Indian-Legal-Llama-3.2-3B-Adapter API & Inference Endpoint

📖 Model Description

Indian Legal Llama 3.2 — 3B (LoRA Adapter) contains the QLoRA adapter weights trained on top of unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit, domain-adapted for all 1,059 sections of India's three landmark 2023 criminal justice reform acts:

Table with columns: Act, Full Name, Replaces, Sections
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.

🔗 Model Family — Llama 3.2 3B

Table with columns: Variant, Repo, Best For
Variant	Repo	Best For
🟢 Merged	GSMS-B/Indian-Legal-Llama-3.2-3B	Out-of-the-box inference, Gradio / API deployment
🔵 LoRA Adapter (this repo)	`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	CPU inference via Ollama / llama.cpp

🚀 Quick Start

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

base_model_id = "unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit"
adapter_id    = "GSMS-B/Indian-Legal-Llama-3.2-3B-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?"))

🎯 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

Table with columns: Use Case, 💡 How to Use
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
🧩 Flexible Adapter Stacking	Swap this adapter onto the base model without maintaining a full merged checkpoint — ideal for multi-adapter experiments
📚 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)

❌ 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

Table with columns: Property, Value
Property	Value
🤖 Base model	`unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit`
🔧 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

📊 Training Dataset

Table with columns: 📂 Dataset, 🔗 Link
📂 Dataset	🔗 Link
Indian Legal QA — BNS + BNSS + BSA 2023	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

⚠️ 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 for training efficiency.

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

Variant

Repo

Best For

🟢 Merged

GSMS-B/Indian-Legal-Llama-3.2-3B

Out-of-the-box inference, Gradio / API deployment

🔵 LoRA Adapter (this repo)

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

CPU inference via Ollama / llama.cpp

python

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

base_model_id = "unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit"
adapter_id    = "GSMS-B/Indian-Legal-Llama-3.2-3B-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?"))

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

🧩 Flexible Adapter Stacking

Swap this adapter onto the base model without maintaining a full merged checkpoint — ideal for multi-adapter experiments

📚 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)

Property

Value

🤖 Base model

unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit

🔧 Fine-tuning method

QLoRA

🎛️ LoRA rank

🎛️ 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

📦 Batch size (effective)

📂 Dataset

🔗 Link

Indian Legal QA — BNS + BNSS + BSA 2023

GSMS-B/Indian-Legal-QA-BNS-BNSS-BSA

Indian-Legal-Llama-3.2-3B-Adapter

README

📖 Model Description

🔗 Model Family — Llama 3.2 3B

🚀 Quick Start

🎯 Recommended Use Cases

✅ Where this model excels

❌ Not recommended for

🏋️ Training Details

📊 Training Dataset

👤 Author

⚠️ Disclaimer

Explore FriendliAI today

README

📖 Model Description

🔗 Model Family — Llama 3.2 3B

🚀 Quick Start

🎯 Recommended Use Cases

✅ Where this model excels

❌ Not recommended for

🏋️ Training Details

📊 Training Dataset

👤 Author

⚠️ Disclaimer