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CODE_OF_CONDUCT.md

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+# Microsoft Open Source Code of Conduct
+
+This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/).
+
+Resources:
+
+- [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/)
+- [Microsoft Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/)
+- Contact [[email protected]](mailto:[email protected]) with questions or concerns

LICENSE

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+MICROSOFT RESEARCH LICENSE TERMS
+
+IF YOU LIVE IN THE UNITED STATES, PLEASE READ THE “BINDING ARBITRATION AND CLASS ACTION WAIVER” SECTION BELOW. IT AFFECTS HOW DISPUTES ARE RESOLVED.
+
+These license terms are an agreement between you and Microsoft Corporation (or one of its affiliates). They apply to the source code, object code, machine learning models, or data (collectively “Materials”) that accompany this license. IF YOU COMPLY WITH THESE LICENSE TERMS, YOU HAVE THE RIGHTS BELOW. BY USING THE MATERIALS, YOU ACCEPT THESE TERMS.
+
+1) INSTALLATION AND USE RIGHTS TO THE MATERIALS.
+
+Subject to the terms of this agreement, you have the below rights, if applicable, to use the Materials solely for non-commercial, non-revenue generating, research purposes:
+
+a) Source Code. If source code is included, you may use and modify the source code, but you may not distribute the source code.
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+12) CONSUMER RIGHTS; REGIONAL VARIATIONS. This agreement describes certain legal rights. You may have other rights, including consumer rights, under the laws of your state, province, or country. Separate and apart from your relationship with Microsoft, you may also have rights with respect to the party from which you acquired the Materials. This agreement does not change those other rights if the laws of your state, province, or country do not permit it to do so. For example, if you acquired the Materials in one of the below regions, or mandatory country law applies, then the following provisions apply to you:
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+13) DISCLAIMER OF WARRANTY. THE MATERIALS ARE LICENSED “AS IS.” YOU BEAR THE RISK OF USING THEM. MICROSOFT GIVES NO EXPRESS WARRANTIES, GUARANTEES, OR CONDITIONS. TO THE EXTENT PERMITTED UNDER APPLICABLE LAWS, MICROSOFT EXCLUDES ALL IMPLIED WARRANTIES, INCLUDING MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, AND NON-INFRINGEMENT.
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+14) LIMITATION ON AND EXCLUSION OF DAMAGES. IF YOU HAVE ANY BASIS FOR RECOVERING DAMAGES DESPITE THE PRECEDING DISCLAIMER OF WARRANTY, YOU CAN RECOVER FROM MICROSOFT AND ITS SUPPLIERS ONLY DIRECT DAMAGES UP TO U.S. $5.00. YOU CANNOT RECOVER ANY OTHER DAMAGES, INCLUDING CONSEQUENTIAL, LOST PROFITS, SPECIAL, INDIRECT OR INCIDENTAL DAMAGES.
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+It also applies even if Microsoft knew or should have known about the possibility of the damages. The above limitation or exclusion may not apply to you because your state, province, or country may not allow the exclusion or limitation of incidental, consequential, or other damages.

README.md

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+---
+license: other
+license_name: microsoft-research-license
+license_link: https://huggingface.co/microsoft/phi-1_5/resolve/main/Research%20License.docx
+language:
+- en
+pipeline_tag: text-generation
+---
+
+## Model Summary
+
+Phi-2 is a Transformer with **2.7 billion** parameters. It was trained using the same data sources as [phi-1.5](https://huggingface.co/microsoft/phi-1.5), augmented with a new data source that consists of various NLP synthetic texts and filtered websites (for safety and educational value). When assessed against benchmarks testing common sense, language understanding, and logical reasoning, phi-2 showcased a nearly state-of-the-art performance among models with less than 13 billion parameters.
+
+Our model hasn't been fine-tuned through reinforcement learning from human feedback. The intention behind crafting this open-source model is to provide the research community with a non-restricted small model to explore vital safety challenges, such as reducing toxicity, understanding societal biases, enhancing controllability, and more.
+
+
+## Intended Uses
+
+Phi-2 is intended for research purposes only. Given the nature of the training data, the phi-2 model is best suited for prompts using the QA format, the chat format, and the code format.
+
+#### QA format:
+
+You can provide the prompt as a standalone question as follows:
+
+```markdown
+Write a detailed analogy between mathematics and a lighthouse.
+```
+where the model generates the text after "." . 
+To encourage the model to write more concise answers, you can also try the following QA format using "Instruct: \<prompt\>\nOutput:"
+```markdown
+Instruct: Write a detailed analogy between mathematics and a lighthouse.
+Output: Mathematics is like a lighthouse. Just as a lighthouse guides ships safely to shore, mathematics provides a guiding light in the world of numbers and logic. It helps us navigate through complex problems and find solutions. Just as a lighthouse emits a steady beam of light, mathematics provides a consistent framework for reasoning and problem-solving. It illuminates the path to understanding and helps us make sense of the world around us.
+```
+where the model generates the text after "Output:".
+
+#### Chat format:
+```markdown
+Alice: I don't know why, I'm struggling to maintain focus while studying. Any suggestions?
+Bob: Well, have you tried creating a study schedule and sticking to it?
+Alice: Yes, I have, but it doesn't seem to help much.
+Bob: Hmm, maybe you should try studying in a quiet environment, like the library.
+Alice: ...
+```
+
+where the model generates the text after the first "Bob:".
+
+#### Code format:
+```python
+def print_prime(n):
+   """
+   Print all primes between 1 and n
+   """
+   primes = []
+   for num in range(2, n+1):
+       is_prime = True
+       for i in range(2, int(math.sqrt(num))+1):
+           if num % i == 0:
+               is_prime = False
+               break
+       if is_prime:
+           primes.append(num)
+   print(primes)
+```
+where the model generates the text after the comments.
+
+  
+**Out of scope**
+
+* The phi-2 model is intended for research purposes. The model-generated text/code should be treated as a starting point rather than a definitive solution for potential use cases. Users should be cautious when employing these models in their applications.
+
+* Direct adoption for production tasks is out of the scope of this research project. As a result, the phi-2 model has not been tested to ensure that it performs adequately for any production-level application. Please refer to the limitation sections of this document for more details.
+
+
+## Limitations of phi-2
+
+  
+
+* Generate Inaccurate Code and Facts: The model may produce incorrect code snippets and statements. Users should treat these outputs as suggestions or starting points, not as definitive or accurate solutions.
+
+* Limited Scope for code: Majority of phi-2 training data is based in Python and use common packages such as "typing, math, random, collections, datetime, itertools". If the model generates Python scripts that utilize other packages or scripts in other languages, we strongly recommend users manually verify all API uses.
+
+* Unreliable Responses to Instruction: The model has not undergone instruction fine-tuning. As a result, it may struggle or fail to adhere to intricate or nuanced instructions provided by users.
+
+* Language Limitations: The model is primarily designed to understand standard English. Informal English, slang, or any other languages might pose challenges to its comprehension, leading to potential misinterpretations or errors in response.
+
+* Potential Societal Biases: phi-2 is not entirely free from societal biases despite efforts in assuring trainig data safety. There's a possibility it may generate content that mirrors these societal biases, particularly if prompted or instructed to do so. We urge users to be aware of this and to exercise caution and critical thinking when interpreting model outputs.
+
+* Toxicity: Despite being trained with carefully selected data, the model can still produce harmful content if explicitly prompted or instructed to do so. We chose to release the model for research purposes only -- We hope to help the open-source community develop the most effective ways to reduce the toxicity of a model directly after pretraining.
+
+* Verbosity: Phi-2 being a base model often produces irrelevant or extra text and responses following its first answer to user prompts within a single turn. This is due to its training dataset being primarily textbooks, which results in textbook-like responses.
+
+  
+
+## Training
+
+### Model
+
+* Architecture: a Transformer-based model with next-word prediction objective
+
+* Context length: 2048 tokens
+
+* Dataset size: 250B tokens, combination of NLP synthetic data created by AOAI GPT-3.5 and filtered web data from Falcon RefinedWeb and SlimPajama, which was assessed by AOAI GPT-4.
+
+* Training tokens: 1.4T tokens
+
+* GPUs: 96xA100-80G
+
+* Training time: 14 days
+
+  
+
+### Software
+
+* [PyTorch](https://github.com/pytorch/pytorch)
+
+* [DeepSpeed](https://github.com/microsoft/DeepSpeed)
+
+* [flash-attention](https://github.com/HazyResearch/flash-attention) > 2.0.0
+
+### License
+
+The model is licensed under the [microsoft-research-license](https://huggingface.co/microsoft/phi-1_5/resolve/main/Research%20License.docx).
+
+## Trademarks
+
+This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft trademarks or logos is subject to and must follow [Microsoft’s Trademark & Brand Guidelines](https://www.microsoft.com/en-us/legal/intellectualproperty/trademarks). Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. Any use of third-party trademarks or logos are subject to those third-party’s policies.

SECURITY.md

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+<!-- BEGIN MICROSOFT SECURITY.MD V0.0.9 BLOCK -->
+
+## Security
+
+Microsoft takes the security of our software products and services seriously, which includes all source code repositories managed through our GitHub organizations, which include [Microsoft](https://github.com/Microsoft), [Azure](https://github.com/Azure), [DotNet](https://github.com/dotnet), [AspNet](https://github.com/aspnet) and [Xamarin](https://github.com/xamarin).
+
+If you believe you have found a security vulnerability in any Microsoft-owned repository that meets [Microsoft's definition of a security vulnerability](https://aka.ms/security.md/definition), please report it to us as described below.
+
+## Reporting Security Issues
+
+**Please do not report security vulnerabilities through public GitHub issues.**
+
+Instead, please report them to the Microsoft Security Response Center (MSRC) at [https://msrc.microsoft.com/create-report](https://aka.ms/security.md/msrc/create-report).
+
+If you prefer to submit without logging in, send email to [[email protected]](mailto:[email protected]).  If possible, encrypt your message with our PGP key; please download it from the [Microsoft Security Response Center PGP Key page](https://aka.ms/security.md/msrc/pgp).
+
+You should receive a response within 24 hours. If for some reason you do not, please follow up via email to ensure we received your original message. Additional information can be found at [microsoft.com/msrc](https://www.microsoft.com/msrc). 
+
+Please include the requested information listed below (as much as you can provide) to help us better understand the nature and scope of the possible issue:
+
+  * Type of issue (e.g. buffer overflow, SQL injection, cross-site scripting, etc.)
+  * Full paths of source file(s) related to the manifestation of the issue
+  * The location of the affected source code (tag/branch/commit or direct URL)
+  * Any special configuration required to reproduce the issue
+  * Step-by-step instructions to reproduce the issue
+  * Proof-of-concept or exploit code (if possible)
+  * Impact of the issue, including how an attacker might exploit the issue
+
+This information will help us triage your report more quickly.
+
+If you are reporting for a bug bounty, more complete reports can contribute to a higher bounty award. Please visit our [Microsoft Bug Bounty Program](https://aka.ms/security.md/msrc/bounty) page for more details about our active programs.
+
+## Preferred Languages
+
+We prefer all communications to be in English.
+
+## Policy
+
+Microsoft follows the principle of [Coordinated Vulnerability Disclosure](https://aka.ms/security.md/cvd).
+
+<!-- END MICROSOFT SECURITY.MD BLOCK -->