Instructions to use patruff/lam3sft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use patruff/lam3sft with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("patruff/lam3sft", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Studio
How to use patruff/lam3sft 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 patruff/lam3sft 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 patruff/lam3sft to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for patruff/lam3sft to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="patruff/lam3sft", max_seq_length=2048, )
- Xet hash:
- 670d70c886ccdc4b6b8a8762412e6d8cc243cf34ca4a8949056df68334a84e09
- Size of remote file:
- 17.2 MB
- SHA256:
- 14b5e679cb69af62e14c3b98d346177bd4137d882a44f87dec9efec982b01a05
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.