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
File size: 502 Bytes
7d2c297 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 | {
"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"
}
} |