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Llama 3 Patronus Lynx 8B Instruct V1.1
Overview :
Patronus-Lynx-8B-Instruct-v1.1 is a fine-tuned version of the meta-llama/Meta-Llama-3.1-8B-Instruct model, specifically designed to detect hallucinations in RAG setups. The model has been trained on multiple datasets, including CovidQA, PubmedQA, DROP, and RAGTruth, incorporating both manually annotated and synthetic data. It assesses whether the given document, question, and answer are faithful to the document content, without providing new information beyond the document scope or contradicting the document information.
Target Users :
The target audience includes researchers, developers, and enterprises seeking a reliable model to evaluate and generate text that is faithful to the source documents. This model is suitable for applications such as natural language processing, text summarization, question-answering systems, and chatbots.
Use Cases
Researchers use the model to assess the accuracy of answers in medical literature.
Developers integrate the model into question-answering systems to provide document-based accurate responses.
Enterprises utilize the model to check for information consistency in financial reports.
Features
Hallucination detection: Evaluate whether the answer is faithful to the provided document.
Text generation: Generate answers based on user-input questions and documents.
Conversational training format: The model is trained in a conversational format, making it suitable for dialogue applications.
Multi-dataset training: Includes training on datasets such as CovidQA, PubmedQA, DROP, and RAGTruth.
Long sequence processing: Supports sequences with a maximum length of 128,000 tokens.
Open-source license: Distributed under the cc-by-nc-4.0 license, allowing for free use and modification.
High performance: Performs excellently in multiple benchmark tests, such as HaluEval and RAGTruth.
How to Use
1. Prepare the input data consisting of questions, documents, and answers.
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2. Organize the input data using the prompt format recommended by the model.
3. Call the Hugging Face pipeline interface, passing in the model name and configuration parameters.
4. Pass the prepared data as user messages to the pipeline.
5. Obtain the model output, including a 'PASS' or 'FAIL' score and inference.
6. Analyze the model output to determine if the answer is faithful to the document based on the score and inference.
7. Adjust the model parameters as needed to optimize performance.
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