Instructions to use prxy5604/a3c52648-f93a-41de-8f41-b2cf5f485e8a with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use prxy5604/a3c52648-f93a-41de-8f41-b2cf5f485e8a 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, "prxy5604/a3c52648-f93a-41de-8f41-b2cf5f485e8a") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from prxy5604/a3c52648-f93a-41de-8f41-b2cf5f485e8a: direct link, hf CLI and curl.
- Browser
- Download file 6.84 kB
-
https://huggingface.co/prxy5604/a3c52648-f93a-41de-8f41-b2cf5f485e8a/resolve/main/training_args.bin
- Command line
-
hf download hf://prxy5604/a3c52648-f93a-41de-8f41-b2cf5f485e8a/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/prxy5604/a3c52648-f93a-41de-8f41-b2cf5f485e8a/resolve/main/training_args.bin
6.84 kB
- Xet hash:
- 9fa3d71a0442ab4227f7ae36d2a3a5698393a40c685ca183a283669e62ec1ace
- Size of remote file:
- 6.84 kB
- SHA256:
- d8b4e074a9a0ddc06877d762cbf59bd4b601b76c47dc5389c56ebe2e2d2b180c
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