mannix / phi3mini-4k

Phi-3 Mini 是微软推出的一个轻量级的 3B 状态的开放模型。2024 年 7 月更新。

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README

从 fp32 进行量化
使用 i-matrix 校准数据 calibration_datav3.txt

Phi-3 是微软开发的一系列开放人工智能模型。

参数大小

Phi-3 Mini - 3B 参数

上下文窗口大小

4k ollama run mannix/phi3-mini-4k

Phi-3 Mini

Phi-3 Mini 是一个 3.8B 参数，轻量级，最先进的开放模型，使用 Phi-3 数据集训练，该数据集包含合成数据和过滤后的公开可用网站数据，重点关注高质量和推理密集型属性。

该模型经过了训练后处理，结合了监督微调和直接偏好优化，以确保精确遵守指令和强大到位的安全措施。

对比常见理性、语言理解、数学、代码、长上下文和逻辑推理的基准测试，Phi-3 Mini-4K-Instruct在参数低于130亿个的模型中展示了强大且处于业界领先水平的性能。

Phi-3 Medium

Phi-3 Medium是一个拥有140亿个参数的语言模型，其性能优于Gemini 1.0 Pro。

目的用途

主要使用场景

该模型旨在用于英语商业和研究领域。该模型可以为需要应用的提供用途，包括
1) 内存/计算受限的环境
2) 延迟敏感的场景
3) 强大的推理能力（尤其是数学和逻辑）
4) 长上下文

我们的模型旨在加速语言和多元建模的研究，用作生成人工智能特征的基础模块。

使用场景考虑

我们的模型并未针对所有下游用途进行专门的开发和评估。开发者应在选择使用场景时考虑语言模型的共有局限性，并在特定下游用途中使用前，特别是在高风险场景中，评估和减轻准确性、安全性和公平性问题。
开发者应了解并遵守与其使用案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视为或被认为是限制或修改模型发布所依据许可证的条款。

负责任人工智能考虑

与其他语言模型一样，Phi系列模型可能表现出不公平、不可靠或冒犯性的行为。以下是一些需要关注的限制行为：

服务质量：Phi模型主要在英语文本上训练。非英语语言可能会表现出较差的性能。在训练数据中代表性较低的英语变体会比标准美式英语表现出更差的成绩。
伤害表现的代表性与刻板印象的持续：这些模型可能会过分或不足地代表某些群体，消除某些群体的代表性，或者强化贬低性或负面刻板印象。尽管进行过安全后训练，但不同群体或负面的训练数据例子（反映现实世界模式和社会偏见）的代表水平不同，这些局限性可能仍然存在。
不适当或冒犯性内容：这些模型可能产生其他类型的不适当或冒犯性内容，在敏感情境中部署时可能需要额外的具体于使用场景的缓解措施。
信息可靠性：语言模型可能会生成不合逻辑的内容或虚构听起来合理但实际上不准确或过时的内容。
代码范围的局限性：Phi-3的训练数据主要基于Python，并使用常见的包如“typing、math、random、collections、datetime、itertools”。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应用。

开发者应应用负责任人工智能的最佳实践，并负责确保特定使用案例符合相关的法律和法规（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附加去偏技术的情况下，模型可能不适合可能对法律地位、资源分配或生活机会产生重大影响的情景（例如住房、就业、信贷等）。

高风险场景：开发者应评估在可能产生不公平、不可靠或冒犯性输出的高风险场景中使用模型的适用性。这包括在需要准确性和可靠性的敏感或专业领域提供咨询（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人工智能系统交互。在应用级别，开发者可以构建反馈机制和管道，将响应基于特定用例的上下文信息，这种技术被称为检索增强生成（RAG）。
有害内容的生成：开发者应评估输出的上下文和使用可用安全性分类器或适合其用例的自定义解决方案。
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拥有38亿个参数，是一个密集型仅解码Transformer模型。该模型通过监督微调（SFT）直接偏好优化（DPO）进行了微调，以确保与人类偏好和安全指南保持一致。
输入：文本。它最适合以聊天格式进行的提示。
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：对输入生成的响应文本
日期：我们的模型在2024年2月至4月之间进行了训练
状态：这是一个基于2023年10月截止日期的离线数据集的静态模型。随着我们对模型进行改进，可能还会发布调优模型的未来版本。

数据集

我们的培训数据包括广泛的各种来源，总共有3.3万亿个标记，是一个结合
1)经过严格质量筛选的公开可用的文档，选拔高质量教育数据和代码；
2)为教学数学、编码、常识推理、世界一般知识（科学、日常生活、心智理论等）目的而创建的新型合成、“教科书式”数据；
3)高质量聊天格式监督数据，涵盖各种主题，以反映人类对不同方面（如指令遵循、真实性、诚实和有用性）的偏好。

软件

许可证

该模型根据MIT许可证发行。

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受Microsoft商标和品牌指南的约束，并必须遵循。在此项目的修改版本中使用Microsoft的商标或徽标不得造成混淆或暗示Microsoft的资金支持。任何第三方商标或徽标的用途均受那些第三方政策的约束。

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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