Instructions to use ruhullah1/mistral-7b-oig-unsloth with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ruhullah1/mistral-7b-oig-unsloth with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ruhullah1/mistral-7b-oig-unsloth", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Studio
How to use ruhullah1/mistral-7b-oig-unsloth with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for ruhullah1/mistral-7b-oig-unsloth to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for ruhullah1/mistral-7b-oig-unsloth to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for ruhullah1/mistral-7b-oig-unsloth to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="ruhullah1/mistral-7b-oig-unsloth", max_seq_length=2048, )
- Xet hash:
- ad23b6583c4f7de6c5f9845f2e752e4ca1f3e63ac87b277be00d345fdf79c42b
- Size of remote file:
- 168 MB
- SHA256:
- 54334d0db2e57ca03f6f5a8808cafc16232d155a22076d87c2a2176079e14ab8
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.