Instructions to use madhuHuggingface/functiongemma-vpc-finetunedv2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use madhuHuggingface/functiongemma-vpc-finetunedv2 with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("madhuHuggingface/functiongemma-vpc-finetunedv2", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

Unsloth Studio new

How to use madhuHuggingface/functiongemma-vpc-finetunedv2 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 madhuHuggingface/functiongemma-vpc-finetunedv2 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 madhuHuggingface/functiongemma-vpc-finetunedv2 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for madhuHuggingface/functiongemma-vpc-finetunedv2 to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="madhuHuggingface/functiongemma-vpc-finetunedv2",
    max_seq_length=2048,
)

functiongemma-vpc-finetunedv2 / last-checkpoint /tokenizer.model

Commit History

Training in progress, step 100, checkpoint

1377cac
verified

madhuHuggingface commited on 17 days ago

Commit History

Training in progress, step 100, checkpoint 1377cac verified

Training in progress, step 100, checkpoint

1377cac
verified