Instructions to use dhananjayyy23/shikshaedge-gemma4-e4b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Local Apps Settings
- Unsloth Studio
How to use dhananjayyy23/shikshaedge-gemma4-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 dhananjayyy23/shikshaedge-gemma4-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 dhananjayyy23/shikshaedge-gemma4-e4b to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for dhananjayyy23/shikshaedge-gemma4-e4b to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="dhananjayyy23/shikshaedge-gemma4-e4b", max_seq_length=2048, )
ShikshaEdge โ Gemma 3 4B fine-tuned for Indian K-12
Fine-tuned from Gemma 3 4B using QLoRA (Unsloth) on 2,170 curriculum-aligned Q&A pairs from NCERT textbooks in Hindi, Marathi, and English. Class 6-10 curriculum.
Use case
Offline AI tutoring for Indian K-12 students via Ollama.
Training
- Method: QLoRA (r=16) via Unsloth
- Dataset: 2,170 NCERT Q&A pairs
- Languages: Hindi, Marathi, English
- Hardware: Google Colab T4
- Epochs: 3
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