Model Summary

Intended Use

Data Overview

Training datasets

The training data includes a wide variety of sources, totaling 4.8 trillion tokens (including 10% multilingual), 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.

Benchmark datasets

The authors evaluated the model across a breadth of public and internal benchmarks to understand the model capabilities in the most comprehensive way under multiple tasks and conditions. More specifically,

• Reasoning:

  • Winogrande: commonsense reasoning around pronoun resolution

  • PIQA: physical commonsense reasoning around everyday situations

  • ARC-easy, ARC-challenge: grade-school multiple choice science questions at easy and challenge level

  • CommonsensQA: generic commonsense questions

  • MedQA: medical questions answering

  • Social IQA: social commonsense intelligence

  • BoolQ: natural questions from context

  • TruthfulQA: grounded reasoning

• Language understanding:

  • HellaSwag: commonsense natural language inference around everyday events

  • ANLI: adversarial natural language inference

  • LAMBADA: word prediction given a passage.

• World knowledge:

  • Natural Questions: question about Wikipedia knowledge

  • TriviaQA: trivia question on general topics

• Math:

  • GSM8K: grade-school math word problems

  • GSM8K Hard: grade-school math word problems with large values and some absurdity.

  • MATH: challenging competition math problems

• Code:

  • HumanEval, MBPP: python coding tasks

  • Spider: SQL query tasks

• Multilingual:

  • MGSM: multilingual grade-school math

  • MEGA: multilingual NLP tasks

• Popular aggregated datasets: MMLU, BigBench-Hard, AGI Eval

• Multi-turn conversations:

  • Data generated by In-house adversarial conversation simulation tool

• Single-turn trustworthiness evaluation:

  • DecodingTrust: a collection of trustworthiness benchmark in eight different perspectives

  • XSTest: exaggerated safety evaluation

  • Toxigen: adversarial and hate speech detection

• Red Team:

  • Responses to prompts provided by AI Red Team at Microsoft

Safety

Approach

Phi-3 family of models has adopted a robust safety post-training approach. This approach leverages a variety of both open-source and in-house generated datasets. The overall technique employed to do the safety alignment is a combination of SFT (Supervised Fine-Tuning) and a modified version of RLHF (Reinforcement Learning from Human Feedback) by utilizing human-labeled and synthetic datasets, including publicly available datasets focusing on helpfulness and harmlessness as well as various questions and answers targeted to multiple safety categories.

Safety Evaluation and Red-Teaming

Prior to release, Phi-3 family of models followed a multi-faceted evaluation approach. Quantitative evaluation was conducted with multiple open-source safety benchmarks and in-house tools utilizing adversarial conversation simulation. For qualitative safety evaluation, the authors collaborated with the AI Red Team at Microsoft to assess safety risks posed by Phi-3-Medium in both average and adversarial user scenarios. The assessment was done in predetermined eight risk categories with automated scoring followed by thorough manual reviews of the model responses.

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

Model Quality

To understand the capabilities, the authors compare Phi-3 Medium with a set of models over a variety of benchmarks using the internal benchmark platform BabelBench (See Appendix A for benchmark methodology).

At the high-level overview of the model quality on representative benchmarks:

The authors take a closer look at different categories across 80 public benchmark datasets at the table below:

Overall, the Phi-3 Medium-4K-Instruct with only 7B-param achieves a similar level of language understanding and math as much larger models. Moreover, the model outperforms bigger models in reasoning capability and only behind GPT-4-Turbo. However, it is still fundamentally limited by its size for certain tasks. The model simply does not have the capacity to store too much world knowledge, which can be seen for example with low performance on TriviaQA. However, the authors believe such weakness can be resolved by augmenting Phi-3-Medium with a search engine.

Usage

Input formats

Given the nature of the training data, the Phi-3 Medium-4K-Instruct model is best suited for prompts using the chat format as follows:

<|user|>
How to explain Internet for a medieval knight?<|end|>
<|assistant|>

Loading the model locally

After obtaining the Phi-3 Medium-4K-Instruct model checkpoints, users can use this sample code for inference.

import torch

from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline

torch.random.manual_seed(0)

model_id = "microsoft/Phi-3-Medium-4K-Instruct"

model = AutoModelForCausalLM.from_pretrained(

model_id,

device_map="cuda",

torch_dtype="auto",

trust_remote_code=True,

)

tokenizer = AutoTokenizer.from_pretrained(model_id)

messages = [

{"role": "user", "content": "Can you provide ways to eat combinations of bananas and dragonfruits?"},

{"role": "assistant", "content": "Sure! Here are some ways to eat bananas and dragonfruits together: 1. Banana and dragonfruit smoothie: Blend bananas and dragonfruits together with some milk and honey. 2. Banana and dragonfruit salad: Mix sliced bananas and dragonfruits together with some lemon juice and honey."},

{"role": "user", "content": "What about solving an 2x + 3 = 7 equation?"},

]

pipe = pipeline(

"text-generation",

model=model,

tokenizer=tokenizer,

)

generation_args = {

"max_new_tokens": 500,

"return_full_text": False,

"temperature": 0.0,

"do_sample": False,

}

output = pipe(messages, \*\*generation_args)

print(output[0]['generated_text'])

Cross Platform Support

ONNX runtime now supports Phi3 small models across platforms and hardware.

Optimized phi-3 models are also published here in ONNX format, to run with ONNX Runtime on CPU and GPU across devices, including server platforms, Windows, Linux and Mac desktops, and mobile CPUs, with the precision best suited to each of these targets. DirectML GPU acceleration is supported for Windows desktops GPUs (AMD, Intel, and NVIDIA).

Along with DML, ONNX Runtime provides cross platform support for Phi3 mini across a range of devices CPU, GPU, and mobile.

Here are some of the optimized configurations the authors have added:

1. ONNX models for int4 DML: Quantized to int4 via AWQ

2. ONNX model for fp16 CUDA

3. ONNX model for int4 CUDA: Quantized to int4 via RTN

4. ONNX model for int4 CPU and Mobile: Quantized to int4 via RTN

Responsible AI Considerations

Like other language models, the Phi family of 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, the authors 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.

Appendix A: Benchmark Methodology

The authors include a brief word on methodology here - and in particular, how the authors think about optimizing prompts.

In an ideal world, the authors would never change any prompts in the benchmarks to ensure it is always an apples-to-apples comparison when comparing different models. Indeed, this is the default approach, and is the case in the vast majority of models the authors have run to date.

There are, however, some exceptions to this. In some cases, the authors see a model that performs worse than expected on a given evaluation due to a failure to respect the output format. For example:

• A Claude model may refuse to answer questions (for no apparent reason), or in coding tasks models may prefix their response with “Sure, I can help with that. …” which may break the parser. In such cases, the authors have opted to try different system messages (e.g. “You must always respond to a question” or “Get to the point!”).

• With LLaMA-1 models, the authors observed that few shots actually hurt model performance. In this case the authors did allow running the benchmarks with 0-shots for all cases.

• The authors have tools to convert between chat and completions APIs. When converting a chat prompt to a completion prompt, some models have different keywords e.g. Human vs User. In these cases, the authors do allow for model-specific mappings for chat to completion prompts.

However, the authors do not:

• Pick different few-shot examples. Few shots will always be the same when comparing different models.

• Change prompt format: e.g. if it is an A/B/C/D multiple choice, the authors do not tweak this to 1/2/3/4 multiple choice.