mannix / phi3-mini-4k

Phi-3 Mini是微软推出的一款轻量级的3B开放模型。于2024年7月更新。

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3786e9c0f13a · 2.2GB

README

从fp32量化
使用i-matrix calibration_datav3.txt

Phi-3是微软开发的一系列开放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是一种轻量级、最先进的开源模型，参数为3.8B，使用Phi-3数据集进行训练，包括合成数据和经过筛选的公开网站数据，重点在于高质量和推理密集型属性。

该模型经历了一个训练后处理过程，结合了监督微调和直接偏好优化，以确保精确遵循指令和稳健的安全措施。

在与其他常见的常识、语言理解、数学、代码、长文本和逻辑推理基准测试相比时，Phi-3 Mini-4K-Instruct在参数少于13亿个的模型中展示了强劲并处于领先水平的性能。

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。

使用目的

主要用例

该模型旨在用于英语的商业和研究目的。该模型适用于需要以下应用的场景：
1) 内存/计算受限的环境
2) 延迟约束的场景
3) 强大的推理（尤其是数学和逻辑）
4) 长文本

我们的模型旨在加速语言和多模态模型的研究，用作用于生成AI功能的构建模块。

用例考虑因素

我们的模型并非针对所有下游目的进行设计和评估。开发人员在选择用例时，应考虑语言模型的局限性，并在特定下游用例中使用前，评估和减轻准确性、安全性和公平性问题，尤其是对于高风险场景。
开发人员应了解并遵守与他们的使用案例相关的法律法规（包括隐私、贸易合规法等）。
本模型卡中包含的内容不应被解释为或视为对模型发布许可证的限制或修改。

负责任的人工智能考虑因素

与其他语言模型一样，Phi系列模型可能以不公平、不可靠或冒犯的方式表现。应关注的一些限制行为包括：

服务质量：Phi模型主要在英语文本上进行训练。除英语之外的语言将经历更差的表现。在训练数据中代表性较小的英语变体会比标准美式英语有更差的表现。
对伤害和刻板印象的表述与传播：这些模型可能会过度或不足地代表某些人群，消除某些群体的代表性，或强化贬低或负面的刻板印象。尽管经过训练后具有安全性，但由于不同群体代表性水平的不同或训练数据中负面刻板印象实例的普遍性，这些局限性可能仍然存在。
不恰当或冒犯性内容：这些模型可能会生成其他类型的不恰当或冒犯性内容，如果没有针对特定用例实施的附加缓解措施，可能不适合敏感场景。
信息可靠性：语言模型可以生成无意义的或捏造的内容，这些内容听起来合理但可能是错误的或过时的。
代码范围的限制：Phi-3的大部分训练数据基于Python，并使用诸如“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：可能需要进一步的评估和额外的去偏见技术，而模型可能不适合可能产生重大影响的场景，例如法律身份、资源配置或生活机会的分配（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、不可靠或冒犯性输出可能极为昂贵或导致伤害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知识领域提供建议（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AI系统进行交互。在应用层面，开发者可以构建反馈机制和流水线，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38亿个参数，是一个密集的仅解码Transformer模型。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。它最适合用于聊天格式的提示。
上下文长度：128K个token
GPUs：512 H100-80G
训练时间：7天
训练数据：3.3T个token
输出：对输入生成的文本
日期：我们的模型在2024年2月至4月之间进行训练
状态：这是一个基于截至2023年10月的离线数据集的静态模型。随着我们改进模型，可能发布调整后的模型的新版本。

数据集

我们的训练数据包括广泛的来源，总计3300万亿个token，包括
1）经过严格质量筛选的公开可用文件，选择了高质量的教育数据、代码；
2）为教学数学、编程、常识推理、世界知识（科学、日常活动、心理理论等）而创建的新合成数据，“教科书式”数据；
3）高质量的话题覆盖各种主题的聊天格式监督数据，以反映人类对不同方面的偏好，如指令遵循、真实性、诚实和帮助性。

软件

许可

该模型采用MIT许可。

商标

本项目可能包含项目、产品或服务的商标或徽标。Microsoft商标或徽标的合法使用受Microsoft商标和品牌指南约束，必须遵守Microsoft的商标 & 品牌指南。在此项目的修改版本中使用Microsoft商标或徽标不得引起混淆或暗示Microsoft赞助。第三方商标或徽标的任何使用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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