Instructions to use havinash-ai/be2dd730-01a6-4a44-85cd-bf3e7955e40c with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use havinash-ai/be2dd730-01a6-4a44-85cd-bf3e7955e40c with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("dunzhang/stella_en_1.5B_v5") model = PeftModel.from_pretrained(base_model, "havinash-ai/be2dd730-01a6-4a44-85cd-bf3e7955e40c") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from havinash-ai/be2dd730-01a6-4a44-85cd-bf3e7955e40c: direct link, hf CLI and curl.
- Browser
- Download file 6.78 kB
-
https://huggingface.co/havinash-ai/be2dd730-01a6-4a44-85cd-bf3e7955e40c/resolve/main/training_args.bin
- Command line
-
hf download hf://havinash-ai/be2dd730-01a6-4a44-85cd-bf3e7955e40c/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/havinash-ai/be2dd730-01a6-4a44-85cd-bf3e7955e40c/resolve/main/training_args.bin
6.78 kB
- Xet hash:
- 5ceefabe8255d2eb36f738ccdbb6aa7344181526fdf49a50de67eb5e777c0da1
- Size of remote file:
- 6.78 kB
- SHA256:
- bb9c4fe530c472d776fed65d714782de3f4a14aed9ac8b6d769605904b2b55dc
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