mannix / phi3-mini-4k

我们的模型不是专为所有下游目的设计和评估的。开发者应在选择用例时考虑语言模型的共同限制，并在特定下游用例中使用之前，评估和减轻准确度、安全性和公平性的问题，特别是在高风险场景下。
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、贸易合规法规等）。
本模型卡中包含的内容不得解释为或视为限制或修改该模型发布许可的条款。

负责任的人工智能考虑因素

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进行行为。以下是一些要了解的限制行为：

服务质量：Phi 模型主要在英语文本上进行训练。除英语之外的语言将表现出糟糕的性能。在训练数据中代表性较弱的英语变体会比标准美国英语表现更差。
对危害的表示和刻板印象的延续：这些模型可能会过度或不足地代表某些群体，消除某些群体的代表性，或强化贬低性或负面刻板印象。尽管进行了培训后的安全性措施，但由于不同群体代表性级别不同，或训练数据中负面刻板印象实例的普遍性，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其他类型的不适当或冒犯性内容，可能会在没有附加特定于用例的缓解措施的情况下，使部署变得不适宜。
信息可靠性：语言模型可以生成不可理喻的内容或编造看似合理但实际上不准确或过时的内容。
对代码的适用范围的限制：Phi-3 训练数据的大部分基于 Python，使用了如“typing、math、random、collections、datetime、itertools”等常见包。如果模型生成使用其他包或其他语言的脚本的 Python 脚本，我们强烈建议用户手动验证所有 API 使用。

开发者应应用负责任的人工智能最佳实践，并负责确保特定用案符合相关法律和法规（例如隐私、贸易等）。重要的考虑领域包括：
+ 分配：模型可能不适合对法律地位、资源配置或生活机会（例如住房、就业、信用等）有潜在重大影响的场景，除非进行进一步评估和额外的去偏技术。

高风险场景：开发者在判别用模型进行高风险操作是否合适的时，应考虑到输出可能不公平、不可靠或具有攻击性，可能导致极高的成本或损害。这包括在准确性和可靠性至关重要的敏感或特殊领域（如法律或健康建议）提供咨询。需要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AI系统互动。在应用层面，开发者可以构建反馈机制和管道，使用针对特定用例的上下文信息来锚定回应，这是一种称为检索增强生成（RAG）的技术。
产生有害内容：开发者应评估输出的上下文和使用用例适当的安全性分类器或定制解决方案。
滥用：其他形式的滥用，如欺诈、垃圾邮件或恶意软件生成也可能发生，开发者应确保其应用不会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，是密集型仅解码器Transformer模型。模型通过监督微调和直接偏好优化（DPO）进行微调以确保与人类偏好和安全指南的一致性。
输入：文本。最适合使用聊天格式的提示。
上下文长度：128K个标记
GPU：512 H100-80G
培训时间：7天
培训数据：3.3T个标记
输出：对输入生成的文本
日期：我们的模型于2024年2月至4月之间训练
状态：这是一个在截至2023年10月的离线数据集上训练的静态模型。随着模特的改进，未来可能会发布微调模型的版本。

数据集

我们的培训数据包括广泛的来源，共涉及3.3万亿个标记，是以下内容的组合
1) 经过严格质量过滤的公开文档，选择高质量的教育数据和小程序；
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）等目的创造的新合成、“教科书式”数据；
3) 有关各种主题的高质量聊天格式监督数据，以反映人类在指示遵循、真实性、诚实和有用性等方面的偏好。

软件

许可

该模型采用MIT许可。

商标

该项目可能包含属于项目、产品或服务的商标或徽标。授权使用微软的商标或徽标必须遵守并遵循微软的商标和品牌指南。不得通过修改该项目版本使用微软的商标或徽标造成混淆或暗示微软的赞助。任何对第三方商标或徽标的用途均受那些第三方政策的管理。

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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