Instructions to use jayavibhav/llama3.2_11B_Vision_Maths_Geometry with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jayavibhav/llama3.2_11B_Vision_Maths_Geometry with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("jayavibhav/llama3.2_11B_Vision_Maths_Geometry", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Unsloth Studio new
How to use jayavibhav/llama3.2_11B_Vision_Maths_Geometry 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 jayavibhav/llama3.2_11B_Vision_Maths_Geometry 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 jayavibhav/llama3.2_11B_Vision_Maths_Geometry to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for jayavibhav/llama3.2_11B_Vision_Maths_Geometry to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="jayavibhav/llama3.2_11B_Vision_Maths_Geometry", max_seq_length=2048, )
- Xet hash:
- 6fa041e2fbc7a16e7643f3e3e5e98c5a5ef9dee2866cdb7a1a679ad9c059a731
- Size of remote file:
- 17.2 MB
- SHA256:
- abd08b46c9c01a903788030f36d9731aad064d0c420e66b88e3e143cda9e4e9a
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