Instructions to use VQA-DeepLearning/gemma_4_lora_E4b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use VQA-DeepLearning/gemma_4_lora_E4b with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("VQA-DeepLearning/gemma_4_lora_E4b", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

Unsloth Studio

How to use VQA-DeepLearning/gemma_4_lora_E4b 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 VQA-DeepLearning/gemma_4_lora_E4b 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 VQA-DeepLearning/gemma_4_lora_E4b to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for VQA-DeepLearning/gemma_4_lora_E4b to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="VQA-DeepLearning/gemma_4_lora_E4b",
    max_seq_length=2048,
)

gemma_4_lora_E4b / training_args.bin

Commit History

Training in progress, step 50

a5277f7
verified

HoangVuSnape commited on Jun 18

Commit History

Training in progress, step 50 a5277f7 verified

Training in progress, step 50

a5277f7
verified