, "coastalcph/llama3-joint-lora") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from coastalcph/llama3-joint-lora: direct link, hf CLI and curl.
- Browser
- Download file 7.06 kB
-
https://huggingface.co/coastalcph/llama3-joint-lora/resolve/main/training_args.bin
- Command line
-
hf download hf://coastalcph/llama3-joint-lora/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/coastalcph/llama3-joint-lora/resolve/main/training_args.bin
7.06 kB
- Xet hash:
- 771b70ef5932462292b4a922f1f65ea237fbf691e14e13803031f04380e8ce17
- Size of remote file:
- 7.06 kB
- SHA256:
- f5a925fdabd70085bd80d460161c4f9e84e22a59f14dcdb288ee77a697507a01
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