---
# YAML Frontmatter for Hugging Face Hub
language:
  - hi
  - en
license: apache-2.0
library_name: transformers
base_model: unsloth/gemma-4-E2B-it-unsloth-bnb-4bit
model-index:
  - name: StriSakhi-Gemma4-2B-LoRA
    results:
      - task:
          type: text-generation
          name: Conversational Legal Guidance
        dataset:
          type: stri-sakhi-legal-549
          name: StriSakhi Legal Training Corpus
        metrics:
          - type: accuracy
            name: Section Citation Accuracy
            value: 0.86
          - type: accuracy
            name: Hindi Purity (Devanagari Ratio)
            value: 0.89
          - type: accuracy
            name: Overall Benchmark Pass Rate
            value: 0.864
tags:
  - unsloth
  - gemma-4
  - lora
  - peft
  - legal-ai
  - women-safety
  - india
  - hindi
  - hinglish
  - domestic-violence
  - posh-act
  - dv-act
  - nalsa
  - structured-output
  - safety-first
datasets:
  - stri-sakhi-legal-549
metrics:
  - section_accuracy
  - hindi_purity
  - helpline_recall
  - timeline_compliance
  - hallucination_rate
pipeline_tag: text-generation
---

# StriSakhi-Gemma4-2B-LoRA — Legal AI Advocate for Indian Women

**Developed by:** Shubendu Biswas
**Competition:** Kaggle Gemma 4 Hackathon / Women in AI Challenge  
**Base Model:** [`unsloth/gemma-4-E2B-it-unsloth-bnb-4bit`](https://huggingface.co/unsloth/gemma-4-E2B-it-unsloth-bnb-4bit)  
**Model Type:** Causal Language Model (LoRA Adapter)  
**Languages:** Hindi (Devanagari), English, Hinglish input → Hindi/English output  
**License:** Apache-2.0 (same as base)  
**Finetuned by:** Unsloth + PEFT (Hugging Face ecosystem)  

---

## Model Summary

StriSakhi ("Legal Companion") is a fine-tuned Gemma-4 2B Instruct model specialized as a **warm, authoritative legal guide for Indian women seeking rights-based information**. Unlike general-purpose LLMs, it is explicitly trained to:

- Respond in **simple, sister-like Hindi** (or English when requested)  
- Cite **actual Indian laws** with correct section numbers (DV Act 2005, POSH Act 2013, CrPC 125, etc.)  
- Structure every response into **5 mandatory blocks**: Empathy → Rights → Action Timeline → Helpline → Follow-up Question  
- Maintain **≥85% Devanagari purity** for Hindi sessions (no Roman script leakage)  
- Refuse to generate harmful advice (e.g., never suggests "compromise" in domestic violence cases)  

**Key Differentiator:** This is a *safety-first, rights-first* legal domain model with structured output conditioning baked into the LoRA weights via 549 curated conversational examples.

---

## Competition Results

| Benchmark | Score | Pass Rate |
|-----------|-------|-----------|
| **Overall (50 cases)** | **86.4%** | **43/50 (86%)** |
| Domestic Violence (10) | 91.2% | 9/10 |
| Property Rights (8) | 84.5% | 7/8 |
| Maintenance/Divorce (8) | 82.1% | 6/8 |
| Dowry Harassment (5) | 88.0% | 4/5 |
| Workplace/POSH (5) | 90.0% | 5/5 |
| Hinglish → Hindi (8) | 85.4% | 7/8 |
| Follow-up Short (6) | 79.2% | 5/6 |

*Benchmark: Custom 50-case legal evaluation suite covering 7 crime categories with automated checks for citation accuracy, Hindi purity, timeline structure, and hallucination resistance.*

---

## Intended Use

### Primary Use Cases
- **Legal intake chatbot** for NGOs and legal aid clinics serving women in India  
- **First-response information** for domestic violence, property rights, maintenance, dowry, and workplace harassment queries  
- **Hinglish-to-Hindi translation** with legal domain expertise (critical for Tier-2/3 India users)  
- **Follow-up Q&A** after initial legal guidance (short-form answers)  

### Out-of-Scope Use
- **Not a substitute for a licensed advocate.** Always directs users to NALSA (15100) and DLSA for actual representation.  
- **Not for emergency response.** Critical emergencies ("happening right now") are handled by a separate hardcoded detector upstream.  
- **Not for non-Indian jurisdictions.** Law citations are India-specific.  
- **Not for document drafting.** Provides guidance, not executable legal documents.  

---

## Training Details

### Hardware
| Spec | Value |
|------|-------|
| GPU | NVIDIA Tesla T4 (Kaggle) |
| VRAM | 14.5 GB |
| Training Time | ~35 minutes |
| Framework | Unsloth 2026.5.2 + Transformers 5.5.0 |

### Hyperparameters
| Parameter | Value |
|-----------|-------|
| Base Model | `unsloth/gemma-4-E2B-it-unsloth-bnb-4bit` |
| Method | LoRA (PEFT) |
| Rank (`r`) | 8 |
| Alpha (`lora_alpha`) | 8 |
| Dropout | 0.0 |
| Target Modules | Attention + MLP (vision frozen) |
| Sequence Length | 4096 |
| Quantization | 4-bit BnB (NF4) |
| Batch Size | 2 |
| Gradient Accumulation | 4 |
| Effective Batch Size | 8 |
| Learning Rate | 2e-4 |
| LR Scheduler | Linear |
| Warmup Steps | 5 |
| Epochs | 3 |
| Optimizer | AdamW 8-bit |
| Weight Decay | 0.001 |
| Seed | 42 |

### Dataset
- **Size:** 549 conversational examples  
- **Format:** ShareGPT-style JSONL with `conversations` array (system/user/assistant turns)  
- **Coverage:**  
  - Domestic Violence (DV Act 2005) — 35%  
  - Property / Inheritance — 20%  
  - Maintenance / Divorce — 20%  
  - Dowry / 498A — 10%  
  - Workplace / POSH Act — 10%  
  - Follow-up short answers — 5%  
- **Language Distribution:** 70% Hindi output, 20% English output, 10% Hinglish input → Hindi output  
- **Data Source:** Synthetic + manually curated legal scenarios based on actual case patterns from Indian district courts. No private user data.  

### Training Procedure
1. **Template Alignment:** Applied Gemma-4 non-thinking chat template to match production llama-server deployment  
2. **Label Masking:** System + user tokens masked as `-100` (ignored in loss); only assistant responses trained  
3. **BOS Deduplication:** Removed duplicate `<bos>` tokens introduced by processor  
4. **Marker-Based Splitting:** Used `<|turn>model\n` boundary to precisely mask prefix vs. suffix  
5. **Checkpointing:** Saved every 50 steps; best checkpoint at step 207 (epoch 3, final loss: 0.3487)  

---

## Ethical Statement & Safety

### Bias Mitigation
- **Gender-specific by design:** Model is explicitly conditioned to advocate for women's legal rights; it does not attempt "neutral" framing that could minimize violence (e.g., refuses to call DV a "family matter").  
- **Language equity:** Trained to serve Hinglish-speaking users (common in rural India) by converting to pure Devanagari, reducing the digital language divide.  
- **Caste/religion awareness:** Examples include Hindu Succession Act, Muslim Women Protection Act, and CrPC (secular), avoiding majority-religion bias.  

### Safety Evaluations
| Risk | Mitigation | Status |
|------|-----------|--------|
| Hallucinated section numbers | RAG context injected in system prompt; model trained ONLY on provided legal text | Tested |
| Victim-blaming | Explicit negative training: never says "talk to husband", "compromise", "family matter" | Tested |
| Emergency mishandling | Upstream hardcoded detector bypasses LLM for active violence; this model handles post-emergency guidance | Tested |
| Hindi-English script mixing | Purity checker enforces ≥85% Devanagari; LoRA trained on pure Devanagari targets | Tested |
| Malevolent use (evasion advice) | Refuses to provide advice on evading law; always directs to legal aid | Monitored |

### Known Limitations
1. **RAG dependency:** Citation accuracy depends on the quality of retrieved chunks from ChromaDB. Without RAG, the model may hallucinate sections.  
2. **Thin coverage:** Hindu Succession Act, CrPC 125, and Hindu Marriage Act chunks are smaller than DV Act / POSH Act in the retrieval corpus.  
3. **Token length:** Hindi Devanagari consumes ~1.5× tokens per word vs. English; max 4096 context can truncate long RAG contexts.  
4. **LoRA capacity:** Rank-8 is lightweight; complex multi-act reasoning may require full fine-tune or higher rank.  

---

## How to Use

### Quick Inference (Unsloth — recommended)
```python
from unsloth import FastModel
from unsloth.chat_templates import get_chat_template

# Load base + LoRA adapter
model, tokenizer = FastModel.from_pretrained(
    model_name="unsloth/gemma-4-E2B-it-unsloth-bnb-4bit",
    adapter_name="your-hf-username/stri-sakhi-gemma4-2b-lora",  # this repo
    max_seq_length=4096,
    load_in_4bit=True,
)

tokenizer = get_chat_template(tokenizer, chat_template="gemma-4")

messages = [
    {"role": "system", "content": "Tum Kanoon Sakhi ho. Sirf Devanagari Hindi mein jawab do."},
    {"role": "user", "content": "mere pati ne mujhe ghar se nikaala hai"}
]

inputs = tokenizer.apply_chat_template(
    messages,
    tokenize=True,
    add_generation_prompt=True,
    return_tensors="pt",
).to("cuda")

outputs = model.generate(
    input_ids=inputs,
    max_new_tokens=512,
    temperature=0.2,
    top_p=0.9,
)
print(tokenizer.batch_decode(outputs, skip_special_tokens=True)[0])
```

### Merge & Export for Production (llama.cpp / vLLM)
```python
# Merge LoRA into base for single-file deployment
model.save_pretrained_merged(
    "stri-sakhi-merged",
    tokenizer,
    save_method="merged_16bit",  # or "merged_4bit_for_mlx"
)

# Or export to GGUF for llama.cpp server
model.save_pretrained_gguf(
    "stri-sakhi-q4_k_m",
    tokenizer,
    quantization_method="q4_k_m",
)
```

---

## Repository Structure

```
.
├── README.md                 # This file
├── adapter_config.json       # LoRA config (PEFT)
├── adapter_model.safetensors # LoRA weights (~16 MB)
├── tokenizer/              # Tokenizer files (if customized)
├── benchmark_results.json  # 50-case evaluation raw results
├── training_logs.txt       # Loss curves per step
└── sample_inference.ipynb  # Reproducible inference demo
```

---

## Training Loss Curve

| Step | Loss |
|------|------|
| 10 | 2.373 |
| 50 | 0.315 |
| 100 | 0.162 |
| 150 | 0.130 |
| 200 | 0.123 |
| 207 (final) | 0.349* |

*Final epoch loss is higher than mid-epoch because the last batch contains harder, longer examples (property rights with multiple citations).*  

---

## Acknowledgements

- **Google DeepMind** for the Gemma-4 model family and open weights  
- **Unsloth** team for 2× faster, 50% memory-reduced fine-tuning  
- **Hugging Face** PEFT & Transformers libraries  
- **Kaggle** for Tesla T4 GPU access  
- **NALSA & DLSA** India for the legal aid framework this model promotes  

---

## Citation

If you use this model in research or production, please cite:

```bibtex
@misc{stri-sakhi-gemma4-2b-2026,
  title = {StriSakhi: A Safety-First Legal Advocate LLM for Indian Women},
  author = {shubendu biswas},
  year = {2026},
  howpublished = {\url{https://huggingface.co/your-username/stri-sakhi-gemma4-2b-lora}},
  note = {Fine-tuned Gemma-4 2B Instruct with LoRA for structured legal guidance}
}
```

**Base model citation:**
```bibtex
@article{gemma4-2026,
  title={Gemma 4: A family of highly capable multimodal models},
  author={Google DeepMind},
  year={2026}
}
```

---

## Disclaimer

This model provides **general legal information only** and does not constitute legal advice. It is not a substitute for a licensed advocate. Always contact **NALSA 15100** or your **District Legal Services Authority (DLSA)** for case-specific representation. The developers assume no liability for actions taken based on model outputs.

---

*Model card generated for Hugging Face Open Source AI Challenge — Women Safety & Empowerment Track.*