Instructions to use pavidhiman/sft-llama318b-6003159d with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pavidhiman/sft-llama318b-6003159d with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("pavidhiman/sft-llama318b-6003159d", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Unsloth Studio new
How to use pavidhiman/sft-llama318b-6003159d 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-6003159d 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-6003159d 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-6003159d to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="pavidhiman/sft-llama318b-6003159d", max_seq_length=2048, )
- Xet hash:
- 687c484198a6a593c8f9ed82e7746329107f09b9d904f1b2258e7612188f833c
- Size of remote file:
- 83.9 MB
- SHA256:
- c37b2fa8cfae10c5e361bb2d474ad3760100e177e15b31f58f3085b5fde062dd
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