Instructions to use shanjivkr/vit-gemma-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use shanjivkr/vit-gemma-model with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("shanjivkr/vit-gemma-model", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

Unsloth Studio new

How to use shanjivkr/vit-gemma-model 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 shanjivkr/vit-gemma-model 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 shanjivkr/vit-gemma-model to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for shanjivkr/vit-gemma-model to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="shanjivkr/vit-gemma-model",
    max_seq_length=2048,
)

vit-gemma-model / handler.py

Commit History

Upload handler.py with huggingface_hub

b372798
verified

shanjivkr commited on Mar 7

Commit History

Upload handler.py with huggingface_hub b372798 verified

Upload handler.py with huggingface_hub

b372798
verified