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CODE_OF_CONDUCT.md

@@ -0,0 +1,9 @@
+# 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

@@ -1,62 +1,22 @@
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+Microsoft.
+Copyright (c) Microsoft Corporation.
+
+MIT License
+
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+of this software and associated documentation files (the "Software"), to deal
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README.md

@@ -1,6 +1,6 @@
 ---
-license: other
-license_link: https://huggingface.co/amgadhasan/phi-4/blob/main/LICENSE
+license: mit
+license_link: https://huggingface.co/microsoft/phi-4/resolve/main/LICENSE
 language:
 - en
 pipeline_tag: text-generation
@@ -20,7 +20,6 @@ widget:
     content: How should I explain the Internet?
 library_name: transformers
 ---
-Note: This model is copied from the official [release](https://aka.ms/phi3-azure-ai) on Azure AI Foundary
 
 # Phi-4 Model Card 
 
@@ -37,17 +36,17 @@ Note: This model is copied from the official [release](https://aka.ms/phi3-azure
 | **Training time**       | 21 days                                                                       |
 | **Training data**       | 9.8T tokens                                                                   |
 | **Outputs**             | Generated text in response to input                                           |
-| **Dates**               | October 2024 – November 2024                                                  |
+| **Dates**               | October 2024 – November 2024                                                  |
 | **Status**              | Static model trained on an offline dataset with cutoff dates of June 2024 and earlier for publicly available data                                                                               |
 | **Release date**        | December 12, 2024                                                             |
-| **License**             | MSRLA                                                                         |
+| **License**             | MIT                                                                         |
 
 ## Intended Use 
 
 |                               |                                                                         |
 |-------------------------------|-------------------------------------------------------------------------|
 | **Primary Use Cases**         | Our model is designed to accelerate research on language models, for use as a building block for generative AI powered features. It provides uses for general purpose AI systems and applications (primarily in English) which require:<br><br>1. Memory/compute constrained environments.<br>2. Latency bound scenarios.<br>3. Reasoning and logic.                                                                       |
-| **Out-of-Scope Use Cases**    | Our models is not specifically designed or evaluated for all downstream purposes, thus:<br><br>1. Developers should consider common limitations of language models as they select use cases, and evaluate and mitigate for accuracy, safety, and fairness before using within a specific downstream use case, particularly for high-risk scenarios.<br>2. Developers should be aware of and adhere to applicable laws or regulations (including privacy, trade compliance laws, etc.) that are relevant to their use case, including the model’s focus on English.<br>3. Nothing contained in this Model Card should be interpreted as or deemed a restriction or modification to the license the model is released under.                                                              |
+| **Out-of-Scope Use Cases**    | Our models is not specifically designed or evaluated for all downstream purposes, thus:<br><br>1. Developers should consider common limitations of language models as they select use cases, and evaluate and mitigate for accuracy, safety, and fairness before using within a specific downstream use case, particularly for high-risk scenarios.<br>2. Developers should be aware of and adhere to applicable laws or regulations (including privacy, trade compliance laws, etc.) that are relevant to their use case, including the model’s focus on English.<br>3. Nothing contained in this Model Card should be interpreted as or deemed a restriction or modification to the license the model is released under.                                                              |
 
 ## Data Overview 
 
@@ -57,7 +56,7 @@ Our training data is an extension of the data used for Phi-3 and includes a wide
 
 1. Publicly available documents filtered rigorously for quality, selected high-quality educational data, and code.
 
-2. Newly created synthetic, “textbook-like” data for the purpose of teaching math, coding, common sense reasoning, general knowledge of the world (science, daily activities, theory of mind, etc.).
+2. Newly created synthetic, “textbook-like” data for the purpose of teaching math, coding, common sense reasoning, general knowledge of the world (science, daily activities, theory of mind, etc.).
 
 3. Acquired academic books and Q&A datasets.
 
@@ -67,7 +66,7 @@ Multilingual data constitutes about 8% of our overall data. We are focusing on t
 
 #### Benchmark datasets 
 
-We evaluated `phi-4` using [OpenAI's SimpleEval](https://github.com/openai/simple-evals) and our own internal benchmarks to understand the model’s capabilities, more specifically: 
+We evaluated `phi-4` using [OpenAI’s SimpleEval](https://github.com/openai/simple-evals) and our own internal benchmarks to understand the model’s capabilities, more specifically: 
 
 * **MMLU:** Popular aggregated dataset for multitask language understanding.
 
@@ -91,13 +90,13 @@ We evaluated `phi-4` using [OpenAI's SimpleEval](https://github.com/openai/simpl
 
 ### Safety Evaluation and Red-Teaming 
 
-Prior to release, `phi-4` followed a multi-faceted evaluation approach. Quantitative evaluation was conducted with multiple open-source safety benchmarks and in-house tools utilizing adversarial conversation simulation. For qualitative safety evaluation, we collaborated with the independent AI Red Team (AIRT) at Microsoft to assess safety risks posed by `phi-4` in both average and adversarial user scenarios. In the average user scenario, AIRT emulated typical single-turn and multi-turn interactions to identify potentially risky behaviors. The adversarial user scenario tested a wide range of techniques aimed at intentionally subverting the model’s safety training including jailbreaks, encoding-based attacks, multi-turn attacks, and adversarial suffix attacks.   
+Prior to release, `phi-4` followed a multi-faceted evaluation approach. Quantitative evaluation was conducted with multiple open-source safety benchmarks and in-house tools utilizing adversarial conversation simulation. For qualitative safety evaluation, we collaborated with the independent AI Red Team (AIRT) at Microsoft to assess safety risks posed by `phi-4` in both average and adversarial user scenarios. In the average user scenario, AIRT emulated typical single-turn and multi-turn interactions to identify potentially risky behaviors. The adversarial user scenario tested a wide range of techniques aimed at intentionally subverting the model’s safety training including jailbreaks, encoding-based attacks, multi-turn attacks, and adversarial suffix attacks.   
 
 Please refer to the technical report for more details on safety alignment. 
 
 ## Model Quality
 
-To understand the capabilities, we compare `phi-4` with a set of models over OpenAI’s SimpleEval benchmark. 
+To understand the capabilities, we compare `phi-4` with a set of models over OpenAI’s SimpleEval benchmark. 
 
 At the high-level overview of the model quality on representative benchmarks. For the table below, higher numbers indicate better performance: 
 

SECURITY.md

@@ -0,0 +1,41 @@
+<!-- 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).
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+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 -->

config.json

@@ -24,7 +24,7 @@
   "rms_norm_eps": 1e-05,
   "rope_scaling": null,
   "rope_theta": 250000,
-  "sliding_window": 16384,
+  "sliding_window": null,
   "tie_word_embeddings": false,
   "torch_dtype": "bfloat16",
   "transformers_version": "4.47.0",

model.safetensors.index.json

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