mannix / phi3-mini-4k

我们的模型不是专门为所有下游用途设计的或评估的。开发者在选择用例时应考虑语言模型的常见局限性，并在特定下游用例中使用之前评估和减轻准确性、安全性和公平性，特别是在高风险场景中。
开发者应了解并遵守与其用案相关的适用法律或法规（包括隐私、贸易合规法律等）。
本模型卡片中包含的内容不应被理解为或认为是对该模型发布的许可的任何限制或修改。

负责任的AI考虑

类似于其他语言模型，Phi系列模型可能会表现出不公平、不可靠或冒犯性的行为。应警惕的一些限制行为包括

服务质量：Phi模型主要由英语文本训练。除英语以外的语言可能会经历更差的表现。在训练数据中代表性较低的非标准美式英语可能会表现出比标准美式英语更差的表现。
危害的表征和刻板印象的持续：这些模型可能过度或不足地代表某些人群，消除某些群体的代表性，或加强贬低或消极的刻板印象。尽管经过安全后训练，由于不同群体表征水平的不同或训练数据中反映了现实世界模式和 societal biases的负面刻板印象例子的普遍性，这些限制可能仍然存在。
不适当或冒犯性内容：这些模型可能会产生不适当或冒犯性的其他内容，这可能在没有额外的针对特定用例的缓解措施的情况下，使得在敏感环境中部署不适当。
信息可靠性：语言模型可以生成无意义的内容或在看似合理但实际上不准确或过时的内容中造假。
代码范围的限制：Phi-3的大部分训练数据基于Python，使用常见包如“typing、math、random、collections、datetime、itertools”。如果模型生成利用其他包或不同语言的脚本的Python脚本，我们强烈建议用户手动验证所有API的使用。

开发者应应用负责任的AI最佳实践，并负责确保特定用例符合相关的法律法规（例如隐私、贸易等）。重要考虑领域包括
+ 分配：在没有进一步评估和附加去偏见技术的情况下，模型可能不适合可能对法律地位、资源分配或生活机会有重大影响的场景（例如：住房、就业、信贷等）。

高风险场景：开发者在评估使用模型于高风险场景的适用性时，应考虑不公平、不可靠或不适当的输出可能造成极大损失或造成伤害。这包括在准确性、可靠性至关重要的敏感或专家领域中提供建议（例如：法律或健康建议）。应根据部署环境在应用级别实施额外的保障措施。
虚假信息：模型可能生成不准确的信息。开发者应遵循透明度最佳实践，并告知最终用户它们正在与一个AI系统互动。在应用层面，开发者可以构建反馈机制和管道，以使用案特定、情境信息来定位响应，这种技术称为检索增强生成（RAG）。
有害内容的生成：开发者应评估输出的情境并使用适合其用例的安全分类器或自定义解决方案。
滥用：其他形式的滥用，如欺诈、垃圾邮件或恶意软件生产也可能是可能的，开发者应确保其应用不违反适用的法律和法规。

培训

模型

架构：Phi-3 Mini具有3.8B个参数，是一个密集型仅变换器模型。模型通过监督微调（SFT）和直接偏好优化（DPO）进行微调，以确保与人类偏好和安全指南保持一致。
输入：文本。它最适合用于聊天格式的提示。
上下文长度：128K个标记
GPU：512 H100-80G
训练时间：7天
训练数据：3.3T个标记
输出：对输入的响应生成的文本
日期：我们的模型是在2024年2月至4月之间训练的
状态：这是一个基于离线数据集且有截止日期为2023年10月的静态模型。随着模型的发展，未来可能发布调整模型的版本。

数据集

我们的训练数据包括广泛的来源，总共有3.3万亿个标记，是以下内容的组合：
1) 经过严格质量筛选的公开可用文档，选择了高质量的受教育数据、代码；
2) 为教学数学、编码、常识推理、世界一般知识（科学、日常生活、心智理论等）目的而创建的新造(interval)的、“教科书”式（style）数据；
3) 包含各种主题的高质量聊天格式监督数据，以反映人类对不同方面的偏好，如指令遵循、真实性、诚实和有益性。

软件

许可证

该模型根据MIT许可证授权。

商标

本项目可能包含项目、产品或服务的商标或徽标。Microsoft商标或徽标的授权使用必须遵循和遵守Microsoft的商标和品牌指南。对修改后此项目中的Microsoft商标或徽标的使用不应引起误导或暗示Microsoft的赞助。任何第三方商标或徽标的使用均受第三方政策约束。

资源

![image.png](https://ollama.org.cn/assets/mannix/phi3-mini-4k/b030ede8-9cbe-4d5c-97d2-6446495c2214)

- Quantization from `fp32`
- Using i-matrix `calibration_datav3.txt`

<p>Phi-3 is a family of open AI models developed by Microsoft.</p>

<h2>Parameter sizes</h2>

<ul>
<li><a href="https://ollama.org.cn/library/phi3:mini" rel="nofollow">Phi-3 Mini</a> – 3B parameters<br>
</li>
</ul>

<h2>Context window sizes</h2>

<ul>
<li>4k <code>ollama run mannix/phi3-mini-4k</code><br>
</li>
</ul>

<p>Phi-3 Mini is a 3.8B parameters, lightweight, state-of-the-art open model trained with the Phi-3 datasets that includes both synthetic data and the filtered publicly available websites data with a focus on high-quality and reasoning dense properties.</p>

<p>The model has underwent a post-training process that incorporates both supervised fine-tuning and direct preference optimization to ensure precise instruction adherence and robust safety measures.</p>

<p>When assessed against benchmarks testing common sense, language understanding, math, code, long context and logical reasoning, Phi-3 Mini-4K-Instruct showcased a robust and state-of-the-art performance among models with less than 13 billion parameters.</p>

<h2>Phi-3 Medium</h2>

<p>Phi-3 Medium is a 14B parameter language model, and outperforms Gemini 1.0 Pro.</p>

<h2>Intended Uses</h2>

<p><strong>Primary use cases</strong></p>

<p>The model is intended for commercial and research use in English. The model provides uses for applications which require<br>
1) memory/compute constrained environments<br>
2) latency bound scenarios<br>
3) strong reasoning (especially math and logic)<br>
4) long context</p>

<p>Our model is designed to accelerate research on language and multimodal models, for use as a building block for generative AI powered features.</p>

<p><strong>Use case considerations</strong></p>

<p>Our models are not specifically designed or evaluated for all downstream purposes. Developers should consider common limitations of language models as they select use cases, and evaluate and mitigate for accuracy, safety, and fariness before using within a specific downstream use case, particularly for high risk scenarios.<br>
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.<br>
Nothing contained in this Model Card should be interpreted as or deemed a restriction or modification to the license the model is released under.</p>

<h2>Responsible AI Considerations</h2>

<p>Like other language models, the Phi series models can potentially behave in ways that are unfair, unreliable, or offensive. Some of the limiting behaviors to be aware of include:</p>

<ul>
<li><p>Quality of Service: the Phi models are trained primarily on English text. Languages other than English will experience worse performance. English language varieties with less representation in the training data might experience worse performance than standard American English.</p></li>

<li><p>Representation of Harms &amp; Perpetuation of Stereotypes: These models can over- or under-represent groups of people, erase representation of some groups, or reinforce demeaning or negative stereotypes. Despite safety post-training, these limitations may still be present due to differing levels of representation of different groups or prevalence of examples of negative stereotypes in training data that reflect real-world patterns and societal biases.</p></li>

<li><p>Inappropriate or Offensive Content: these models may produce other types of inappropriate or offensive content, which may make it inappropriate to deploy for sensitive contexts without additional mitigations that are specific to the use case.</p></li>

<li><p>Information Reliability: Language models can generate nonsensical content or fabricate content that might sound reasonable but is inaccurate or outdated.</p></li>

<li><p>Limited Scope for Code: Majority of Phi-3 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.</p></li>
</ul>

<p>Developers should apply responsible AI best practices and are responsible for ensuring that a specific use case complies with relevant laws and regulations (e.g. privacy, trade, etc.). Important areas for consideration include:<br>
+ Allocation: Models may not be suitable for scenarios that could have consequential impact on legal status or the allocation of resources or life opportunities (ex: housing, employment, credit, etc.) without further assessments and additional debiasing techniques.</p>

<ul>
<li><p>High-Risk Scenarios: Developers should assess suitability of using models in high-risk scenarios where unfair, unreliable or offensive outputs might be extremely costly or lead to harm. This includes providing advice in sensitive or expert domains where accuracy and reliability are critical (ex: legal or health advice). Additional safeguards should be implemented at the application level according to the deployment context.</p></li>

<li><p>Misinformation: Models may produce inaccurate information. Developers should follow transparency best practices and inform end-users they are interacting with an AI system. At the application level, developers can build feedback mechanisms and pipelines to ground responses in use-case specific, contextual information, a technique known as Retrieval Augmented Generation (RAG).</p></li>

<li><p>Generation of Harmful Content: Developers should assess outputs for their context and use available safety classifiers or custom solutions appropriate for their use case.</p></li>

<li><p>Misuse: Other forms of misuse such as fraud, spam, or malware production may be possible, and developers should ensure that their applications do not violate applicable laws and regulations.</p></li>
</ul>

<h2>Training</h2>

<h3>Model</h3>

<ul>
<li>Architecture: Phi-3 Mini has 3.8B parameters and is a dense decoder-only Transformer model. The model is fine-tuned with Supervised fine-tuning (SFT) and Direct Preference Optimization (DPO) to ensure alignment with human preferences and safety guidelines.<br>
</li>
<li>Inputs: Text. It is best suited for prompts using chat format.<br>
</li>
<li>Context length: 128K tokens<br>
</li>
<li>GPUS: 512 H100-80G<br>
</li>
<li>Training time: 7 days<br>
</li>
<li>Training data: 3.3T tokens<br>
</li>
<li>Outputs: Generated text in response to the input<br>
</li>
<li>Dates: Our models were trained between February and April 2024<br>
</li>
<li>Status: This is a static model trained on an offline dataset with cutoff date October 2023. Future versions of the tuned models may be released as we improve models.<br>
</li>
</ul>

<h3>Datasets</h3>

<p>Our training data includes a wide variety of sources, totaling 3.3 trillion tokens, and is a combination of<br>
1) publicly available documents filtered rigorously for quality, selected high-quality educational data, and code;<br>
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.);<br>
3) high quality chat format supervised data covering various topics to reflect human preferences on different aspects such as instruct-following, truthfulness, honesty and helpfulness.</p>

<h3>Software</h3>

<ul>
<li><a href="https://github.com/pytorch/pytorch" rel="nofollow">PyTorch</a><br>
</li>
<li><a href="https://github.com/microsoft/DeepSpeed" rel="nofollow">DeepSpeed</a><br>
</li>
<li><a href="https://github.com/huggingface/transformers" rel="nofollow">Transformers</a><br>
</li>
<li><a href="https://github.com/HazyResearch/flash-attention" rel="nofollow">Flash-Attention</a><br>
</li>
</ul>

<h3>License</h3>

<p>The model is licensed under the <a href="https://ollama.org.cn/library/phi3:latest/blobs/fa8235e5b48fENSE" rel="nofollow">MIT license</a>.</p>

<h2>Trademarks</h2>

<p>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 <a href="https://www.microsoft.com/en-us/legal/intellectualproperty/trademarks" rel="nofollow">Microsoft’s Trademark &amp; Brand Guidelines</a>. 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.</p>

<h2>Resources</h2>

<ul>
<li><a href="https://hugging-face.cn/microsoft/Phi-3-mini-4k-instruct-gguf" rel="nofollow">HuggingFace</a><br>
</li>
<li><a href="https://aka.ms/phi3-blog" rel="nofollow">Phi-3 Microsoft Blog</a><br>
</li>
<li><a href="https://aka.ms/phi3-tech-report" rel="nofollow">Phi-3 Technical Report</a><br>
</li>
<li><a href="https://aka.ms/phi3-azure-ai" rel="nofollow">Phi-3 on Azure AI Studio</a><br>
</li>
<li><a href="https://aka.ms/phi3-hf" rel="nofollow">Phi-3 on Hugging Face</a><br>
</li>
<li>Phi-3 ONNX: <a href="https://aka.ms/phi3-mini-4k-instruct-onnx" rel="nofollow">4K</a> and <a href="https://aka.ms/phi3-mini-128k-instruct-onnx" rel="nofollow">128K</a></li>
</ul>

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