Primary Task: Abstractive Summarization of Indian Court Proceedings
This model is a specialized fine-tune designed to address the massive backlog in the Indian judiciary by providing concise, high-quality summaries of complex legal documents.
🚀 Project Context: Legal Judgment Summarizer
This model serves as the core engine for a specialized summarization tool. It is trained to parse through:
Statutory Law: Identifying relevant IPC sections.
Case Law: Summarizing the ratio decidendi (reason for the decision).
Judicial Proceedings: Distilling long court transcripts into actionable abstracts.
📊 Training Data & Methodology
1. Dataset: Indian Legal Corpus (ILC)
The model was trained on the ILC dataset, which is unique for its highly abstractive human-written summaries.
Impact: Unlike standard datasets, ILC enables the model to understand the intent and context of a judgment rather than just its literal text.
2. Technical Efficiency (Unsloth & QLoRA)
Using Unsloth and 4-bit quantization, the model maintains high reasoning capabilities while being lightweight enough to run on consumer-grade hardware or budget cloud instances.
Acceleration: 2x faster training.
Memory Optimization: Reduced VRAM footprint through QLoRA (Rank-16/32 adapters).
🙏 Credits & Citations
ILC Dataset:Trivedi, P., et al. (2024). Indian Legal Corpus (ILC): A Dataset for Summarizing Indian Legal Proceedings using Natural Language. Engineered Science, 27, 1022.
Foundation:Meta AI for the Llama 3.1 architecture.
Framework:Unsloth AI for the fine-tuning efficiency.
🔧 Recommended Inference Settings
For best results in legal summarization, use:
Temperature: 0.3 (Lower temperature prevents "hallucinating" non-existent statutes).
Top_p: 0.9
Max Tokens: 1024 (To allow for detailed abstracts).
