mannix / phi3-mini-4k

我们未特别为所有下游用途设计或评估我们的模型。开发者在选择使用案例时，应考虑语言模型常见的局限性，并在特定下游用例中使用之前，评估和缓解准确度、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解释为或视为对该模型发布下许可的任何限制或修改。

负责任的人工智能考量

与其它语言模型一样，Phi系列模型可能以不公平、不可靠或冒犯性的方式行事。以下是一些需注意的限制行为：

服务质量：Phi模型主要在英语文本上进行训练。非英语将表现出较差的性能。在训练数据中代表性较小的英语变体可能比标准美式英语表现得差。
对受害者的描绘及刻板印象的延续：这些模型可能过度或不足代表某些人群，抹去某些群体的代表，或强化贬低或负面的刻板印象。尽管经过安全性的后培训，这些局限性可能仍然存在，这是因为不同群体代表性的水平不同或在训练数据中存在反映现实模式和社交偏见的负面刻板印象的例子。
不适当或冒犯性内容：这些模型可能产生其他类型的不适当或冒犯性内容，这可能需要在特定用例中实施额外的缓解措施，以使其适用于敏感环境。
信息可靠性：语言模型可以生成无意义的内容或制造看似合理但其实是错误或过时的内容。
代码的有限应用：大多数Phi-3训练数据基于Python，使用“typing、math、random、collections、datetime、itertools”等常用包。如果模型生成使用其他包或不同语言中的脚本，我们强烈建议用户手动验证所有API使用。

开发者应遵循负责任的AI最佳实践，并负责确保特定用案符合相关的法律和法规（例如隐私、贸易等）。重要考虑因素包括：
+ 配置：模型可能不适用于可能导致法律地位、资源分配或生活机会的影响的场景，除非进行进一步评估和额外的去偏差技术。

高风险场景：开发者在使用模型于高风险场景时应评估模型的适用性，在这些场景中，不公平、不可靠或冒犯性的输出可能代价极高或导致危害。这包括在准确性和可靠性至关重要的敏感或专家领域提供咨询（例如法律或健康咨询）。应根据部署环境在应用层面实施额外的保障措施。
错误信息：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，以将响应建立在特定用例和上下文信息的基础上，这种技术称为检索增强生成（RAG）。
有害内容生成：开发者应评估输出内容的上下文，并使用适用于其用例的安全分类器或定制解决方案。
滥用：其他滥用形式，如欺诈、垃圾邮件或恶意软件生产可能是可能的，开发者应确保其应用程序不违反适用的法律法规。

培训

模型

架构：Phi-3 Mini拥有3.8B个参数，是一个密集解码-only Transformer模型。通过使用监督微调（SFT）和直接偏好优化（DPO）来微调模型，以确保与人类偏好和安全指南的一致性。
输入：文本。它最适合用于聊天格式提示。
上下文长度：128K个标记
GPU：512个H100-80G
训练时间：7天
训练数据：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月接受训练
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。随着我们改进模型，可能会发布调整后模型的未来版本。

数据集

我们的训练数据包括广泛多样的来源，总数为3.3万亿个标记，包括
1) 经过严格质量过滤的公开可用的文档，选择高质量的教育资源和代码；
2) 为教学数学、编码、常识推理、世界一般知识（科学、日常活动、心智理论等）目的而创建的新合成“教科书式”数据；
3) 高质量的聊天格式监督数据，涵盖各种主题，反映人类对指令遵循、真实性、诚信和帮助性等方面的偏好。

软件

许可

该模型根据MIT许可进行许可。[链接]

商标

本项目中可能包含有关项目、产品或服务的商标或徽标。Microsoft商标或徽标的授权使用需遵守并跟随[链接]。在此项目的修改版本中使用Microsoft商标或徽标不得引起混淆或暗示Microsoft的赞助。任何使用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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