Feature Extraction
Transformers
Safetensors
lagrange_flash
custom-code
mathematics
physics
mechanistic-interpretability
program-synthesis
research
custom_code
Instructions to use FlaneAI/LaGrange-1.0-Flash with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use FlaneAI/LaGrange-1.0-Flash with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="FlaneAI/LaGrange-1.0-Flash", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("FlaneAI/LaGrange-1.0-Flash", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download lagrange_runtime.zip from FlaneAI/LaGrange-1.0-Flash: direct link, hf CLI and curl.
- Browser
- Download file 465 kB
-
https://huggingface.co/FlaneAI/LaGrange-1.0-Flash/resolve/main/lagrange_runtime.zip
- Command line
-
hf download hf://FlaneAI/LaGrange-1.0-Flash/lagrange_runtime.zip
-
curl -L -o lagrange_runtime.zip https://huggingface.co/FlaneAI/LaGrange-1.0-Flash/resolve/main/lagrange_runtime.zip
465 kB
- Xet hash:
- 8be74667143fb36d1488d443855f7ac48c515fe9cceb59905b1f3deffb522eef
- Size of remote file:
- 465 kB
- SHA256:
- ad7280c917f3db78c1891657a02920568d11751745cc50b89b72bb2985b657a3
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