Instructions to use grc-iit/FunctionGemma-ndp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use grc-iit/FunctionGemma-ndp with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("grc-iit/FunctionGemma-ndp", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Unsloth Studio
How to use grc-iit/FunctionGemma-ndp with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for grc-iit/FunctionGemma-ndp to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for grc-iit/FunctionGemma-ndp to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for grc-iit/FunctionGemma-ndp to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="grc-iit/FunctionGemma-ndp", max_seq_length=2048, )
| { | |
| "backend": "unsloth", | |
| "base_model": "unsloth/functiongemma-270m-it", | |
| "model_type": "gemma3_text", | |
| "loader": "FastLanguageModel", | |
| "target_modules": [ | |
| "q_proj", | |
| "k_proj", | |
| "v_proj", | |
| "o_proj", | |
| "gate_proj", | |
| "up_proj", | |
| "down_proj" | |
| ], | |
| "response_masking": true, | |
| "n_rows": 1087, | |
| "lora_dir": "/u/sislam3/Phagocyte/example/datasets/A_clio_3tools/artifacts/tool_functiongemma_v4_270m/lora", | |
| "train_loss": 0.181027400079092, | |
| "train_runtime_s": 112.4248, | |
| "merged_dir": "/u/sislam3/Phagocyte/example/datasets/A_clio_3tools/artifacts/tool_functiongemma_v4_270m/merged_16bit" | |
| } |