Text Generation
PEFT
Safetensors
mistral
alignment-handbook
Generated from Trainer
trl
sft
conversational
Instructions to use edpowers/data with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use edpowers/data with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-Instruct-v0.2") model = PeftModel.from_pretrained(base_model, "edpowers/data") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1bed1849b696f40ef7ce4b68e3270b8186c4c0c80234c0b6167d6c1a10bc4624
- Size of remote file:
- 617 MB
- SHA256:
- bb6d6addf9a5964eb8913cf4d298675cb9044b190b16c9f90bc3a3143890a304
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.