Instructions to use DevQuasar/Mistral-7B-Instruct-v0.3_brainstorm-v3.1_adapter with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use DevQuasar/Mistral-7B-Instruct-v0.3_brainstorm-v3.1_adapter with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-Instruct-v0.3") model = PeftModel.from_pretrained(base_model, "DevQuasar/Mistral-7B-Instruct-v0.3_brainstorm-v3.1_adapter") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from DevQuasar/Mistral-7B-Instruct-v0.3_brainstorm-v3.1_adapter: direct link, hf CLI and curl.
- Browser
- Download file 5.24 kB
-
https://huggingface.co/DevQuasar/Mistral-7B-Instruct-v0.3_brainstorm-v3.1_adapter/resolve/main/training_args.bin
- Command line
-
hf download hf://DevQuasar/Mistral-7B-Instruct-v0.3_brainstorm-v3.1_adapter/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/DevQuasar/Mistral-7B-Instruct-v0.3_brainstorm-v3.1_adapter/resolve/main/training_args.bin
5.24 kB
- Xet hash:
- e01100344af19e1e389982b2a1a9311b0a52622afe3d360a9953f9d51053f496
- Size of remote file:
- 5.24 kB
- SHA256:
- 7f8e4d64acfb2a77a85af9e7fb6a488a00ff3aa1b394e96957e5e57dd0d2996a
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.