mannix / phi3-mini-4k

Phi-3 Mini 是微软的 3B 状态领先的轻量级开源模型。于 2024 年 7 月更新。

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7 周前更新

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453bc4daa2d2 · 1.7GB

Readme

从fp32量化
使用i-matrixcalibration_datav3.txt

Phi-3是微软开发的开源AI模型系列。

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 是一个包含3.8B参数的轻量级、开创性开放模型。它使用Phi-3数据集进行训练，包括合成数据和过滤后的公开网站数据，特别关注高质量和推理密集性属性。

该模型经过了后训练过程，结合了监督微调和直接偏好优化，以确保精确遵循指令并采取稳健的安全性措施。

在与常识、语言理解、数学、代码、长文本上下文和逻辑推理的基准测试中，Phi-3 Mini-4K-Instruct在参数少于13亿的模型中展现了强大而开创性的性能。

Phi-3 Medium

Phi-3 Medium 是一个14B参数的语言模型，其性能优于 Gemini 1.0 Pro。

使用场景

主要使用场景

该模型旨在用于英文的商业和研究领域。模型可用于需要以下功能的应用程序
1) 内存/计算约束环境
2) 延迟限制场景
3) 强大的推理能力（特别是数学和逻辑）
4) 长文本上下文

我们的模型旨在加速语言和多模态模型的研究，作为生成AI功能的构建块。

使用场景考虑事项

我们的模型并没有针对所有下游用途专门设计或评估。开发者在选择使用场景时，应考虑语言模型的常见限制，并在特定下游使用场景中使用之前，评估和缓解准确性、安全和公正问题，尤其是在高风险场景中。
开发者应了解和遵守与其使用场景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应被解释为或视为对该模型发布所依据许可的限制或修改。

负责任的人工智能考虑事项

与其他语言模型一样，Phi系列模型可能以不公平、不可靠或令人反感的的方式行事。一些需要注意的限制行为包括

服务质量：Phi模型主要在英语文本上进行训练。除英语以外的语言可能会表现得更差。在训练数据中代表性较少的英语变体可能比标准美国英语表现得更差。
伤害的表现与刻板印象的延续：这些模型可能过度或不足地代表某些群体，消除某些群体的表示，或加强贬低或负面的刻板印象。尽管经过安全后训练，由于不同群体代表水平的不同或反映现实世界模式和社交偏见的训练数据中负面刻板印象的普遍性，这些限制仍可能存在。
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，在没有针对该使用场景的具体缓解措施的情况下，可能不适合在敏感环境中部署。
信息可靠性：语言模型可以生成无意义的内容或伪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：Phi-3训练数据的大部分是建立在Python基础上的，使用常见的包如“typing, math, random, collections, datetime, itertools”。如果模型生成利用其他包或其他语言中的脚本的Python脚本，我们强烈建议用户手动验证所有API的使用。

开发者应应用负责任的人工智能最佳实践，并负责确保特定的使用场景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：在不进行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影响（如住房、就业、信贷等）的场景。

高风险场景：开发者应评估在高风险场景中使用模型（例如可能产生不公平、不可靠或冒犯性的输出时可能极其昂贵的或可能导致损害的情形）的适用性。这包括在敏感或专家领域提供建议，在这些领域准确性和可靠性至关重要（例如：法律或健康建议）。应根据部署环境在应用层实施额外安全措施。
虚假信息：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，并向最终用户通知他们正在与人工智能系统交互。在应用层，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巩固响应，这种技术称为检索增强生成（RAG）。
有害内容的生成：开发者应评估输出的上下文和使用适用于其用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生产可能可行，开发者应确保其应用程序不违反适用的法律和法规。

训练

模型

架构：Phi-3 Mini具有3.8B个参数，是仅解码的密集型Transformer模型。该模型通过与监督微调（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进行提示。
上下文长度：128K个标记
GPU：512 H100-80G
训练时间：7天
训练数据：3.3T个标记
输出：对输入生成的文本响应
日期：我们的模型训练时间为2024年2月至4月
状态：这是一个在截止日期为2023年10月的离线数据集上训练的静态模型。随着模型性能的提升，可能发布经过调整的模型版本。

数据集

我们的训练数据包括各种来源的总计3300亿个标记，是以下内容的组合：
1) 经过严格质量过滤的公开可用的文件，选择高质量的教育数据和代码；
2) 为教学数学、编码、常识推理、世界一般知识（科学、日常活动、心智理论等）而创建的新创建的“教科书式”数据；
3) 覆盖各种主题的高质量聊天格式监督数据，以反映人类对不同方面的偏好，如指令遵循、真实性、诚实和帮助。

软件

许可证

该模型采用MIT许可证。

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必须遵守并遵循Microsoft商标和品牌指南。在修改后的本项目中使用Microsoft商标或徽标不得造成混淆或暗示Microsoft的支持。第三方商标或徽标的任何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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