Instructions to use abaddon182/9902becd-9a95-4997-a536-1254a8265242 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use abaddon182/9902becd-9a95-4997-a536-1254a8265242 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, "abaddon182/9902becd-9a95-4997-a536-1254a8265242") - Notebooks
- Google Colab
- Kaggle
Download last-checkpoint/training_args.bin from abaddon182/9902becd-9a95-4997-a536-1254a8265242: direct link, hf CLI and curl.
- Browser
- Download file 6.84 kB
-
https://huggingface.co/abaddon182/9902becd-9a95-4997-a536-1254a8265242/resolve/main/last-checkpoint/training_args.bin
- Command line
-
hf download hf://abaddon182/9902becd-9a95-4997-a536-1254a8265242/last-checkpoint/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/abaddon182/9902becd-9a95-4997-a536-1254a8265242/resolve/main/last-checkpoint/training_args.bin
6.84 kB
- Xet hash:
- 7a293eeebc5273a18b396895d25a939e3a2d3f1d29d9c6080d6ea82d8c7b7e27
- Size of remote file:
- 6.84 kB
- SHA256:
- 5ca70b6202580e653638c6f123f6dc7043797aacb789666d98634ff3d32a13d9
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