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Model: GSMS-B/Indian-Legal-Qwen2.5-3B Source: Original Platform
2026-06-27 19:58:16 +08:00
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+---
+language:
+- en
+license: apache-2.0
+base_model: unsloth/Qwen2.5-1.5B-Instruct
+tags:
+- legal
+- indian-law
+- BNS
+- BNSS
+- BSA
+- criminal-law
+- qwen
+- qwen2.5
+- qlora
+- unsloth
+- domain-adaptation
+- instruction-tuning
+- question-answering
+- law
+- india
+datasets:
+- GSMS-B/Indian-Legal-QA-BNS-BNSS-BSA
+pipeline_tag: text-generation
+---
+
+# ⚖️🐉 Indian Legal Qwen 2.5 — 3B
+
+<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-Merged%20Model-22C55E?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 fully merged model** — ready for out-of-the-box inference with no extra setup. For lightweight adapter loading see the [Adapter](https://huggingface.co/GSMS-B/Indian-Legal-Qwen2.5-3B-Adapter) · For CPU/Ollama usage see the [GGUF](https://huggingface.co/GSMS-B/Indian-Legal-Qwen2.5-3B-GGUF).
+
+---
+
+## 📖 Model Description
+
+**Indian Legal Qwen 2.5 — 3B** is a domain-adapted version of [`unsloth/Qwen2.5-3B-Instruct-bnb-4bit`](https://huggingface.co/unsloth/Qwen2.5-3B-Instruct-bnb-4bit), fine-tuned using QLoRA on a structured question-answer dataset covering all **1,059 sections** of India's three landmark 2023 criminal justice reform acts:
+
+| Act | Full Name | Replaces | Sections |
+|---|---|---|---|
+| 📕 **BNS 2023** | Bharatiya Nyaya Sanhita | IPC 1860 | 358 |
+| 📗 **BNSS 2023** | Bharatiya Nagarik Suraksha Sanhita | CrPC 1973 | 531 |
+| 📘 **BSA 2023** | Bharatiya Sakshya Adhiniyam | Indian Evidence Act 1872 | 170 |
+
+Trained on **6,354 instruction-format QA pairs** — 6 question types per section covering definitions, scenarios, legal elements, exceptions, and consequences — giving it broad, structured coverage of India's reformed criminal law framework.
+
+---
+
+## 🔗 Model Family — Qwen 2.5 3B
+
+| Variant | Repo | Best For |
+|---|---|---|
+| 🟢 **Merged (this repo)** | `GSMS-B/Indian-Legal-Qwen2.5-3B` | Out-of-the-box inference, Gradio / API deployment |
+| 🔵 **LoRA Adapter** | [GSMS-B/Indian-Legal-Qwen2.5-3B-Adapter](https://huggingface.co/GSMS-B/Indian-Legal-Qwen2.5-3B-Adapter) | Lightweight loading on top of base model |
+| 🟡 **GGUF (Quantized)** | [GSMS-B/Indian-Legal-Qwen2.5-3B-GGUF](https://huggingface.co/GSMS-B/Indian-Legal-Qwen2.5-3B-GGUF) | CPU inference via Ollama / llama.cpp |
+
+---
+
+## 🚀 Quick Start
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+model_id  = "GSMS-B/Indian-Legal-Qwen2.5-3B"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model     = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype=torch.float16,
+    device_map="auto"
+)
+
+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 is a Zero FIR under BNSS 2023?"))
+```
+
+### 💻 Run locally with Ollama (GGUF)
+```bash
+ollama run hf.co/GSMS-B/Indian-Legal-Qwen2.5-3B-GGUF
+```
+
+---
+
+## 🎯 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
+
+| Use Case | 💡 How to Use |
+|---|---|
+| 🔍 **RAG Pipeline** | Pair with a BM25 or vector retriever over BNS/BNSS/BSA texts; feed retrieved sections as context for grounded, citation-backed answers |
+| 🤖 **Legal Chatbot Backend** | Use as the generation backbone of a legal assistant app with a ChromaDB / FAISS document store |
+| 📚 **Legal Education Tool** | Build interactive Q&A apps for law students and practitioners learning the 2023 criminal justice reforms |
+| 🔎 **Section Lookup Assistant** | Combine with a section index to surface the exact BNS / BNSS / BSA provision relevant to a given situation |
+| 🧪 **Further Fine-tuning** | Use as a starting point for more specialised adaptation (e.g., only BNSS procedure, only BSA evidence rules) |
+| 📝 **Structured Legal Summarization** | Summarize individual sections when the section text is supplied as input context |
+| 🏛️ **Legal NLP Research** | Benchmark Indian criminal law understanding across model families (Qwen vs Llama) |
+| ⚖️ **Comparative Law Analysis** | Highlight differences between old acts (IPC/CrPC/IEA) and their 2023 replacements |
+
+### ❌ 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
+
+---
+
+## 🏋️ Training Details
+
+| Property | Value |
+|---|---|
+| 🤖 Base model | `unsloth/Qwen2.5-3B-Instruct-bnb-4bit` |
+| 🔧 Fine-tuning method | QLoRA |
+| 🎛️ LoRA rank | 16 |
+| 🎛️ LoRA alpha | 16 |
+| 🧩 Target modules | q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj |
+| 📊 Training data | 6,354 QA pairs — 1,059 sections × 6 question types |
+| 🔁 Epochs | 3 |
+| 📦 Batch size (effective) | 2 |
+| 📈 Learning rate | 2e-4 |
+| ⚙️ Optimizer | adamw_8bit |
+| 💻 Hardware | Google Colab T4 GPU |
+| 🛠️ Framework | Unsloth + TRL SFTTrainer |
+| 💬 Prompt format | ChatML |
+
+---
+
+## 📊 Training Dataset
+
+| 📂 Dataset | 🔗 Link |
+|---|---|
+| Indian Legal QA — BNS + BNSS + BSA 2023 | [GSMS-B/Indian-Legal-QA-BNS-BNSS-BSA](https://huggingface.co/datasets/GSMS-B/Indian-Legal-QA-BNS-BNSS-BSA) |
+
+**6 question types per section:**
+`definitional_topic` · `definitional_section` · `scenario` · `elements` · `exceptions` · `consequence`
+
+---
+
+## 👤 Author
+
+**GSMS-B** — Bugatha Ganasyam Mani Sankar
+🤗 [Hugging Face Profile](https://huggingface.co/GSMS-B)
+
+---
+
+## ⚠️ 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.
+
+---
+
+*⚡ Fine-tuned using [Unsloth](https://github.com/unslothai/unsloth) for training efficiency.*