Update README.md

README.md

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 ---
 library_name: transformers
-tags: []
+tags:
+- text-generation
+- pytorch
+- Lynx
+- Patronus AI
+- evaluation
+- hallucination-detection
+license: cc-by-nc-4.0
+language:
+- en
 ---
 
 # Model Card for Model ID
 
-<!-- Provide a quick summary of what the model is/does. -->
-
+Lynx is an open-source hallucination evaluation model. Patronus-Lynx-8B-Instruct-v1.1 was trained on a mix of datasets including CovidQA, PubmedQA, DROP, RAGTruth.
+The datasets contain a mix of hand-annotated and synthetic data. The maximum sequence length is 8000 tokens. 
 
 
 ## Model Details
 
-### Model Description
-
-<!-- Provide a longer summary of what this model is. -->
-
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+- **Model Type:** Patronus-Lynx-8B-Instruct-v1.1 is a fine-tuned version of meta-llama/Meta-Llama-3.1-8B-Instruct model.
+- **Language:** Primarily English
+- **Developed by:** Patronus AI
+- **Paper:** [https://arxiv.org/abs/2407.08488](https://arxiv.org/abs/2407.08488)
+- **License:** [https://creativecommons.org/licenses/by-nc/4.0/](https://creativecommons.org/licenses/by-nc/4.0/)
 
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-
-### Model Sources [optional]
+### Model Sources
 
 <!-- Provide the basic links for the model. -->
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-
-## Uses
-
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
-### Direct Use
-
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
-
-### Downstream Use [optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
-
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
-
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+- **Repository:** [https://github.com/patronus-ai/Lynx-hallucination-detection](https://github.com/patronus-ai/Lynx-hallucination-detection)
 
-[More Information Needed]
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
 
 ## How to Get Started with the Model
+Lynx is trained to detect hallucinations in RAG settings. Provided a document, question and answer, the model can evaluate whether the answer is faithful to the document.
 
-Use the code below to get started with the model.
-
-[More Information Needed]
-
-## Training Details
-
-### Training Data
-
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
-[More Information Needed]
-
-### Training Procedure
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
+To use the model, we recommend using the following prompt:
 
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
-
-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
+```
+PROMPT = """
+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.
 
+--
+QUESTION (THIS DOES NOT COUNT AS BACKGROUND INFORMATION):
+{question}
 
+--
+DOCUMENT:
+{context}
 
-## Model Examination [optional]
+--
+ANSWER:
+{answer}
 
-<!-- Relevant interpretability work for the model goes here -->
+--
 
-[More Information Needed]
+Your output should be in JSON FORMAT with the keys "REASONING" and "SCORE":
+{{"REASONING": <your reasoning as bullet points>, "SCORE": <your final score>}}
+"""
+```
 
-## Environmental Impact
+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. 
 
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+## Inference
 
-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).
+To run inference, you can use HF pipeline:
 
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
+```
 
-## Technical Specifications [optional]
+model_name = 'PatronusAI/Llama-3-Patronus-Lynx-8B-Instruct-v1.1'
+pipe = pipeline(
+          "text-generation",
+          model=model_name,
+          max_new_tokens=600,
+          device="cuda",
+          return_full_text=False
+        )
 
-### Model Architecture and Objective
+messages = [
+    {"role": "user", "content": prompt},
+]
 
-[More Information Needed]
+result = pipe(messages)
+print(result[0]['generated_text'])
 
-### Compute Infrastructure
+```
 
-[More Information Needed]
+Since the model is trained in chat format, ensure that you pass the prompt as a user message.
 
-#### Hardware
+For more information on training details, refer to our [ArXiv paper](https://arxiv.org/abs/2407.08488).
 
-[More Information Needed]
-
-#### Software
-
-[More Information Needed]
-
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
-
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
-
-## Model Card Authors [optional]
+## Evaluation
 
-[More Information Needed]
+The model was evaluated on [PatronusAI/HaluBench](https://huggingface.co/datasets/PatronusAI/HaluBench).
+
+
+| Model | HaluEval | RAGTruth | FinanceBench | DROP | CovidQA | PubmedQA | Overall
+| :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |
+| GPT-4o | 87.9% | 84.3% | **85.3%** | 84.3% | 95.0% | 82.1% | 86.5% |
+| GPT-4-Turbo | 86.0% | **85.0%** | 82.2% | 84.8% | 90.6% | 83.5% | 85.0% |
+| GPT-3.5-Turbo | 62.2% | 50.7% | 60.9% | 57.2% | 56.7% | 62.8% | 58.7% |
+| Claude-3-Sonnet | 84.5% | 79.1% | 69.7% | 84.3% | 95.0% | 82.9% | 78.8% |
+| Claude-3-Haiku | 68.9% | 78.9% | 58.4% | 84.3% | 95.0% | 82.9% | 69.0% |
+| RAGAS Faithfulness | 70.6% | 75.8% | 59.5% | 59.6% | 75.0% | 67.7% | 66.9% |
+| Mistral-Instruct-7B | 78.3% | 77.7% | 56.3% | 56.3% | 71.7% | 77.9% | 69.4% |
+| Llama-3-Instruct-8B | 83.1% | 80.0% | 55.0% | 58.2% | 75.2% | 70.7% | 70.4% |
+| Llama-3-Instruct-70B | 87.0% | 83.8% | 72.7% | 69.4% | 85.0% | 82.6% | 80.1% |
+| LYNX (8B) | 85.7% | 80.0% | 72.5% | 77.8% | 96.3% | 85.2% | 82.9% |
+| LYNX (70B) | **88.4%** | 80.2% | 81.4% | **86.4%** | **97.5%** | **90.4%** | **87.4%** |
+| LYNX v1.1 (8B) | 87.27% |	79.88%	| 75.60% | 77.50% |	96.90%	| 88.90% |	84.34% |
+
+## Citation
+If you are using the model, cite using
+
+```
+@article{ravi2024lynx,
+  title={Lynx: An Open Source Hallucination Evaluation Model},
+  author={Ravi, Selvan Sunitha and Mielczarek, Bartosz and Kannappan, Anand and Kiela, Douwe and Qian, Rebecca},
+  journal={arXiv preprint arXiv:2407.08488},
+  year={2024}
+}
+```
 
 ## Model Card Contact
-
-[More Information Needed]
+[@sunitha-ravi](https://huggingface.co/sunitha-ravi)
+[@RebeccaQian1](https://huggingface.co/RebeccaQian1)
+[@presidev](https://huggingface.co/presidev)