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library_name: transformers
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---
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# Model Card for Model ID
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## Model Details
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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- **Developed by:** [More Information Needed]
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- **Funded by [optional]:** [More Information Needed]
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- **Shared by [optional]:** [More Information Needed]
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- **Model type:** [More Information Needed]
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- **Language(s) (NLP):** [More Information Needed]
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- **License:** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
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### Model Sources [optional]
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<!-- Provide the basic links for the model. -->
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- **Repository:** [
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- **Paper [optional]:** [More Information Needed]
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- **Demo [optional]:** [More Information Needed]
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## Uses
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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### Direct Use
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<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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[More Information Needed]
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### Downstream Use [optional]
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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[More Information Needed]
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### Out-of-Scope Use
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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[More Information Needed]
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## Bias, Risks, and Limitations
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<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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[More Information Needed]
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### Recommendations
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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## How to Get Started with the Model
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### Training Procedure
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[More Information Needed]
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- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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## Evaluation
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### Testing Data, Factors & Metrics
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#### Testing Data
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<!-- This should link to a Dataset Card if possible. -->
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#### Factors
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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[More Information Needed]
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#### Metrics
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<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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[More Information Needed]
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### Results
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[More Information Needed]
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#### Summary
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## Model Examination [optional]
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<!-- Relevant interpretability work for the model goes here -->
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[More Information Needed]
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## Environmental Impact
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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- **Hardware Type:** [More Information Needed]
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- **Hours used:** [More Information Needed]
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- **Cloud Provider:** [More Information Needed]
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- **Compute Region:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
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## Technical Specifications [optional]
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### Model Architecture and Objective
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[More Information Needed]
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### Compute Infrastructure
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[More Information Needed]
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#### Hardware
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[More Information Needed]
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#### Software
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## Citation [optional]
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[More Information Needed]
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## Glossary [optional]
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<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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[More Information Needed]
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## More Information [optional]
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[More Information Needed]
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## Model Card Authors [optional]
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[More Information Needed]
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## Model Card Contact
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[More Information Needed]
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---
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library_name: transformers
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tags:
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- text-generation
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- pytorch
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- Lynx
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- Patronus AI
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- evaluation
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- hallucination-detection
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license: llama3
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language:
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- en
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# Model Card for Model ID
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Lynx is an open-source hallucination evaluation model. Patronus-Lynx-70B-Instruct was trained on a mix of datasets such as CovidQA, PubmedQA, DROP, FinanceBench.
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The datasets contain a mix of hand-annotated and synthetic data. The maximum sequence length is 8000 tokens.
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## Model Details
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- **Model Type:** Patronus-Lynx-70B-Instruct is a fine-tuned version of meta-llama/Meta-Llama-3-70B-Instruct model.
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- **Language:** Primarily English
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- **Developed by:** Patronus AI
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- **License:** [https://llama.meta.com/llama3/license](https://llama.meta.com/llama3/license)
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### Model Sources [optional]
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<!-- Provide the basic links for the model. -->
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- **Repository:** [https://github.com/patronus-ai/Lynx-hallucination-detection](https://github.com/patronus-ai/Lynx-hallucination-detection)
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## How to Get Started with the Model
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The model is fine-tuned to be used to detect faithfulness in a RAG setting. Provided a document, question and answer, the model can evaluate whether the answer is faithful to the document.
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To use the model, we recommend using the prompt we used for fine-tuning:
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```
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PROMPT = """
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Given the following QUESTION, DOCUMENT and ANSWER you must analyze the provided answer and determine whether it is faithful to the contents of the DOCUMENT. The ANSWER must not offer new information beyond the context provided in the DOCUMENT. The ANSWER also must not contradict information provided in the DOCUMENT. Output your final verdict by strictly following this format: "PASS" if the answer is faithful to the DOCUMENT and "FAIL" if the answer is not faithful to the DOCUMENT. Show your reasoning.
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--
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QUESTION (THIS DOES NOT COUNT AS BACKGROUND INFORMATION):
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{question}
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DOCUMENT:
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{context}
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--
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ANSWER:
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{answer}
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--
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Your output should be in JSON FORMAT with the keys "REASONING" and "SCORE":
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{{"REASONING": <your reasoning as bullet points>, "SCORE": <your final score>}}
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"""
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```
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The model will output the score as 'PASS' if the answer is faithful to the document or FAIL if the answer is not faithful to the document.
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## Training Details
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The model was finetuned for 3 epochs using 32 H100s on dataset of size 2400. We use [lion](https://github.com/lucidrains/lion-pytorch) optimizer with lr=5.0e-7. For more details on data generation, please check out our Github repo.
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### Training Data
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We train on 2400 samples consisting of CovidQA, PubmedQA, DROP and RAGTruth samples. For datasets that do not contain hallucinated samples, we generate perturbations to introduce hallucinations in the data. For more details about the data generation process, refer to the paper.
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The training data can be found here: [PatronusAI/drop-RAGTruth-covidqa-pubmed](https://huggingface.co/datasets/PatronusAI/drop-RAGTruth-covidqa-pubmed)
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## Evaluation
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The model was evaluated on [PatronusAI/hallucination-evaluation-benchmark](https://huggingface.co/datasets/PatronusAI/hallucination-evaluation-benchmark).
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It outperforms GPT-3.5-Turbo, GPT-4-Turbo, GPT-4o and Claude Sonnet.
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## Citation [optional]
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[More Information Needed]
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## Model Card Contact
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[@sunitha-ravi](https://huggingface.co/sunitha-ravi)
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