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Model: GSMS-B/Indian-Legal-Llama-3.2-3B
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---
language:
- en
license: apache-2.0
base_model: unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit
tags:
- legal
- indian-law
- BNS
- BNSS
- BSA
- criminal-law
- llama
- llama3
- 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 Llama 3.2 — 3B
<p align="center">
<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"/>
</p>
---
## 📖 Model Description
**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:
| 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 |
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.
---
## 🔗 Model Variants
| Variant | Repo | Best For |
|---|---|---|
| 🟢 **Merged (this repo)** | `GSMS-B/Indian-Legal-Llama-3.2-3B` | Out-of-the-box inference, Gradio/API deployment |
| 🔵 **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 |
| 🟡 **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 |
---
## 🚀 Quick Start
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
model_id = "GSMS-B/Indian-Legal-Llama-3.2-3B"
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-Llama-3.2-3B-GGUF
```
---
## 🎯 Recommended Use Cases
> ⚠️ **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.
### ✅ Where this model works well
| Use Case | How to Use |
|---|---|
| 🔍 **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 |
| 🤖 **Legal Chatbot Backend** | Combine with a document store of the actual act texts; use this model for generation with retrieved context |
| 📚 **Legal Education Tool** | Build Q&A apps for law students learning the new 2023 acts |
| 🔎 **Section Lookup Assistant** | Pair with a section index to quickly surface which section of BNS/BNSS/BSA applies to a given situation |
| 🧪 **Research & Experimentation** | Fine-tune further on specific sub-domains (e.g., only BNSS procedure, only BSA evidence rules) |
| 📝 **Structured Legal Summarization** | Summarize specific sections when given the section text as input context |
### ❌ Not recommended for
- Standalone legal advice without a retrieval component
- High-stakes legal decisions without human expert review
- Jurisdictions outside BNS / BNSS / BSA 2023 scope
---
## 🏋️ Training Details
| Property | Value |
|---|---|
| Base model | meta-llama/Llama-3.2-3B-Instruct |
| Fine-tuning method | QLoRA |
| LoRA rank | 64 |
| LoRA alpha | 128 |
| 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) | 4 |
| Learning rate | 2e-4 |
| Optimizer | adamw_8bit |
| Hardware | Google Colab T4 GPU |
| Framework | Unsloth + TRL SFTTrainer |
| Prompt format | ChatML |
---
## 📊 Training Data
| 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
---
## ⚠️ 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.*

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{{- bos_token }}
{%- if custom_tools is defined %}
{%- set tools = custom_tools %}
{%- endif %}
{%- if not tools_in_user_message is defined %}
{%- set tools_in_user_message = true %}
{%- endif %}
{%- if not date_string is defined %}
{%- if strftime_now is defined %}
{%- set date_string = strftime_now("%d %b %Y") %}
{%- else %}
{%- set date_string = "26 Jul 2024" %}
{%- endif %}
{%- endif %}
{%- if not tools is defined %}
{%- set tools = none %}
{%- endif %}
{#- This block extracts the system message, so we can slot it into the right place. #}
{%- if messages[0]['role'] == 'system' %}
{%- set system_message = messages[0]['content']|trim %}
{%- set messages = messages[1:] %}
{%- else %}
{%- set system_message = "" %}
{%- endif %}
{#- System message #}
{{- "<|start_header_id|>system<|end_header_id|>\n\n" }}
{%- if tools is not none %}
{{- "Environment: ipython\n" }}
{%- endif %}
{{- "Cutting Knowledge Date: December 2023\n" }}
{{- "Today Date: " + date_string + "\n\n" }}
{%- if tools is not none and not tools_in_user_message %}
{{- "You have access to the following functions. To call a function, please respond with JSON for a function call." }}
{{- 'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.' }}
{{- "Do not use variables.\n\n" }}
{%- for t in tools %}
{{- t | tojson(indent=4) }}
{{- "\n\n" }}
{%- endfor %}
{%- endif %}
{{- system_message }}
{{- "<|eot_id|>" }}
{#- Custom tools are passed in a user message with some extra guidance #}
{%- if tools_in_user_message and not tools is none %}
{#- Extract the first user message so we can plug it in here #}
{%- if messages | length != 0 %}
{%- set first_user_message = messages[0]['content']|trim %}
{%- set messages = messages[1:] %}
{%- else %}
{{- raise_exception("Cannot put tools in the first user message when there's no first user message!") }}
{%- endif %}
{{- '<|start_header_id|>user<|end_header_id|>\n\n' -}}
{{- "Given the following functions, please respond with a JSON for a function call " }}
{{- "with its proper arguments that best answers the given prompt.\n\n" }}
{{- 'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.' }}
{{- "Do not use variables.\n\n" }}
{%- for t in tools %}
{{- t | tojson(indent=4) }}
{{- "\n\n" }}
{%- endfor %}
{{- first_user_message + "<|eot_id|>"}}
{%- endif %}
{%- for message in messages %}
{%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %}
{{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' }}
{%- elif 'tool_calls' in message %}
{%- if not message.tool_calls|length == 1 %}
{{- raise_exception("This model only supports single tool-calls at once!") }}
{%- endif %}
{%- set tool_call = message.tool_calls[0].function %}
{{- '<|start_header_id|>assistant<|end_header_id|>\n\n' -}}
{{- '{"name": "' + tool_call.name + '", ' }}
{{- '"parameters": ' }}
{{- tool_call.arguments | tojson }}
{{- "}" }}
{{- "<|eot_id|>" }}
{%- elif message.role == "tool" or message.role == "ipython" %}
{{- "<|start_header_id|>ipython<|end_header_id|>\n\n" }}
{%- if message.content is mapping or message.content is iterable %}
{{- message.content | tojson }}
{%- else %}
{{- message.content }}
{%- endif %}
{{- "<|eot_id|>" }}
{%- endif %}
{%- endfor %}
{%- if add_generation_prompt %}
{{- '<|start_header_id|>assistant<|end_header_id|>\n\n' }}
{%- endif %}

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"architectures": [
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],
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"num_hidden_layers": 28,
"num_key_value_heads": 8,
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"pretraining_tp": 1,
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"tie_word_embeddings": true,
"unsloth_fixed": true,
"unsloth_version": "2026.6.9",
"use_cache": true,
"vocab_size": 128256
}

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