mannix / phi3-mini-4k

我们的模型并未专门设计或评估用于所有下游用途。开发者在选择用例时，应考虑语言模型的常见局限性，并在使用之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。
开发人员应了解并遵守与他们的用例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任何内容均不应被解释为或视为对模型下许可证的约束或修改。

负责任的人工智能考虑事项

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。以下是一些需要注意的限制行为：

服务质量：Phi模型主要使用英语文本进行训练。英语之外的语言可能会体验到更差的表现。在训练数据中代表性较小的英语变体可能会比标准美式英语表现出更差的表现。
危害表示及刻板印象的持续性：这些模型可能会过分或不足地代表某些群体，消除某些群体的表示，或加强侮辱性或负面刻板印象。尽管经过后的安全措施，由于不同群体表示水平的不同或训练数据中负面刻板印象样本的普遍性，这些限制可能仍然存在。
不适当或冒犯内容：这些模型可能还会生成其他类型的不适当或冒犯内容，在未采取特定于用例的额外缓解措施的情况下，这可能会使部署在不敏感环境中不适当。
信息可靠性：语言模型可能生成无意义的内容或捏造听起来合理但实际上不准确或过时的内容。
代码范围有限：大部分Phi-3训练数据基于Python，并使用如“typing, math, random, collections, datetime, itertools”等常用包。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。

开发人员应应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法律和法规（例如隐私、贸易等）。重要的考虑领域包括：
+ 分配：在没有进一步评估和额外的去偏见技术的情况下，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（例如住所、就业、信贷等）。

高风险场景：开发者应评估在高风险场景中使用模型的适用性，在这些场景中，不公平、不可靠或攻击性的输出可能代价极高或导致损害。这包括在敏感或专业领域提供建议，其中准确性和可靠性至关重要（例如法律或健康咨询）。应根据部署上下文在应用程序级别实施附加安全措施。
错误信息：模型可能会产生不准确的信息。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。在应用程序级别，开发者可以构建反馈机制和管道，通过使用特定于场景的上下文信息来定位响应，这是一种称为检索增强生成（RAG）的技术。
生成有害内容：开发者应评估输出在其上下文中的适用性，并使用适用于其使用情况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和法规。

培训

模型

架构：Phi-3 Mini具有38亿个参数，是一个仅解码的密集Transformer模型。该模型通过监督微调（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
GPU：512 H100-80G
培训时间：7天
培训数据：3.3T个令牌
输出：针对输入生成的文本
日期：我们的模型在2024年2月至4月之间进行训练
状态：这是一个在2023年10月之前的数据集上离线训练的静态模型。随着模型改进，可能会发布微调模型的未来版本。

数据集

我们的训练数据包括种类繁多的来源，总数为3.3万亿个令牌，是以下内容的组合
1）经过严格质量筛选的公开可用的文档，选择了高质量的 educational 数据和代码；
2）为教学数学、编码、常识推理、世界通用知识（科学、日常生活、心理理论等）而创建的新合成，“教科书式”数据；
3）高质量聊天格式监督数据，涵盖各种主题，反映人类在各种方面的偏好，例如指令遵循、真实性、诚实和帮助性。

软件

许可

该模型受MIT许可。

商标

此项目可能包含项目、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守并遵循Microsoft的商标和品牌指南。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。任何第三方商标或徽标的用途均受第三方政策约束。

资源

![image.png](https://ollama.ac.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.ac.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.ac.cn/library/phi3:latest#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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