mannix / phi3-mini-4k

我们的模型并未针对所有下游目的进行专门的设计或评估。开发者选择用例时，应考虑语言模型的常见局限性，并在特定下游用例中使用前评估和缓解准确性、安全性和公平性问题，尤其是在高风险场景中。
开发者应了解并遵守与其用例相关的适用法律或法规（包括隐私、贸易合规法规等）。
本模型卡片中包含的内容不应被解释为或被视为限制或修改模型发布所依据的许可证。

负责任人工智能考量

与其他语言模型一样，Phi系列模型可能会以不公平、不可靠或冒犯性的方式表现。值得注意的一些限制行为包括

服务质量：Phi模型主要在英语文本上进行训练。非英语语言将表现出较差的性能。在训练数据中代表性较差的英语语言可能表现得比标准美式英语差。
危害 representation & 贫困化刻板印象：这些模型可能会过度或不足地表示某些群体，消除某些群体的 representation，或强化贬低或负面的刻板印象。尽管进行了安全训练后，由于不同群体 representation 的不同程度或训练数据中负面刻板印象的例子在现实世界模式和 societal偏见中的普遍性，这些局限性可能仍然存在。
不适当或冒犯性内容：这些模型可能会生成其他类型的不适当或冒犯性内容，这可能导致在没有额外特定于用例的缓解措施的情况下，不适当地部署敏感环境。
信息可靠性：语言模型可以生成无意义的或不准确的内容。
代码范围有限：Phi-3的大部分训练数据基于 Python，并使用“类型、数学、随机、集合、日期时间、迭代工具”等常见包。如果模型生成使用其他包或其他语言的脚本，我们强烈建议用户手动验证所有API使用。

开发者应采用负责任的人工智能最佳实践，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）。重要的考虑领域包括
+ 分配：模型可能不适用于可能对法律地位、资源分配或生活机会产生重大影响的场景，除非进行进一步评估并采用额外的去偏技术。

高风险场景：在可能导致不公平、不可靠或冒犯性输出且可能极其昂贵或导致损害的高风险场景中，开发者应评估使用模型的可适用性。这包括在敏感或专家领域提供建议（如法律或健康建议）时。应根据部署环境在应用层面实施额外的安全措施。
虚假信息：模型可能产生不准确的信息。开发者应遵循透明度最佳实践，并告知最终用户他们正在与一个AI系统交互。在应用层面，开发者可以构建反馈机制和管道，通过使用案例特定的上下文信息来定位响应，这种技术称为检索增强生成（RAG）。
有害内容的生成：开发者应评估输出上下文并使用适用于其使用案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制作等可能存在，开发者应确保其应用程序不违反适用的法律法规。

训练

模型

架构：Phi-3 Mini具有3.8B参数，是密集的解码 Transformer 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。它最适合使用聊天格式的提示。
上下文长度：128K个令牌
GPUs：512 H100-80G
训练时间：7天
训练数据：3.3T个令牌
输出：对输入生成的文本响应
日期：我们的模型是在2024年2月至4月期间训练的
状态：这是一个基于截至2023年10月的离线数据集静态训练的模型。随着我们改进模型，未来的版本可能会发布调整后的模型。

数据集

我们的训练数据包括各种来源，总计33万亿个令牌，是以下内容的组合：
1) 经过严格筛选以确保质量的公开可获得的文档、选择高质量的教育数据和代码；
2) 为了教学数学、编码、常识推理、世界通用知识（科学、日常活动、心理理论等）而全新创建的“教科书式”数据；
3) 覆盖各种话题的高质量聊天格式监督数据，以反映人类偏好在不同方面的看法，如指令遵循、真实性、诚实性和有益性。

软件

许可

该模型根据MIT许可授权。

商标

本项目中可能包含项目、产品或服务的商标或标志。Microsoft商标或标志的授权使用必须遵守并遵循Microsoft的商标与品牌指南。使用Microsoft商标或标志的修改版本的项目不得引起混淆或暗示Microsoft的赞助。任何第三方商标或标志的使用都受那些第三方政策所约束。

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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