Instructions to use pavidhiman/sft-llama318b-89aa660c with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pavidhiman/sft-llama318b-89aa660c with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("pavidhiman/sft-llama318b-89aa660c", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Unsloth Studio new
How to use pavidhiman/sft-llama318b-89aa660c 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 pavidhiman/sft-llama318b-89aa660c 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 pavidhiman/sft-llama318b-89aa660c to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for pavidhiman/sft-llama318b-89aa660c to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="pavidhiman/sft-llama318b-89aa660c", max_seq_length=2048, )
- Xet hash:
- c5761e3270be38c65f56db3ea8bbd13132ccaa93e2c8b988caf6b2d9b409c275
- Size of remote file:
- 83.9 MB
- SHA256:
- ef10cfdba3a0072d34ef73f7c75f84c18cba2c06cf34d780396fc67d77d0d71f
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