PEFT
Safetensors
mistral
alignment-handbook
trl
sft
unsloth
Generated from Trainer
4-bit precision
bitsandbytes
Instructions to use Peter/uploadtestsmallstep with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Peter/uploadtestsmallstep with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/mistral-7b") model = PeftModel.from_pretrained(base_model, "Peter/uploadtestsmallstep") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Desktop
Download training_args.bin from Peter/uploadtestsmallstep: direct link, hf CLI and curl.
- Browser
- Download file 4.73 kB
-
https://huggingface.co/Peter/uploadtestsmallstep/resolve/main/training_args.bin
- Command line
-
hf download hf://Peter/uploadtestsmallstep/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Peter/uploadtestsmallstep/resolve/main/training_args.bin
4.73 kB
- Xet hash:
- e698e62fdc0214afd2de4153b27a783839bb64737de7d241736e4a63e8afa6e1
- Size of remote file:
- 4.73 kB
- SHA256:
- 7860103c4029471570a6c2bbbe2a6a10ca474ff1bb45e01c79d447ec86ed7133
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