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Model: subrit/subrit-legal-gpt2-quecto-v1
Source: Original Platform

.gitattributes

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README.md

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+---
+license: cc-by-sa-4.0
+language:
+- en
+library_name: transformers
+tags:
+- legal
+- india
+- law
+- gpt2
+- gguf
+- quecto
+---
+
+# ⚖️ Subrit's Legal AI (Quecto V1)
+
+'Model:' `subrit-legal-gpt2-quecto-v1`  
+'Author:' Subrit Dikshit  
+'License:' CC BY-SA 4.0  
+
+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](https://huggingface.co/datasets/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}
+}
+```

added_tokens.json

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config.json

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+{
+  "activation_function": "gelu_new",
+  "architectures": [
+    "GPT2LMHeadModel"
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+  "layer_norm_epsilon": 1e-05,
+  "model_type": "gpt2",
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+  "n_embd": 768,
+  "n_head": 12,
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+  "summary_proj_to_labels": true,
+  "summary_type": "cls_index",
+  "summary_use_proj": true,
+  "task_specific_params": {
+    "text-generation": {
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+    }
+  },
+  "transformers_version": "4.57.6",
+  "use_cache": true,
+  "vocab_size": 50258
+}

generation_config.json

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+}

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special_tokens_map.json

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tokenizer_config.json

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