mannix / phi3-mini-4k

我们的模型并不是为了所有下游用途而专门设计或评估的。开发者应在选择用例时考虑语言模型的一些常见局限性，并在特定下游用例中使用之前，评估和降低准确性、安全性和公平性。
开发者应意识到并遵守与其用例相关的适用法律或法规（包括隐私法、贸易合规法律等）。
本模型卡中包含的内容不应被视为或认为是对该模型发布许可的限制或修改。

负责任的人工智能考略

与其他语言模型一样，Phi 系列模型可能以不公、不可靠或冒犯性的方式表现。需要注意的一些限制行为包括

服务质量：Phi 模型主要在英语文本上训练。除英语以外的语言可能会表现较差。在训练数据中代表性较低的英语语言可能会比标准美式英语表现更差。
伤害的表示与刻板印象的延续：这些模型可能过度或不足地表示人群，消除某些群体的代表性，或强化贬低或消极的刻板印象。尽管经过安全的后训练，这些局限性可能仍然存在，因为不同群体的代表性水平和训练数据中负面刻板印象示例的普遍性存在差异。
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，在没有针对特定用例采取额外缓解措施的情况下，可能不适合在敏感环境中部署。
信息可靠性：语言模型可能生成不合逻辑的内容或编造听起来合理但实际上不准确或过时的内容。
代码的范围有限：Phi-3 大多数训练数据基于 Python，并使用诸如 "typing, math, random, collections, datetime, itertools" 等常见包。如果模型生成使用其他包或不同语言的脚本的 Python 脚本，我们强烈建议用户手动验证所有 API 使用。

开发者应采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的法律和法规（例如隐私、贸易等）。重要考略领域包括
+ 分配：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场景，除非进行进一步的评估和额外的去偏见技术。

高风险场景：开发人员应评估在高风险场景中使用模型（此处可能产生不公平、不可靠或冒犯性的输出，可能导致极高的成本或损害）的适用性。这包括在敏感或专家领域（如法律或医疗咨询）提供建议。根据部署环境，在应用层面实施额外的安全措施。
误导性信息：模型可能产生不准确的信息。开发人员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系统进行交互。在应用层面，开发人员可以构建反馈机制和管道，以使用特定用例的上下文信息来验证响应，这种技术被称为检索增强生成（RAG）。
有害内容的生成：开发人员应评估输出的上下文和使用适用的安全分类器或针对其用例定制的解决方案。
滥用：其他形式的滥用，如欺诈、垃圾邮件或恶意软件的生产，也是可能的，开发人员应确保其应用不违反适用的法律和法规。

训练

模型

架构：Phi-3 Mini拥有38亿参数，是一种密集的只解码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。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
训练时间：7天
训练数据：3.3T个令牌
输出：针对输入生成的文本
日期：我们的模型训练时间为2024年2月至4月
状态：这是一个在2023年10月之前下线的训练集上训练的静态模型。随着模型的改进，可能会在未来发布调整后的模型版本。

数据集

我们的训练数据包括广泛的来源，共计330亿个令牌，包括以下组合：
1）经过严格质量筛选的公开文档、精选高质量教育数据和代码；
2）为教学目的而新创建的“教科书式”数据，用于教授数学、编码、常识推理、世界通用知识（科学、日常生活、心理理论等）；
3）涵盖各个主题的高质量聊天格式监督数据，以反映人类不同方面的偏好，如指令遵循、真实性、诚实和有帮助性。

软件

许可证

该模型遵循MIT许可证。

商标

该项目可能包含项目、产品和服务的商标或徽标。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。在修改后的该项目的版本中使用Microsoft商标或徽标不得引起混淆或暗示Microsoft的赞助。使用第三方商标或徽标均须遵守那些第三方的政策。

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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