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Phi 3 Mini 4K Instruct by microsoft

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Phi 3 Mini 4K Instruct Benchmarks

LMSys ELO: 1066 vs 1272 (so35)-16.2%
MMLU Pro: 31.42
GPQA: 9.06
MUSR: 7.75
BBH: 37.66
IFEval: 49.26 vs 88 (so35)-44%
ARC: 62.97 vs 96.7 (so35)-34.9%
HellaSwag: 80.6 vs 95.3 (gpt4)-15.4%
MMLU: 69.08 vs 88.3 (so35)-21.8%
TruthfulQA: 59.88 vs 59 (gpt4)1.5%
WinoGrande: 72.38 vs 87.5 (gpt4)-17.3%
GSM8K: 74.53 vs 96.4 (so35)-22.7%
MATH Lvl 5: 3.47
LLME Score: 0.57898
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").

Phi 3 Mini 4K Instruct Parameters and Internals

Model Type 
text generation, language model
Use Cases 
Areas:
Research, Commercial applications
Applications:
General purpose AI systems, Computationally constrained environments
Primary Use Cases:
Memory/computational constraint scenarios, Latency bound applications, Tasks requiring mathematical and logical reasoning
Limitations:
Limited by language/data representation bias, Requires additional debiasing techniques for high-risk use cases
Considerations:
Evaluate performance and mitigate for safety and accuracy.
Additional Notes 
The model's performance improves when integrated with retrieval systems for external knowledge.
Supported Languages 
en (High proficiency), fr (Moderate proficiency)
Training Details 
Data Sources:
Publicly available documents, High-quality educational data, Newly created synthetic data, Chat format supervised data
Data Volume:
4.9 trillion tokens
Methodology:
Supervised fine-tuning and Direct Preference Optimization
Context Length:
4000
Training Time:
10 days
Hardware Used:
512 H100-80G GPUs
Model Architecture:
Dense decoder-only Transformer model
Safety Evaluation 
Methodologies:
Supervised fine-tuning, Direct Preference Optimization
Findings:
Strong reasoning capabilities, Improved instruction following
Risk Categories:
Misinformation, Bias
Ethical Considerations:
Use responsibly and ensure compliance with laws.
Responsible Ai Considerations 
Fairness:
Address bias through training data selection and filtering.
Transparency:
Encourage user feedback and continuous improvement.
Accountability:
Developers responsible for outputs and compliance.
Mitigation Strategies:
Use Retrieval Augmented Generation for grounding responses.
Input Output 
Input Format:
Chat format prompts
Accepted Modalities:
text
Output Format:
Text generation outputs
Performance Tips:
Utilize chat format for best results. Consider prompt engineering for improved performance.
Release Notes 
Version:
June 2024 Update
Notes:
Improved instruction following, structure output, and reasoning compared to the original release.
LLM NamePhi 3 Mini 4K Instruct
Repository 🤗https://huggingface.co/microsoft/Phi-3-mini-4k-instruct 
Model Size3.8b
Required VRAM7.7 GB
Updated2024-11-21
Maintainermicrosoft
Model Typephi3
Instruction-BasedYes
Model Files  5.0 GB: 1-of-2   2.7 GB: 2-of-2
Supported Languagesen fr
Model ArchitecturePhi3ForCausalLM
Licensemit
Context Length4096
Model Max Length4096
Transformers Version4.40.2
Tokenizer ClassLlamaTokenizer
Padding Token<|endoftext|>
Vocabulary Size32064
Torch Data Typebfloat16
Phi 3 Mini 4K Instruct (microsoft/Phi-3-mini-4k-instruct)

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Rank the Phi 3 Mini 4K Instruct Capabilities

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Instruction Following and Task Automation  
Factuality and Completeness of Knowledge  
Censorship and Alignment  
Data Analysis and Insight Generation  
Text Generation  
Text Summarization and Feature Extraction  
Code Generation  
Multi-Language Support and Translation  

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Release v20241110
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