Instructions to use vtava/functiongemma-270m-it-simple-tool-calling-memory-fusion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vtava/functiongemma-270m-it-simple-tool-calling-memory-fusion with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("vtava/functiongemma-270m-it-simple-tool-calling-memory-fusion", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download sequential_progress.pt from vtava/functiongemma-270m-it-simple-tool-calling-memory-fusion: direct link, hf CLI and curl.
- Browser
- Download file 18.8 MB
-
https://huggingface.co/vtava/functiongemma-270m-it-simple-tool-calling-memory-fusion/resolve/main/sequential_progress.pt
- Command line
-
hf download hf://vtava/functiongemma-270m-it-simple-tool-calling-memory-fusion/sequential_progress.pt
-
curl -L -o sequential_progress.pt https://huggingface.co/vtava/functiongemma-270m-it-simple-tool-calling-memory-fusion/resolve/main/sequential_progress.pt
18.8 MB
- Xet hash:
- 798bf3f00c0c8552b44a743d970a0033f477df169190976a1a3f5bc00fb2f886
- Size of remote file:
- 18.8 MB
- SHA256:
- b696e561fed982451abdf60c09af96e4d48fc66dd92dcefd17032062d80e9c11
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