mannix / phi3-mini-4k

Phi-3 Mini 是微软的一个轻量级 3B 先锋开放模型。于 2024 年 7 月更新。

139 提交 7 周前更新

7 周前更新

7 周前

c1260aa14bbf · 3.1GB

说明文件

从 fp32 进行量化
使用 i-matrix 校准数据 calibration_datav3.txt

Phi-3 是微软开发的一个开源AI模型系列。

参数大小

Phi-3 Mini – 3B参数

上下文字符窗口大小

4k ollama run mannix/phi3-mini-4k

Phi-3 Mini

Phi-3 Mini 是一个3.8B参数，轻量，最先进的开源模型，使用Phi-3数据集进行训练，包括合成数据和经过过滤的公开可用的网站数据，重点关注高质量和推理密集度的特性。

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采取稳健的安全措施。

当与测试常识、语言理解、数学、代码、长上下文和逻辑推理的基准测试进行比较时，Phi-3 Mini-4K-Instruct在参数少于13亿的模型中展现出了强大和最先进的表现力。

Phi-3 Medium

Phi-3 Medium是一款14B参数的语言模型，并且性能优于Gemini 1.0 Pro。

意向用途

主要使用案例

该模型的设计用于英语商业和研究用途。该模型适用于需要
1) 内存/计算受限的环境
2) 延迟受限的场景
3) 强有力的推理（尤其是数学和逻辑）
4) 长上下文

我们的模型旨在加速对语言和多媒体模型的研究，用作生成人工智能功能的构建块。

使用案例注意事项

我们的模型并非为所有下游用途而专门设计或评估。开发者在选择使用案例时，应考虑语言模型的共同限制，并在特定下游使用案例中（尤其是高风险场景）使用前评估和减轻准确度、安全性和公平性的问题。
开发人员应了解并遵守与其使用案例相关的适用法律或法规（包括隐私、贸易合规法律等）。
本模型卡中不包含的内容不应被理解为或视为对模型发布许可的任何限制或修改。

负责任的AI考虑事项

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。一些应关注的限制行为包括

服务质量：Phi模型主要针对英语文本进行训练。非英语语言将遇到更差的表现。在训练数据中代表性较低的语言变种可能比标准美式英语表现更差。
伤害的表示及刻板印象的延续：这些模型可能会过分或不足地呈现某些人群，消除某些群体的代表性，或强化贬低或负面的刻板印象。尽管经过安全后培训，由于不同群体在不同级别的代表性以及训练数据中负面刻板印象的例子普遍存在，这些限制可能仍然存在。
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，在没有针对特定使用案例采取的额外缓解措施的情况下，可能不适合敏感环境。
信息可靠性：语言模型可以生成无意义的内容，或制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：大多数Phi-3训练数据基于Python，并使用“typing、math、random、collections、datetime、itertools”等常见包。如果模型生成利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。

开发人员应应用负责任的AI最佳实践，并确保特定使用案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：模型可能不适用于可能对法律地位、资源分配或生活机会产生重大影响的场景，除非进行进一步评估并采用额外的去偏见技术。

高风险场景：开发人员应评估在风险很高的情况下使用模型适宜性， outputs 可能极其昂贵或导致伤害（例如，在法律或健康建议等敏感或专家领域中）。应根据部署环境在应用级别实施额外保护措施。
错误信息：模型可能产生不准确的信息。开发者应遵循透明度最佳实践，并告知最终用户提供的是人工智能系统。
有害内容的生成：开发者应评估输出的上下文，并使用适用于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生产也可能发生，开发者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具有38亿参数，是一个密集的单解码器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的一致性。
输入：文本。它最适用于聊天格式的提示。
上下文长度：128K个令牌
GPU：512个H100-80G
训练时间：7天
训练数据：3.3T个令牌
输出：对输入的生成的文本响应
日期：我们的模型训练时间为2024年2月至4月
状态：这是一个基于截至2023年10月的离线数据集的静态模型。随着模型改进，我们将可能发布调整后的模型的未来版本。

数据集

我们的训练数据包括广泛的来源，总共有3300亿个令牌，包括以下组合：
1) 经过严谨筛选的公开发布文献、高质量的教育数据、以及代码；
2) 为教学数学、编程、常识推理、世界观常识（科学、日常活动、心智理论等）而创建的新合成，“教科书式”数据；
3) 高质量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、诚实和有益性。

软件

许可证

该模型采用MIT授权。

商标

本项目可能包含项目、产品或服务的商标或标志。Microsoft商标或标志的授权使用必须遵循并遵循Microsoft的商标和品牌指南。在此项目的修改版本中使用Microsoft商标或标志时，不得引起混淆或暗示Microsoft资助。第三方商标或标志的使用必须遵守这些第三方政策。

资源

![image.png](https://ollama.ac.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.ac.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.ac.cn/library/phi3:latest#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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