phi3 - Ollama 框架

我們的模型並非專門為所有下游用途而設計或評估。開發人員在選擇使用案例時，應考量語言模型的常見限制，並在使用於特定下游使用案例（尤其是高風險場景）之前，評估並減輕準確性、安全性及公平性。開發人員應注意並遵守適用於其使用案例的法律或法規（包括隱私、貿易合規法律等）。本模型卡中包含的任何內容均不應解釋或視為對模型發布所依據的許可證的限制或修改。

負責任的 AI 考量

與其他語言模型一樣，Phi 系列模型可能會以不公平、不可靠或冒犯的方式運作。需要注意的一些限制行為包括：

服務品質：Phi 模型主要以英文文本進行訓練。非英語語言的效能會較差。訓練資料中代表性較低的英語語言變體，其效能可能比標準美式英語更差。
危害的呈現與刻板印象的延續：這些模型可能會過度或過少呈現某些人群，抹除某些人群的代表性，或強化貶低或負面的刻板印象。儘管經過安全後訓練，但由於不同群體代表性的程度不同，或反映現實世界模式和社會偏見的訓練資料中負面刻板印象的例子普遍存在，這些限制可能仍然存在。
不當或冒犯性內容：這些模型可能會產生其他類型的不當或冒犯性內容，這可能會使其不適合在敏感環境中部署，除非採取針對特定使用案例的額外緩解措施。
資訊可靠性：語言模型可能會產生無意義的內容，或捏造聽起來合理但不準確或過時的內容。
程式碼的有限範圍：Phi-3 的大多數訓練資料都基於 Python，並使用常見的套件，例如「typing、math、random、collections、datetime、itertools」。如果模型產生使用其他套件的 Python 腳本，或其他語言的腳本，我們強烈建議使用者手動驗證所有 API 用法。

開發人員應採用負責任的 AI 最佳實務，並負責確保特定使用案例符合相關法律法規（例如隱私、貿易等）。重要的考量領域包括：+ 分配：模型可能不適用於可能對法律地位或資源或生活機會分配產生重大影響的場景（例如：住房、就業、信貸等），除非經過進一步評估和額外的去偏見技術。

高風險場景：開發人員應評估在高風險場景中使用模型的適用性，在這些場景中，不公平、不可靠或冒犯性的輸出可能會造成極高的代價或導致傷害。這包括在準確性和可靠性至關重要的敏感或專業領域（例如：法律或健康建議）中提供建議。應根據部署環境在應用程式層級實施額外的安全措施。
錯誤資訊：模型可能會產生不準確的資訊。開發人員應遵循透明度最佳實務，並告知終端使用者他們正在與 AI 系統互動。在應用程式層級，開發人員可以建置回饋機制和管道，以使用案例特定的上下文資訊來根據回應，這是一種稱為檢索增強生成 (RAG) 的技術。
有害內容的產生：開發人員應根據其上下文評估輸出，並使用適用於其使用案例的可用安全分類器或自訂解決方案。
濫用：可能存在其他形式的濫用，例如詐欺、垃圾郵件或惡意軟體產生，開發人員應確保其應用程式不違反適用的法律法規。

訓練

模型

架構：Phi-3 Mini 具有 3.8B 參數，是一種密集、僅解碼器的 Transformer 模型。該模型使用監督式微調 (SFT) 和直接偏好最佳化 (DPO) 進行微調，以確保與人類偏好和安全準則保持一致。
輸入：文字。最適合使用聊天格式的提示。
上下文長度：128K tokens
GPU：512 H100-80G
訓練時間：7 天
訓練資料：3.3T tokens
輸出：針對輸入產生的文字
日期：我們的模型在 2024 年 2 月至 4 月之間進行訓練
狀態：這是一個靜態模型，在截至 2023 年 10 月的離線資料集上進行訓練。隨著我們改進模型，未來可能會發布已調整模型的版本。

資料集

我們的訓練資料包含各種來源，總計 3.3 兆個 tokens，並且是以下各項的組合：1) 經過嚴格品質篩選的公開文件、選定的高品質教育資料和程式碼；2) 為教學數學、程式設計、常識推理、世界通用知識（科學、日常活動、心智理論等）而新建立的合成「教科書式」資料；3) 高品質聊天格式監督資料，涵蓋各種主題，以反映人類在不同方面（例如指令遵循、真實性、誠實和樂於助人）的偏好。

軟體

許可證

該模型已根據 MIT 許可證授權。

商標

此專案可能包含專案、產品或服務的商標或標誌。Microsoft 商標或標誌的授權使用受 Microsoft 商標和品牌指南的約束，且必須遵循該指南。在本專案的修改版本中使用 Microsoft 商標或標誌不得造成混淆或暗示 Microsoft 贊助。任何第三方商標或標誌的使用均受該第三方的政策約束。

資源

Phi-3 is a family of open AI models developed by Microsoft.

## Parameter sizes

- [Phi-3 Mini](https://ollama.dev.org.tw/library/phi3:mini) – 3B parameters – `ollama run phi3:mini`
- [Phi-3 Medium](https://ollama.dev.org.tw/library/phi3:medium) – 14B parameters – `ollama run phi3:medium`

## Context window sizes

> Note: the 128k version of this model requires [Ollama 0.1.39](https://github.com/ollama/ollama/releases/tag/v0.1.39) or later.

- 4k `ollama run phi3:mini` `ollama run phi3:medium`
- 128k `ollama run phi3:medium-128k`

![image.png](https://ollama.dev.org.tw/assets/library/phi3/83b3de66-82d8-4455-9117-256802c1b82e)

## Phi-3 Mini

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.

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.

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.

## Phi-3 Medium

Phi-3 Medium is a 14B parameter language model, and outperforms Gemini 1.0 Pro.

![image.png](https://ollama.dev.org.tw/assets/library/phi3/2868e29b-3bba-4c4a-a6ed-1a27fb102867)

## Intended Uses

**Primary use cases**

The model is intended for commercial and research use in English. The model provides uses for applications which require
1) memory/compute constrained environments
2) latency bound scenarios
3) strong reasoning (especially math and logic)
4) long context

Our model is designed to accelerate research on language and multimodal models, for use as a building block for generative AI powered features.

**Use case considerations**

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.
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.
Nothing contained in this Model Card should be interpreted as or deemed a restriction or modification to the license the model is released under.

## Responsible AI Considerations
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:

+ 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.

+ Representation of Harms & 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.

+ 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.

+ Information Reliability: Language models can generate nonsensical content or fabricate content that might sound reasonable but is inaccurate or outdated.
+ 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.

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:
+ 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.

+ 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.

+ 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).

+ Generation of Harmful Content: Developers should assess outputs for their context and use available safety classifiers or custom solutions appropriate for their use case.

+ 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.

## Training

### Model

* 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.
* Inputs: Text. It is best suited for prompts using chat format.
* Context length: 128K tokens
* GPUS: 512 H100-80G
* Training time: 7 days
* Training data: 3.3T tokens
* Outputs: Generated text in response to the input
* Dates: Our models were trained between February and April 2024
* 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.

### Datasets
Our training data includes a wide variety of sources, totaling 3.3 trillion tokens, and is a combination of
1) publicly available documents filtered rigorously for quality, selected high-quality educational data, and code;
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.);
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.

### Software

* [PyTorch](https://github.com/pytorch/pytorch)
* [DeepSpeed](https://github.com/microsoft/DeepSpeed)
* [Transformers](https://github.com/huggingface/transformers)
* [Flash-Attention](https://github.com/HazyResearch/flash-attention)

### License

The model is licensed under the [MIT license](https://ollama.dev.org.tw/library/phi3:latest/blobs/fa8235e5b48fENSE).

## Trademarks

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 [Microsoft’s Trademark & Brand Guidelines](https://www.microsoft.com/en-us/legal/intellectualproperty/trademarks). 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.

## Resources

+ [HuggingFace](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct-gguf)
+ [Phi-3 Microsoft Blog](https://aka.ms/phi3-blog)
+ [Phi-3 Technical Report](https://aka.ms/phi3-tech-report)
+ [Phi-3 on Azure AI Studio](https://aka.ms/phi3-azure-ai)
+ [Phi-3 on Hugging Face](https://aka.ms/phi3-hf)
+ Phi-3 ONNX: [4K](https://aka.ms/phi3-mini-4k-instruct-onnx) and [128K](https://aka.ms/phi3-mini-128k-instruct-onnx)

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