Instructions to use bane5631/24e0c56e-8af7-4e7c-a681-6237c0cc9eed with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use bane5631/24e0c56e-8af7-4e7c-a681-6237c0cc9eed with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("TinyLlama/TinyLlama-1.1B-Chat-v1.0") model = PeftModel.from_pretrained(base_model, "bane5631/24e0c56e-8af7-4e7c-a681-6237c0cc9eed") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from bane5631/24e0c56e-8af7-4e7c-a681-6237c0cc9eed: direct link, hf CLI and curl.
- Browser
- Download file 6.78 kB
-
https://huggingface.co/bane5631/24e0c56e-8af7-4e7c-a681-6237c0cc9eed/resolve/main/training_args.bin
- Command line
-
hf download hf://bane5631/24e0c56e-8af7-4e7c-a681-6237c0cc9eed/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/bane5631/24e0c56e-8af7-4e7c-a681-6237c0cc9eed/resolve/main/training_args.bin
6.78 kB
- Xet hash:
- 70af890c27292012fff1d18288aaf7918ddfdab51d2146c084362088108b733e
- Size of remote file:
- 6.78 kB
- SHA256:
- 794d4a7d749a9d2fdc90b35c0dc99ae896aab06ce340eb31b5d1629bd30f4eef
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