🧠 FlamingNeuron / llama381binstruct_summarize_short_merged

This is a merged model based on NousResearch/Meta-Llama-3.1-8B-Instruct, fine-tuned using LoRA adapters for legal-domain summarization. The LoRA weights have been merged with the base model for standalone use.

🔍 Task

This model converts legalese into short, human-readable summaries, based on data from the legal_summarization project.

💡 Example Usage

For complete setup instructions and working inference examples, see:
👉 GitHub Repo: LLaMA3-demo

This model expects Meta-style structured prompts with two fields: original_text and reference_summary. The original_text contains the input passage, and the model generates a summary in place of the empty reference_summary.

🏋️ Training Procedure

This model was trained using Supervised Fine-Tuning (SFT) on legal document summaries using the legal_summarization dataset. LoRA adapters were applied during training and merged afterward using merge_and_unload().

⚙️ Framework Versions

• TRL: 0.16.1
• Transformers: 4.51.3
• PyTorch: 2.6.0+cu124
• Datasets: 3.5.0
• Tokenizers: 0.21.1

📚 Citations

This model was fine-tuned using TRL.

⚖️ Legal Notice

This model builds on Meta’s LLaMA 3.1 architecture and is governed by the LLaMA 3.1 Community License. All use must comply with Meta’s acceptable use policy.

It was fine-tuned using the legal_summarization dataset for research and educational purposes only.

This model is not intended for commercial use exceeding the limitations described in the Meta license (e.g. more than 700M monthly active users).