airesearch/LLaMa3.1-8B-Legal-ThaiCCL-Combine
Hugging Face
TEXT GENERATIONConcurrency Cost:1Model Size:8BQuant:FP8Ctx Length:32kArchitecture:Transformer0.0K Warm

airesearch/LLaMa3.1-8B-Legal-ThaiCCL-Combine is an 8 billion parameter large language model built upon Meta's Llama 3.1 architecture, specifically fine-tuned for answering Thai legal questions. This model is optimized for use with Retrieval-Augmented Generation (RAG) systems, referencing legal documents to provide accurate responses. Its primary differentiator is its robust performance in Thai legal question-answering, particularly when handling both relevant and irrelevant context information.

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Overview

LLaMa3.1-8B-Legal-ThaiCCL-Combine is an 8 billion parameter model developed by airesearch, fine-tuned from Meta's Llama 3.1. It is specifically designed to address Thai legal questions, leveraging the WangchanX Thai Legal dataset for its training. A key aspect of its development involved full fine-tuning using the WangchanX Finetuning pipeline.

Key Capabilities

  • Thai Legal Question Answering: Excels at providing legally informed answers in Thai, referencing relevant law sections and associated details like punishments or fees.
  • RAG System Integration: Optimized for use with Retrieval-Augmented Generation (RAG) systems, designed to process retrieved legal documents to formulate responses.
  • Robust Context Handling: Uniquely trained with both positive and negative contexts, making it more resilient to scenarios where a RAG system might provide irrelevant information alongside correct context.
  • Specific Prompt Format: Designed to work with a predefined system prompt and question template, ensuring consistent and accurate legal advice.

When to Use This Model

  • Legal Assistance Applications: Ideal for building AI assistants that provide legal advice in Thai, such as the 'Sommai' persona described in the prompt format.
  • RAG-based Legal Systems: Suitable for integration into RAG pipelines where the model needs to synthesize answers from retrieved legal texts.
  • Handling Noisy Contexts: Particularly beneficial in environments where the quality of retrieved documents might vary, as its training on mixed contexts enhances its ability to filter and utilize relevant information effectively.
Popular Sampler Settings

Top 3 parameter combinations used by Featherless users for this model. Click a tab to see each config.

temperature
top_p
top_k
frequency_penalty
presence_penalty
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