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Llama 3 Patronus Lynx 8B Instruct
Overview :
Llama-3-Patronus-Lynx-8B-Instruct is a fine-tuned version of the meta-llama/Meta-Llama-3-8B-Instruct model developed by Patronus AI, primarily designed to detect hallucinations in retrieval-augmented generation (RAG) settings. The model is trained on multiple datasets, including CovidQA, PubmedQA, DROP, and RAGTruth, featuring both human-annotated and synthetic data. It evaluates whether a given document, question, and answer are faithful to the document content, refraining from providing new information outside the document or contradicting it.
Target Users :
The target audience includes researchers, developers, and enterprises needing a model to assess and detect the authenticity of AI-generated content, particularly in applications where information accuracy is critical, such as in healthcare, finance, and academic research.
Use Cases
Researchers use the model to evaluate the authenticity of answers in medical literature.
Financial analysts leverage the model to verify the accuracy of information in financial reports.
Academic institutions utilize the model to validate data and conclusions in academic research.
Features
Hallucination detection: Assess the fidelity of answers to the content of the given document.
Text generation: Generate evaluation results based on the provided questions, documents, and answers.
Chat format training: The model is trained in a chat format, making it suitable for dialogue systems.
Multi-dataset training: Combines datasets from various domains, enhancing the model's generalization capabilities.
Open-source license: The model operates under the cc-by-nc-4.0 license, allowing non-commercial use and distribution.
High performance: Performs exceptionally well on several evaluation datasets, particularly excelling in FinanceBench and CovidQA.
Inference capabilities: Capable of running inferences and generating model outputs.
How to Use
1. Prepare the text content for questions, documents, and answers.
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2. Fill in the questions, documents, and answers using the prompt format recommended by the model.
3. Invoke the model through the Hugging Face pipeline interface by passing in the prepared prompt.
4. The model will output results in JSON format, including 'REASONING' and 'SCORE'.
5. Determine if the answer corresponds to the document based on the model's 'SCORE'; 'PASS' indicates fidelity, while 'FAIL' indicates a lack thereof.
6. Analyze the 'REASONING' section to understand the model's evaluation rationale.
7. Deploy the model in your environment if needed, or use the inference endpoints provided by Hugging Face for inference.
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