This is a specialized miniature 'Legal AI' trained from scratch & fine-tuned on the 'Indian Penal Code (IPC)', 'CrPC', and 'Constitution'. It runs efficiently on consumer hardware (CPUs) using GGUF quantization.

⚠️ Limitations & Disclaimer

'Model Architecture:' This model uses GPT-2 custom configuration (defined from scratch) architecture. It is trained from scratch and not a direct fine-tune of the gpt2-small checkpoint, but utilizes the standard GPT2LMHeadModel structure for compatibility. It performs best on simple definition and punishment questions.
'Reasoning Limits:' Due to its small size, it is "NOT" capable of complex reasoning, multi-turn logic, or "lawyer-level" argumentation.
'Hallucinations:' Like all Small Language Models (SLMs), this model can "hallucinate" (generate plausible-sounding but incorrect information). 'Always verify specific section numbers and punishments against official legal texts.'
'Usage:' This is a research prototype for educational purposes. It is 'NOT' a substitute for professional legal advice.

📦 Model Details

'Architecture:' Custom GPT-2 configuration (Trained from scratch).
'Training Data:' Indian Legal Texts (IPC, CrPC, Constitution).
'Formats Included:'
- 'PyTorch:' Standard non-quantized weights for GPU inference or further fine-tuning (~500 MB).
- 'GGUF (Q8_0):' 8-bit quantized for fast CPU/Edge inference (~130 MB).

👨‍💻 Credits & Attribution

This model was trained by 'Subrit Dikshit'.

'Training Data:' Techmaestro369/indian-legal-texts-finetuning (CC BY-SA 4.0).
'Base Model:' Custom GPT-2 configuration (Trained from scratch).

🚀 How to Run (Demo Script)

This repository contains 'two versions' of the model. Choose the one that fits your needs.

🔧 Prerequisites

Python: 3.10 or 3.11 is recommended.
OS: Windows, macOS, or Linux.

Option 1: Run the PyTorch Version (Standard HF). Use this if you are using the standard transformers library or have a GPU.

'Requires:' pip install transformers torch

python

from transformers import GPT2LMHeadModel, GPT2Tokenizer

# 1. Load from Hugging Face
model_name = "subrit/subrit-legal-gpt2-quecto-v1"
tokenizer = GPT2Tokenizer.from_pretrained(model_name)
model = GPT2LMHeadModel.from_pretrained(model_name)

# 2. Ask a Question
input_text = "Question: What is Article 14 of the Constitution?\nAnswer:"
inputs = tokenizer(input_text, return_tensors="pt")

# 3. Generate Answer
outputs = model.generate(**inputs, max_new_tokens=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Option 2: Run the GGUF Version (Recommended for Speed/CPU)

Use this if you want to run the model on a laptop CPU without a GPU.

'Requires:' pip install llama-cpp-python huggingface_hub

python

from huggingface_hub import hf_hub_download
from llama_cpp import Llama

# 1. Download the GGUF file
model_path = hf_hub_download(
    repo_id="subrit/subrit-legal-gpt2-quecto-v1",
    filename="subrit_legal_gpt2_q8.gguf"
)

# 2. Load the Engine
llm = Llama(model_path=model_path, n_ctx=512, verbose=False)

# 3. Ask a Question
question = "What is the punishment for murder under Section 302?"
output = llm(f"Question: {question}\nAnswer:", max_tokens=60, stop=["Question:", "\n"])

print(output['choices'][0]['text'])

Please cite it as follows:

bibtex

@misc{dikshit2025legalgpt2,
  author = {Dikshit, Subrit},
  title = {Subrit's Legal AI (Quecto V1): A Quantized GPT-2 Fine-Tune on Indian Law},
  year = {2025},
  publisher = {Hugging Face},
  journal = {Hugging Face Model Hub},
  howpublished = {\url{[https://huggingface.co/subrit/subrit-legal-gpt2-quecto-v1](https://huggingface.co/subrit/subrit-legal-gpt2-quecto-v1)}}
}

Acknowledgements:

@dataset{indian_legal_texts,
  author = {Gupta, Akshat (Techmaestro369)},
  title = {Indian Legal Texts Finetuning Dataset},
  year = {2024},
  publisher = {Hugging Face},
  url = {[https://huggingface.co/datasets/Techmaestro369/indian-legal-texts-finetuning](https://huggingface.co/datasets/Techmaestro369/indian-legal-texts-finetuning)}
}

@article{radford2019language,
  title={Language Models are Unsupervised Multitask Learners},
  author={Radford, Alec and Wu, Jeffrey and Child, Rewon and Luan, David and Amodei, Dario and Sutskever, Ilya},
  year={2019}
}