Update README.md

README.md

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 ---
-library_name: transformers
-tags: []
+language:
+- en
+license: apache-2.0
+tags:
+- llama
+- entigraph
+- synthetic-continued-pretraining
 ---
 
-# Model Card for Model ID
+# EntiGraph CPT Model (based on Llama 3 8B)
 
-<!-- Provide a quick summary of what the model is/does. -->
+## Model Description
 
+The EntiGraph CPT model is a continuation of the Llama 3 8B base model, trained using the [Synthetic Continued Pretraining by Yang et al. (2024)](https://arxiv.org/pdf/2409.07431) approach with the EntiGraph algorithm. This model has been trained on a synthetic corpus generated from the QuALITY dataset to acquire domain-specific knowledge efficiently.
+The code used to train the model is available at the [Synthetic Continued Pretraining GitHub repo](https://github.com/ZitongYang/Synthetic_Continued_Pretraining).
 
+### Model Details
 
-## 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.
-
-- **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]
-
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+- **Developed by:** Zitong Yang, Neil Band, Shuangping Li, Emmanuel Candès, and Tatsunori Hashimoto
+- **Model type:** Causal Language Model
+- **Language(s):** English
+- **License:** Apache 2.0
+- **Finetuned from model:** Llama 3 8B
 
 ## 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. -->
+### Intended Use
 
-[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]
+This model is intended for research purposes and applications requiring domain-specific knowledge related to the QuALITY dataset. It can be used for tasks such as closed-book question answering, summarization, and other NLP tasks within the domain of the training data.
 
 ### 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. -->
-
-[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
-
-Use the code below to get started with the model.
-
-[More Information Needed]
+This model should not be used for generating factual information outside the scope of its training data or for any malicious purposes.
 
 ## 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]
+The model was trained on a 455M token synthetic corpus generated by the EntiGraph algorithm from the QuALITY dataset.
 
 ### 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]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
+- **Pretraining:** Continued pretraining on the EntiGraph synthetic corpus
+- **Hyperparameters:** 
+  - Learning rate: 5e-06
+  - Batch size: 16
+  - Weight decay: 0.01
+  - Warmup: 0.05
+  - Epochs: 2
+  - RedPajama replay rate: 0.1
 
 ## 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
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-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).
-
-- **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 Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
-
-#### Hardware
-
-[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. -->
+The model has been evaluated on the QuALITY question answering dataset, demonstrating improved performance in closed-book QA tasks compared to the base model.
 
-[More Information Needed]
+## Limitations and Biases
 
-## More Information [optional]
+While the EntiGraph CPT model shows improved performance on domain-specific tasks, it may inherit biases present in the original Llama 3 8B model and the QuALITY dataset. Users should be aware of potential limitations in generating content outside its training domain.
 
-[More Information Needed]
+## Citation
 
-## Model Card Authors [optional]
+If you use this model, please cite the original paper:
 
-[More Information Needed]
+```
+@misc{yang2024syntheticcontinuedpretraining,
+      title={Synthetic continued pretraining}, 
+      author={Zitong Yang and Neil Band and Shuangping Li and Emmanuel Candès and Tatsunori Hashimoto},
+      year={2024},
+      eprint={2409.07431},
+      archivePrefix={arXiv},
+      primaryClass={cs.LG},
+      url={https://arxiv.org/abs/2409.07431}, 
+}
+```
 
-## Model Card Contact
+## Ethical Considerations
 
-[More Information Needed]
+Users of this model should be aware of the ethical implications of using large language models and ensure responsible use in applications.