Update README.md

README.md

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 library_name: transformers
-tags: []
+tags: [Structured Pruning, Phi-2, Memory-efficient Pruning]
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 # Model Card for Model ID
 
-<!-- Provide a quick summary of what the model is/does. -->
-
-
+We prune the Phi-2 (2.7B) model to 35% sparsty (1.8B) and then finetune on 100K 2048 length sequences from the C4 dataset (https://huggingface.co/datasets/c4).
+Our pruning algorithm is described in the paper [Everybody Prune Now: Structured Pruning of LLMs with only Forward Passes](https://arxiv.org/abs/2402.05406). 
+[Code for pruning algorithm can be found here ](https://github.com/ldery/Bonsai/tree/main).
 
 ## Model Details
+Model is derived from Pruning the  [Phi-2 Model](https://huggingface.co/microsoft/phi-2)
 
 ### Model Description
 
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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.
 
-- **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]
+- **Developed by:** Lucio Dery, Steven Kolawole, Jean-François Kagy, Virginia Smith, Graham Neubig, Ameet Talwalkar
+- **Model type:** Decoder-only
+- **Language(s) (NLP):** English
+- **License:** MIT
 
 ### Model Sources [optional]
 
 <!-- Provide the basic links for the model. -->
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
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-## 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
+- **Repository:** [https://github.com/ldery/Bonsai/tree/main]
+- **Paper [optional]:** [https://arxiv.org/abs/2402.05406]
 
-<!-- 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.
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-## How to Get Started with the Model
-
-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]
+Finetuned on 100K 2048 length sequences from the C4 dataset (https://huggingface.co/datasets/c4).
 
 ### Training Procedure 
 
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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-#### Preprocessing [optional]
-
-[More Information Needed]
+Full fine-tuning.  
 
 
 #### 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 -->
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-#### Speeds, Sizes, Times [optional]
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-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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-[More Information Needed]
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-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
+Distillation KL-Weight :  0.01
 
-### Testing Data, Factors & Metrics
+Learning Rate : 1e-4
 
-#### Testing Data
+Batch Size : 128
 
-<!-- This should link to a Dataset Card if possible. -->
+Optimzer : AdamW
 
-[More Information Needed]
+Warmup Steps : 5
 
-#### Factors
 
-<!-- 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
-
-<!-- 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
-
-[More Information Needed]
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-#### Summary
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-
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-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
 
 ## Environmental Impact
 
@@ -144,58 +63,28 @@ Use the code below to get started with the model.
 
 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]
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-## Technical Specifications [optional]
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-### Model Architecture and Objective
-
-[More Information Needed]
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-### Compute Infrastructure
-
-[More Information Needed]
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-#### Hardware
-
-[More Information Needed]
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-#### Software
-
-[More Information Needed]
+- **Hardware Type:** NVIDIA A6000
 
 ## 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]
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-**APA:**
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-[More Information Needed]
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-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
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-## More Information [optional]
-
-[More Information Needed]
+@misc{dery2024everybody,
+      title={Everybody Prune Now: Structured Pruning of LLMs with only Forward Passes}, 
+      author={Lucio Dery and Steven Kolawole and Jean-Francois Kagey and Virginia Smith and Graham Neubig and Ameet Talwalkar},
+      year={2024},
+      eprint={2402.05406},
+      archivePrefix={arXiv},
+      primaryClass={cs.LG}
+}
 
 ## Model Card Authors [optional]
 
-[More Information Needed]
+Lucio Dery: [email protected]
 
 ## Model Card Contact
 
-[More Information Needed]
+[email protected]