mannix / phi3-mini-4k

Phi-3 Mini是微软推出的3B最新开源模型，轻量级。2024年7月更新。

139次拉动更新于7周前

7周前更新

7周前

f7a09136b986 · 1.5GB

自述文件

从 fp32 量化
使用 i-matrix 《calibration_datav3.txt》校准

Phi-3 是由微软开发的一系列开源 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 是一个具有 3.8B 参数的最新型轻量级开源模型，使用 Phi-3 数据集进行训练，该数据集包含合成数据以及经过筛选的公开可用的网站数据，重点在于高质量和推理密集型属性。

该模型经过了一项后训练过程，结合了监督微调和直接偏好优化，以确保精确遵循指令和强有力的安全措施。

在与测试常见常识、语言理解、数学、代码、长上下文和逻辑推理的基准对比中，Phi-3 Mini-4K-Instruct在参数少于13亿的小模型中展示了强大的性能和领先的水平。

Phi-3 Medium

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

预期用途

主要用例

该模型的目的是用于英语的商业和研究，为需要以下用途的应用提供支持：
1) 内存/计算受限环境
2) 延迟受限场景
3) 强大推理（特别是数学和逻辑）
4) 长上下文

我们的模型旨在加速语言和多功能模型的研究，可作为一个构建构建器，用于生成智能特征。

用例考虑

我们的模型并非专门设计或评估用于所有下游目的。开发者选择用例时，应考虑语言模型的常见局限性，并在特定下游用例中使用之前（尤其是对于高风险场景），评估和减轻准确性、安全性和不公。
开发人员应了解和遵守与他们的用例相关的适用法律或法规（包括隐私、贸易合规法规等）。
本模型卡中包含的任何内容均不应被解释为或被视为对模型所发布许可证的限制或修改。

负责任的人工智能考量

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方式表现。一些应警惕的限制行为包括

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得更差。在训练数据中代表性不足的英语变体可能比标准美国英语表现得更差。
危害的表现和刻板印象的延续：这些模型可能在某些人群的群体中过分或不足地代表，消除某些群体的代表性，或加强贬低或不利的刻板印象。尽管经过训练后的安全措施，由于不同群体的代表性不同或负面刻板印象在训练数据中的普遍性，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，在没有针对特定用例的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：语言模型可能会生成无意义的內容或伪造听起来合理但实际上不准确或过时的內容。
代码的局限性：Phi-3的训练数据 majority是基于Python的，并使用“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的使用。

开发人员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法律和法规（例如隐私、贸易等）。重要的考虑领域包括
+ 配置：在没有进一步的评估和额外的去偏见技术的情况下，模型可能不适合可能对法律地位或资源分配或生活机会产生重大影响的场景（例如住房、就业、信用等）。

高风险场景：开发者应评估在高风险场景中（如输出不公平、不可靠或令人反感时可能导致巨大损失或造成伤害）使用模型的适用性。这包括在敏感或专业领域提供咨询建议（例如法律或健康咨询）时。应根据部署上下文在应用层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。开发者应遵循透明度最佳实践，并通知最终用户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，以使用用例特定的上下文信息来定位响应，这种方法被称为检索增强生成（RAG）。
生成有害内容：开发者应评估输出的上下文，并使用适合其用例的安全分类器或自定义解决方案。
滥用：可能存在其他滥用形式，如欺诈、垃圾邮件或恶意软件生产，开发者应确保其应用不违反适用的法律和法规。

培训

模型

架构：Phi-3 Mini 模型具有 3.8B 个参数，是一个密集的单解码器 Transformer 模型。该模型使用监督微调（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。最好用于聊天格式的提示。
上下文长度：128K 令牌
GPU：512 H100-80G
培训时间：7 天
培训数据：3.3T 令牌
输出：对输入生成文本响应
日期：我们的模型于 2024 年 2 月至 4 月期间进行培训
状态：这是一个基于截止日期为 2023 年 10 月的离线数据集进行训练的静态模型。随着模型改进，可能发布调优模型的新版本。

数据集

我们的培训数据来源广泛，总计 3.3 万亿令牌，包括
1) 经过严格筛选的女性、高质量教育数据以及代码的公开可用文档；
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）而创建的新的“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，以反映人类在不同方面的偏好，如指令遵循、真实性、诚实和帮助性。

软件

许可证

该模型根据MIT 许可证授权。

商标

本项目中可能包含项目、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循并遵循Microsoft 的商标和品牌指南。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 的赞助。任何第三方商标或徽标的用途均受该第三方政策约束。

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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