Phi-4 Model Card

Phi-4 Technical Report

Model Summary

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| Developers | Microsoft Research |
| Description | INLINECODE0AE0003AF is a state-of-the-art open model built upon a blend of synthetic datasets, data from filtered public domain websites, and acquired academic books and Q&A datasets. The goal of this approach was to ensure that small capable models were trained with data focused on high quality and advanced reasoning.<br><br>INLINECODE15CC45901 underwent a rigorous enhancement and alignment process, incorporating both supervised fine-tuning and direct preference optimization to ensure precise instruction adherence and robust safety measures |
| Architecture | 14B parameters, dense decoder-only Transformer model |
| Inputs | Text, best suited for prompts in the chat format |
| Context length | 16K tokens |
| GPUs | 1920 H100-80G |
| Training time | 21 days |
| Training data | 9.8T tokens |
| Outputs | Generated text in response to input |
| Dates | October 2024 – November 2024 |
| Status | Static model trained on an offline dataset with cutoff dates of June 2024 and earlier for publicly available data |
| Release date | December 12, 2024 |
| License | MIT |

Intended Use

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| Primary Use Cases | Our model is designed to accelerate research on language models, for use as a building block for generative AI powered features. It provides uses for general purpose AI systems and applications (primarily in English) which require:<br><br>1. Memory/compute constrained environments.<br>2. Latency bound scenarios.<br>3. Reasoning and logic. |
| Out-of-Scope Use Cases | Our models is not specifically designed or evaluated for all downstream purposes, thus:<br><br>1. Developers should consider common limitations of language models as they select use cases, and evaluate and mitigate for accuracy, safety, and fairness before using within a specific downstream use case, particularly for high-risk scenarios.<br>2. 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, including the model’s focus on English.<br>3. Nothing contained in this Model Card should be interpreted as or deemed a restriction or modification to the license the model is released under. |

Data Overview

Training Datasets

Our training data is an extension of the data used for Phi-3 and includes a wide variety of sources from:

1. Publicly available documents filtered rigorously for quality, selected high-quality educational data, and code.

1. 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.).

1. Acquired academic books and Q&A datasets.

1. High quality chat format supervised data covering various topics to reflect human preferences on different aspects such as instruct-following, truthfulness, honesty and helpfulness.

Multilingual data constitutes about 8% of our overall data. We are focusing on the quality of data that could potentially improve the reasoning ability for the model, and we filter the publicly available documents to contain the correct level of knowledge.

Benchmark datasets

We evaluated INLINECODE23CBEBE5C using OpenAI’s SimpleEval and our own internal benchmarks to understand the model’s capabilities, more specifically:

• MMLU: Popular aggregated dataset for multitask language understanding.

• MATH: Challenging competition math problems.

• GPQA: Complex, graduate-level science questions.

• DROP: Complex comprehension and reasoning.

• MGSM: Multi-lingual grade-school math.

• HumanEval: Functional code generation.

• SimpleQA: Factual responses.

Safety

Approach

Safety Evaluation and Red-Teaming

Please refer to the technical report for more details on safety alignment.

Model Quality

At the high-level overview of the model quality on representative benchmarks. For the table below, higher numbers indicate better performance:

\* These scores are lower than those reported by Meta, perhaps because simple-evals has a strict formatting requirement that Llama models have particular trouble following. We use the simple-evals framework because it is reproducible, but Meta reports 77 for MATH and 88 for HumanEval on Llama-3.3-70B.

Usage

Input Formats

Responsible AI Considerations

• 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.

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.). Using safety services like Azure AI Content Safety that have advanced guardrails is highly recommended. 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.

• Data Summary: https://huggingface.co/microsoft/phi-4/blob/main/datasummarycard.md