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 functiongemma_memory_fusion_config.json from vtava/functiongemma-270m-it-simple-tool-calling-memory-fusion: direct link, hf CLI and curl.
- Browser
- Download file 502 Bytes
-
https://huggingface.co/vtava/functiongemma-270m-it-simple-tool-calling-memory-fusion/resolve/main/functiongemma_memory_fusion_config.json
- Command line
-
hf download hf://vtava/functiongemma-270m-it-simple-tool-calling-memory-fusion/functiongemma_memory_fusion_config.json
-
curl -L -o functiongemma_memory_fusion_config.json https://huggingface.co/vtava/functiongemma-270m-it-simple-tool-calling-memory-fusion/resolve/main/functiongemma_memory_fusion_config.json
502 Bytes
| { | |
| "format": "functiongemma-memory-fusion-sequential-v1", | |
| "base_model": "vtava/functiongemma-270m-it-simple-tool-calling", | |
| "accepted_layers": [ | |
| 5, | |
| 11, | |
| 17 | |
| ], | |
| "memory_fusion": { | |
| "feature_dim": 32, | |
| "memory_rank": 64, | |
| "dilations": [ | |
| 1, | |
| 2, | |
| 4, | |
| 8, | |
| 16, | |
| 32, | |
| 64, | |
| 128 | |
| ], | |
| "shifted_window": 8, | |
| "train_output_projection": true | |
| }, | |
| "metadata": { | |
| "base_model_revision": "303296b8f3262f08ecdfc6008e94374bbd697d02" | |
| } | |
| } |