Indian-Legal-Qwen2.5-3B-Adapter

⚖️🐉 Indian Legal Qwen 2.5 — 3B · LoRA Adapter

<p align="center"> <img src="https://img.shields.io/badge/Base%20Model-Qwen%202.5%203B-6366F1?style=for-the-badge" alt="Base Model"/> <img src="https://img.shields.io/badge/Type-LoRA%20Adapter-0EA5E9?style=for-the-badge" alt="Type"/> <img src="https://img.shields.io/badge/Domain-Indian%20Criminal%20Law-DC2626?style=for-the-badge" alt="Domain"/> <img src="https://img.shields.io/badge/Method-QLoRA-2563EB?style=for-the-badge" alt="Method"/> <img src="https://img.shields.io/badge/Acts-BNS%20%7C%20BNSS%20%7C%20BSA-16A34A?style=for-the-badge" alt="Acts"/> <img src="https://img.shields.io/badge/License-Apache%202.0-F59E0B?style=for-the-badge" alt="License"/> </p>

🔵 This is the lightweight LoRA Adapter — load it on top of the base model for flexible, memory-efficient inference. For plug-and-play usage, see the Merged Model.

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📖 Model Description

Indian Legal Qwen 2.5 — 3B (LoRA Adapter) contains the QLoRA adapter weights trained on top of unsloth/Qwen2.5-3B-Instruct-bnb-4bit, domain-adapted for all 1,059 sections of India's three landmark 2023 criminal justice reform acts:

Trained on 6,354 instruction-format QA pairs — 6 question types per section covering definitions, scenarios, legal elements, exceptions, and consequences.

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🔗 Model Family — Qwen 2.5 3B

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🚀 Quick Start

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
import torch

base_model_id = "unsloth/Qwen2.5-3B-Instruct-bnb-4bit"
adapter_id    = "GSMS-B/Indian-Legal-Qwen2.5-3B-Adapter"

tokenizer  = AutoTokenizer.from_pretrained(base_model_id)
base_model = AutoModelForCausalLM.from_pretrained(
    base_model_id,
    torch_dtype=torch.float16,
    device_map="auto"
)
model = PeftModel.from_pretrained(base_model, adapter_id)

SYSTEM = "You are an expert legal assistant specializing in Indian criminal law — BNS, BNSS, and BSA 2023."

def ask(question):
    messages = [
        {"role": "system", "content": SYSTEM},
        {"role": "user",   "content": question}
    ]
    text   = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
    inputs = tokenizer(text, return_tensors="pt").to(model.device)
    with torch.no_grad():
        out = model.generate(**inputs, max_new_tokens=300, temperature=0.1,
                             do_sample=True, pad_token_id=tokenizer.eos_token_id)
    return tokenizer.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True)

print(ask("What are the key differences between BNS 2023 and IPC 1860?"))

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🎯 Recommended Use Cases

⚠️ Important Note: This model has been domain-adapted on structured QA data and works best as a component in a larger pipeline rather than a standalone answer engine. Direct usage without retrieval context may produce incomplete or imprecise answers on complex legal queries.

✅ Where this model excels

❌ Not recommended for

• Standalone legal advice without a retrieval component
• High-stakes legal decisions without qualified human review
• Jurisdictions or acts outside BNS / BNSS / BSA 2023

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🏋️ Training Details

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📊 Training Dataset

6 question types per section: definitional_topic · definitional_section · scenario · elements · exceptions · consequence

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👤 Author

GSMS-B — Bugatha Ganasyam Mani Sankar 🤗 Hugging Face Profile

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⚠️ Disclaimer

This model is intended for research and educational purposes only. It does not constitute legal advice. Outputs should not be relied upon for any legal decision without review by a qualified legal professional. The model's responses reflect patterns in training data and may contain errors or omissions.

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⚡ Fine-tuned using Unsloth for training efficiency.