mannix / phi3-mini-4k

Phi-3 Mini是微软的一个轻盈的3B前沿开放模型。已于2024年7月更新。

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7周前更新

7周前

fe9cca4ade43 · 2.6GB

说明

从fp32进行量化
使用i-matrixcalibration_datav3.txt

Phi-3是微软开发的一组开放AI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是一款参数为380亿的轻量级、世界领先的开源模型，它使用Phi-3数据集进行训练，该数据集包括合成数据和过滤后的公开网站数据，重点关注高质量和推理密集型属性。

该模型经过后训练过程，结合了监督微调和直接偏好优化，以确保精确执行指令和稳健的安全措施。

在评估常识、语言理解、数学、代码、长语篇和逻辑推理等基准测试时，Phi-3 Mini-4K-Instruct在参数少于130亿的模型中展现了强大且世界领先的性能。

Phi-3 Medium

Phi-3 Medium是一款参数为140亿的文本模型，性能优于Gemini 1.0 Pro。

预期用途

主要用例

该模型旨在用于英语的的商业和研究，为需要以下用途的应用程序提供服务：
1) 内存/计算受限的环境
2) 延迟受限的场景
3) 强大的推理（尤其是数学和逻辑）
4) 长语篇

我们设计的模型旨在加速语言和多模态模型的研究，作为生成式AI驱动的特性的构建块使用。

用例考虑因素

我们的模型并未特别设计和评估用于所有下游用途。开发者应考虑语言模型的常见限制，并在选择用例时进行评估，以及在使用特定下游用例之前评估和缓解准确度、安全性和公正性问题，特别是对于高风险场景。
开发者应意识到并遵守与他们用例相关的所有适用法律或法规（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被视为对模型发布许可的任何限制或修改。

负责任的人工智能考虑因素

与其他语言模型一样，Phi系列模型可能会以不公平、不可靠或冒犯的方式行事。应注意的一些限制行为包括

服务质量：Phi模型主要在英文文本上进行训练。其他语言的性能可能会更差。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的表现更差。
危害的表现和刻板印象的持续：这些模型可能会过分或不足地代表某些群体，消除某些群体的表现，或加强贬低或负面的刻板印象。尽管经过安全后训练，但由于不同群体代表性水平的差异或训练数据中反映现实世界模式和 societal偏见的不利刻板印象的普遍性，这些限制可能仍会存在。
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，如果没有额外的特定于用例的缓解措施，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制造看似合理但实际不准确或过时的内容。
代码范围有限：Phi-3培训数据的大多数基于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使用。

开发者应实施负责任的人工智能最佳实践，并负责确保特定的用例符合相关的法律和法规（例如隐私、贸易等）。应考虑的重要领域包括
分配：在进一步评估和额外的debiasing技术之前，模型可能不适合具有重大法律状态或资源分配或生活机会影响的情况（例如：住房、就业、信贷等）.+

高风险场景：开发人员应评估在可能产生不公平、不可靠或诽谤性输出的高风险场景中使用模型的相关性，因为这可能导致极其高昂的成本或造成损害。这包括在敏感或专家领域提供建议，在这些领域中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程序级别实施额外的安全措施。
错误信息：模型可能生成不准确的信息。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。在应用程序级别，开发人员可以构建反馈机制和管道，以使用用例特定的上下文信息定位响应，这是一种称为检索增强生成（RAG）的技术。
有害内容的生成：开发人员应评估输出的上下文和使用案例，并使用适合其用例的安全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制造可能，开发人员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模型具有38亿个参数，是一个仅具有密集解码器的Transformer模型。该模型使用监督式微调（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 tokens
GPU：512 H100-80G
训练时间：7天
训练数据：3.3T tokens
输出：对于输入生成的文本响应
日期：我们2024年2月至4月训练了模型
状态：这是一个根据截至2023年10月的离线数据集训练的静态模型。随着我们对模型的改进，可能还会发布微调模型的未来版本。

数据集

我们的训练数据包括各种来源，共计3.3万亿个tokens，是以下内容的组合
1) 经过严格筛选的质量高、精选高质量的教育数据和代码的公开可用文件；
2) 为教学数学、编程、常识推理、世界通用知识（科学、日常生活、心智理论等）而创建的“教科书式”的合成数据；
3) 覆盖各种主题的高质量聊天式监督数据，以反映人类对不同方面的偏好，如指示遵循、真实性、诚实和有用性。

软件

许可证

模型根据MIT许可证授权。

商标

此项目可能包含关于项目、产品或服务的商标或徽标。微软商标或徽标的授权使用应遵守并遵循微软的商标及品牌指南。在此项目的修改版本中使用微软的商标或徽标不得引起混淆或意味着微软的赞助。任何第三方商标或徽标的用法均受第三方政策的约束。

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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