shivvamm/Meta-Llama-3.1-8B_16bit_indianlaw

Model Card

Legal LLM (Fine-Tuned on Indian Supreme Court Synthetic Q&A)

• Developed by: Shivam Pandey (shivvamm)
• License: apache-2.0
• Finetuned from: unsloth/meta-llama-3.1-8b-unsloth-bnb-4bit

This model is a fine-tuned LLaMA-3.1-8B version optimized using Unsloth for high-speed training and inference.
It was trained on a synthetic legal Q&A dataset derived from Indian Supreme Court case data, enabling the model to reason over legal facts, summarize judgments, and answer domain-specific legal queries.

Training was conducted using the Hugging Face TRL library with Unsloth’s accelerated training pipeline, achieving ~2× faster fine-tuning compared to standard methods.

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📘 Training Dataset

The training data consists of:

• Synthetic legal questions and answers generated from publicly available Supreme Court of India judgment texts.
• Structured Q&A style conversation format.
• Emphasis on:
  • Case summaries
  • Legal principles
  • Interpretations
  • Procedural details
  • Outcome classification

No private or confidential data was used.

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🧠 Model Capabilities

The model has been evaluated on test samples from the synthetic dataset and performs reliably on:

• Answering legal domain questions
• Summarizing court judgments
• Explaining legal concepts in simple language
• Classifying legal issues
• Extracting key principles from case texts

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⚡ Performance

Initial testing shows:

• Strong understanding of legal terminology
• Accurate reasoning within the context of Supreme Court judgments
• Smooth conversational and structured Q&A performance
• Improved logical consistency after fine-tuning

Further benchmarking (e.g., LawBench, Indian legal QA datasets) is planned.

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🚀 Fine-Tuning Details

• Framework: Unsloth + Hugging Face TRL
• Technique: PEFT / LoRA
• Precision: 4-bit (bnb-4bit)
• Training Speed: ~2× faster with Unsloth acceleration
• Training Style: Instruction-tuned Q&A format

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🗂 Use Cases

• Legal research assistance
• Court judgment summarization
• Law student Q&A assistant
• Domain-specific legal reasoning
• Automated drafting helpers (non-advisory)

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⚠️ Limitations & Disclaimer

• The model does not provide real legal advice.
• Outputs may contain inaccuracies and should not be used for professional legal decision-making.
• The dataset includes synthetically generated labels, which may introduce bias or hallucinations.

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❤️ Built With

This model was trained using:

Powered by Unsloth, LLaMA, and Hugging Face TRL.

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