trionohidayat/qwen-3b-legal-indo-rag-grpo
The trionohidayat/qwen-3b-legal-indo-rag-grpo is a 3.1 billion parameter Qwen2-based causal language model developed by trionohidayat. It is specifically fine-tuned for legal Indonesian RAG (Retrieval Augmented Generation) tasks, leveraging a 32768 token context length. This model is optimized for legal information retrieval and generation within the Indonesian context, offering specialized performance for legal applications.
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Model Overview
The trionohidayat/qwen-3b-legal-indo-rag-grpo is a 3.1 billion parameter Qwen2 model, developed by trionohidayat. It has been fine-tuned with a focus on legal Indonesian Retrieval Augmented Generation (RAG) tasks, making it suitable for specialized applications requiring legal context in the Indonesian language. The model benefits from a substantial context length of 32768 tokens, allowing it to process and understand extensive legal documents.
Key Characteristics
- Base Model: Qwen2 architecture.
- Parameter Count: 3.1 billion parameters.
- Context Length: 32768 tokens, enabling processing of long legal texts.
- Specialization: Fine-tuned for legal Indonesian RAG, indicating proficiency in retrieving and generating legal information relevant to Indonesia.
- Training Efficiency: The model was trained using Unsloth and Huggingface's TRL library, which facilitated a 2x faster training process.
Intended Use Cases
This model is particularly well-suited for:
- Legal Information Retrieval: Assisting in finding specific legal clauses, precedents, or regulations within Indonesian legal documents.
- Legal Document Analysis: Summarizing or extracting key information from Indonesian legal texts.
- Legal Question Answering: Providing answers to legal queries based on retrieved Indonesian legal data.
- Legal Research: Supporting researchers and legal professionals in navigating Indonesian legal frameworks.