Instructions to use CharlesLi/mistral_gsm8k_gold_answer_lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use CharlesLi/mistral_gsm8k_gold_answer_lora with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-Instruct-v0.1") model = PeftModel.from_pretrained(base_model, "CharlesLi/mistral_gsm8k_gold_answer_lora") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d314fdaceb5026a3d6a4517bf3f05f027f49b72babca29c1acc994554648c0a1
- Size of remote file:
- 168 MB
- SHA256:
- 2937e88e9c3b95872db0ce7ac37e57a404da2aa9dadce1280e1c44a48343f07f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.