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Model: GSMS-B/Indian-Legal-Llama-3.2-3B Source: Original Platform
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---
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language:
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- en
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license: apache-2.0
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base_model: unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit
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tags:
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- legal
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- indian-law
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- BNS
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- BNSS
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- BSA
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- criminal-law
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- llama
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- llama3
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- qlora
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- unsloth
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- domain-adaptation
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- instruction-tuning
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- question-answering
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- law
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- india
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datasets:
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- GSMS-B/Indian-Legal-QA-BNS-BNSS-BSA
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pipeline_tag: text-generation
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---
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# ⚖️ Indian Legal Llama 3.2 — 3B
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<p align="center">
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<img src="https://img.shields.io/badge/Base%20Model-Llama%203.2%203B-7C3AED?style=for-the-badge&logo=meta" alt="Base Model"/> <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"/>
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</p>
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---
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## 📖 Model Description
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**Indian Legal Llama 3.2 — 3B** is a domain-adapted version of [`unsloth/Llama-3.2-3B-Instruct`](https://huggingface.co/unsloth/Llama-3.2-3B-Instruct), fine-tuned using QLoRA on a structured question-answer dataset covering all 1,059 sections of India's three 2023 criminal justice acts:
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| Act | Full Name | Replaces | Sections |
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|---|---|---|---|
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| 📕 **BNS 2023** | Bharatiya Nyaya Sanhita | IPC 1860 | 358 |
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| 📗 **BNSS 2023** | Bharatiya Nagarik Suraksha Sanhita | CrPC 1973 | 531 |
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| 📘 **BSA 2023** | Bharatiya Sakshya Adhiniyam | Indian Evidence Act 1872 | 170 |
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The model was trained on **6,354 instruction-format QA pairs** — 6 questions per section covering definitions, scenarios, legal elements, exceptions, and consequences — giving it broad coverage of Indian criminal law provisions.
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---
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## 🔗 Model Variants
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| Variant | Repo | Best For |
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|---|---|---|
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| 🟢 **Merged (this repo)** | `GSMS-B/Indian-Legal-Llama-3.2-3B` | Out-of-the-box inference, Gradio/API deployment |
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| 🔵 **LoRA Adapter** | [GSMS-B/Indian-Legal-Llama-3.2-3B-Adapter](https://huggingface.co/GSMS-B/Indian-Legal-Llama-3.2-3B-Adapter) | Lightweight loading on top of base model |
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| 🟡 **GGUF (Quantized)** | [GSMS-B/Indian-Legal-Llama-3.2-3B-GGUF](https://huggingface.co/GSMS-B/Indian-Legal-Llama-3.2-3B-GGUF) | CPU inference via Ollama / llama.cpp |
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---
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## 🚀 Quick Start
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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import torch
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model_id = "GSMS-B/Indian-Legal-Llama-3.2-3B"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(
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model_id,
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torch_dtype=torch.float16,
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device_map="auto"
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)
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SYSTEM = "You are an expert legal assistant specializing in Indian criminal law — BNS, BNSS, and BSA 2023."
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def ask(question):
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messages = [
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{"role": "system", "content": SYSTEM},
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{"role": "user", "content": question}
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]
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text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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inputs = tokenizer(text, return_tensors="pt").to(model.device)
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with torch.no_grad():
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out = model.generate(**inputs, max_new_tokens=300, temperature=0.1,
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do_sample=True, pad_token_id=tokenizer.eos_token_id)
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return tokenizer.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True)
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print(ask("What is a Zero FIR under BNSS 2023?"))
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```
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### 💻 Run locally with Ollama (GGUF)
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```bash
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ollama run hf.co/GSMS-B/Indian-Legal-Llama-3.2-3B-GGUF
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```
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---
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## 🎯 Recommended Use Cases
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> ⚠️ **Important Note:** This model has been domain-adapted on structured QA data and works best when used as a component in a larger system rather than as a standalone answer engine. Direct usage may produce incomplete or imprecise answers on complex legal queries.
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### ✅ Where this model works well
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| Use Case | How to Use |
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|---|---|
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| 🔍 **RAG (Retrieval-Augmented Generation)** | Use a retriever (BM25, vector search) to fetch relevant BNS/BNSS/BSA sections, then pass to this model as context for grounded answers |
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| 🤖 **Legal Chatbot Backend** | Combine with a document store of the actual act texts; use this model for generation with retrieved context |
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| 📚 **Legal Education Tool** | Build Q&A apps for law students learning the new 2023 acts |
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| 🔎 **Section Lookup Assistant** | Pair with a section index to quickly surface which section of BNS/BNSS/BSA applies to a given situation |
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| 🧪 **Research & Experimentation** | Fine-tune further on specific sub-domains (e.g., only BNSS procedure, only BSA evidence rules) |
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| 📝 **Structured Legal Summarization** | Summarize specific sections when given the section text as input context |
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### ❌ Not recommended for
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- Standalone legal advice without a retrieval component
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- High-stakes legal decisions without human expert review
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- Jurisdictions outside BNS / BNSS / BSA 2023 scope
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---
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## 🏋️ Training Details
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| Property | Value |
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|---|---|
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| Base model | meta-llama/Llama-3.2-3B-Instruct |
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| Fine-tuning method | QLoRA |
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| LoRA rank | 64 |
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| LoRA alpha | 128 |
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| Target modules | q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj |
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| Training data | 6,354 QA pairs — 1,059 sections × 6 question types |
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| Epochs | 3 |
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| Batch size (effective) | 4 |
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| Learning rate | 2e-4 |
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| Optimizer | adamw_8bit |
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| Hardware | Google Colab T4 GPU |
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| Framework | Unsloth + TRL SFTTrainer |
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| Prompt format | ChatML |
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---
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## 📊 Training Data
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| Dataset | Link |
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|---|---|
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| 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) |
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6 question types per section: `definitional_topic` · `definitional_section` · `scenario` · `elements` · `exceptions` · `consequence`
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---
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## 👤 Author
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**GSMS-B** — Bugatha Ganasyam Mani Sankar
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---
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## ⚠️ Disclaimer
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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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---
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*Fine-tuned using [Unsloth](https://github.com/unslothai/unsloth) for training efficiency.*
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93
chat_template.jinja
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chat_template.jinja
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{{- bos_token }}
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{%- if custom_tools is defined %}
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{%- set tools = custom_tools %}
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{%- endif %}
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{%- if not tools_in_user_message is defined %}
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{%- set tools_in_user_message = true %}
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{%- endif %}
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{%- if not date_string is defined %}
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{%- if strftime_now is defined %}
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{%- set date_string = strftime_now("%d %b %Y") %}
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{%- else %}
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{%- set date_string = "26 Jul 2024" %}
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{%- endif %}
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{%- endif %}
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{%- if not tools is defined %}
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{%- set tools = none %}
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{%- endif %}
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{#- This block extracts the system message, so we can slot it into the right place. #}
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{%- if messages[0]['role'] == 'system' %}
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{%- set system_message = messages[0]['content']|trim %}
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{%- set messages = messages[1:] %}
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{%- else %}
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{%- set system_message = "" %}
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{%- endif %}
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{#- System message #}
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{{- "<|start_header_id|>system<|end_header_id|>\n\n" }}
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{%- if tools is not none %}
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{{- "Environment: ipython\n" }}
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{%- endif %}
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{{- "Cutting Knowledge Date: December 2023\n" }}
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{{- "Today Date: " + date_string + "\n\n" }}
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{%- if tools is not none and not tools_in_user_message %}
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{{- "You have access to the following functions. To call a function, please respond with JSON for a function call." }}
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{{- 'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.' }}
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{{- "Do not use variables.\n\n" }}
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{%- for t in tools %}
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{{- t | tojson(indent=4) }}
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{{- "\n\n" }}
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{%- endfor %}
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{%- endif %}
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{{- system_message }}
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{{- "<|eot_id|>" }}
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{#- Custom tools are passed in a user message with some extra guidance #}
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{%- if tools_in_user_message and not tools is none %}
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{#- Extract the first user message so we can plug it in here #}
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{%- if messages | length != 0 %}
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{%- set first_user_message = messages[0]['content']|trim %}
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{%- set messages = messages[1:] %}
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{%- else %}
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{{- raise_exception("Cannot put tools in the first user message when there's no first user message!") }}
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{%- endif %}
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{{- '<|start_header_id|>user<|end_header_id|>\n\n' -}}
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{{- "Given the following functions, please respond with a JSON for a function call " }}
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{{- "with its proper arguments that best answers the given prompt.\n\n" }}
|
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{{- 'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.' }}
|
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{{- "Do not use variables.\n\n" }}
|
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{%- for t in tools %}
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{{- t | tojson(indent=4) }}
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{{- "\n\n" }}
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{%- endfor %}
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{{- first_user_message + "<|eot_id|>"}}
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{%- endif %}
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{%- for message in messages %}
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{%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %}
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{{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' }}
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{%- elif 'tool_calls' in message %}
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{%- if not message.tool_calls|length == 1 %}
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{{- raise_exception("This model only supports single tool-calls at once!") }}
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{%- endif %}
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{%- set tool_call = message.tool_calls[0].function %}
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{{- '<|start_header_id|>assistant<|end_header_id|>\n\n' -}}
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{{- '{"name": "' + tool_call.name + '", ' }}
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{{- '"parameters": ' }}
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{{- tool_call.arguments | tojson }}
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{{- "}" }}
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{{- "<|eot_id|>" }}
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{%- elif message.role == "tool" or message.role == "ipython" %}
|
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{{- "<|start_header_id|>ipython<|end_header_id|>\n\n" }}
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{%- if message.content is mapping or message.content is iterable %}
|
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{{- message.content | tojson }}
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{%- else %}
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{{- message.content }}
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{%- endif %}
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{{- "<|eot_id|>" }}
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{%- endif %}
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{%- endfor %}
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{%- if add_generation_prompt %}
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{{- '<|start_header_id|>assistant<|end_header_id|>\n\n' }}
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{%- endif %}
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37
config.json
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config.json
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{
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"architectures": [
|
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"LlamaForCausalLM"
|
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
|
||||
"bos_token_id": 128000,
|
||||
"torch_dtype": "float16",
|
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"eos_token_id": 128009,
|
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"head_dim": 128,
|
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"hidden_act": "silu",
|
||||
"hidden_size": 3072,
|
||||
"initializer_range": 0.02,
|
||||
"intermediate_size": 8192,
|
||||
"max_position_embeddings": 131072,
|
||||
"mlp_bias": false,
|
||||
"model_type": "llama",
|
||||
"num_attention_heads": 24,
|
||||
"num_hidden_layers": 28,
|
||||
"num_key_value_heads": 8,
|
||||
"pad_token_id": 128004,
|
||||
"pretraining_tp": 1,
|
||||
"rms_norm_eps": 1e-05,
|
||||
"rope_parameters": {
|
||||
"factor": 32.0,
|
||||
"high_freq_factor": 4.0,
|
||||
"low_freq_factor": 1.0,
|
||||
"original_max_position_embeddings": 8192,
|
||||
"rope_theta": 500000.0,
|
||||
"rope_type": "llama3"
|
||||
},
|
||||
"tie_word_embeddings": true,
|
||||
"unsloth_fixed": true,
|
||||
"unsloth_version": "2026.6.9",
|
||||
"use_cache": true,
|
||||
"vocab_size": 128256
|
||||
}
|
||||
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generation_config.json
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generation_config.json
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{
|
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"bos_token_id": 128000,
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"do_sample": true,
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"eos_token_id": [
|
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128001,
|
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128008,
|
||||
128009
|
||||
],
|
||||
"max_length": 131072,
|
||||
"pad_token_id": 128004,
|
||||
"temperature": 0.6,
|
||||
"top_p": 0.9,
|
||||
"transformers_version": "5.12.1"
|
||||
}
|
||||
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model-00001-of-00002.safetensors
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{
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"metadata": {
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"total_size": 6425499648
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}
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||||
BIN
tokenizer.json
(Stored with Git LFS)
Normal file
BIN
tokenizer.json
(Stored with Git LFS)
Normal file
Binary file not shown.
2070
tokenizer_config.json
Normal file
2070
tokenizer_config.json
Normal file
File diff suppressed because it is too large
Load Diff
Reference in New Issue
Block a user