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, )
File size: 614 Bytes
6121f68 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 | {
"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"
} |