Instructions to use monsterapi/mistral_7b_WizardLMEvolInstruct70k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use monsterapi/mistral_7b_WizardLMEvolInstruct70k with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-v0.1") model = PeftModel.from_pretrained(base_model, "monsterapi/mistral_7b_WizardLMEvolInstruct70k") - Notebooks
- Google Colab
- Kaggle
Download train-loss.png from monsterapi/mistral_7b_WizardLMEvolInstruct70k: direct link, hf CLI and curl.
- Browser
- Download file 1.29 MB
-
https://huggingface.co/monsterapi/mistral_7b_WizardLMEvolInstruct70k/resolve/main/train-loss.png
- Command line
-
hf download hf://monsterapi/mistral_7b_WizardLMEvolInstruct70k/train-loss.png
-
curl -L -o train-loss.png https://huggingface.co/monsterapi/mistral_7b_WizardLMEvolInstruct70k/resolve/main/train-loss.png
1.29 MB

- Xet hash:
- 7f9f78f3d537f84f86545893b5f8aff71a6c51357e73cfdb966ef5e2e73c9c0d
- Size of remote file:
- 1.29 MB
- SHA256:
- 5e376af9e3339d6d36604d2ae69a878c48504eb2eb84dc7380f4f0efff47d97e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.