Instructions to use rudrararaa/satya-gemma4-e4b-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Local Apps
- Unsloth Studio new
How to use rudrararaa/satya-gemma4-e4b-lora 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 rudrararaa/satya-gemma4-e4b-lora 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 rudrararaa/satya-gemma4-e4b-lora to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for rudrararaa/satya-gemma4-e4b-lora to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="rudrararaa/satya-gemma4-e4b-lora", max_seq_length=2048, )
SATYA — Fine-Tuned Gemma 4 E4B for Truth Detection
LoRA adapter fine-tuned for on-device misinformation detection.
Training Data
- 3,232 Indian fact-checks (BOOM Live, Alt News, FACTLY, The Quint, Vishvas News)
- 5,500 LIAR2 political claims
Results (on 437 held-out test examples)
| Metric | Base Gemma 4 | SATYA Fine-Tuned | Improvement |
|---|---|---|---|
| Accuracy | 0.3461 | 0.5370 | +0.1910 |
| F1 (macro) | 0.2306 | 0.3025 | +0.0718 |
Training
- Method: LoRA (rank 16, alpha 32, dropout 0.0) via Unsloth
- Steps: 1000 of 1392 (Kaggle 12-hour cap)
- Hardware: Tesla T4 x2
License: Apache 2.0
Inference Providers NEW
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