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_run_status.json from vtava/functiongemma-270m-it-simple-tool-calling-memory-fusion: direct link, hf CLI and curl.
- Browser
- Download file 124 Bytes
-
https://huggingface.co/vtava/functiongemma-270m-it-simple-tool-calling-memory-fusion/resolve/main/sequential_run_status.json
- Command line
-
hf download hf://vtava/functiongemma-270m-it-simple-tool-calling-memory-fusion/sequential_run_status.json
-
curl -L -o sequential_run_status.json https://huggingface.co/vtava/functiongemma-270m-it-simple-tool-calling-memory-fusion/resolve/main/sequential_run_status.json
124 Bytes
| { | |
| "status": "complete", | |
| "accepted_layers": [ | |
| 5, | |
| 11, | |
| 17 | |
| ], | |
| "target_layers": [ | |
| 5, | |
| 11, | |
| 17 | |
| ] | |
| } |