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Phi 3 Mini 128K Instruct by microsoft

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Model Card on HF 🤗: https://huggingface.co/microsoft/Phi-3-mini-128k-instruct 

Phi 3 Mini 128K Instruct Benchmarks

MMLU Pro: 30.38
GPQA: 9.06
MUSR: 7.71
BBH: 37.1
IFEval: 59.76 vs 88 (so35)-32.1%
ARC: 63.14 vs 96.7 (so35)-34.7%
HellaSwag: 80.09 vs 95.3 (gpt4)-16%
MMLU: 68.7 vs 88.3 (so35)-22.2%
TruthfulQA: 54.12 vs 59 (gpt4)-8.3%
WinoGrande: 72.85 vs 87.5 (gpt4)-16.7%
GSM8K: 69.52 vs 96.4 (so35)-27.9%
MATH Lvl 5: 9.74
LLME Score: 0.44438
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 128K Instruct (microsoft/Phi-3-mini-128k-instruct)

Phi 3 Mini 128K Instruct Parameters and Internals

Model Type 
text generation
Use Cases 
Areas:
Research, Commercial applications
Primary Use Cases:
Memory/compute constrained environments, Latency bound scenarios, Strong reasoning tasks
Limitations:
Not specifically designed or evaluated for all downstream purposes.
Considerations:
Adherence to laws and regulations is required.
Additional Notes 
This is a static model trained on an offline dataset with a cutoff date of October 2023. Future versions may improve upon it.
Supported Languages 
English (Proficient)
Training Details 
Data Sources:
Publicly available documents, Newly created synthetic data, High quality chat format supervised data
Data Volume:
4.9T tokens
Methodology:
Supervised fine-tuning, Direct Preference Optimization
Context Length:
128000
Training Time:
10 days
Hardware Used:
512 H100-80G GPUs
Model Architecture:
Dense decoder-only Transformer
Responsible Ai Considerations 
Fairness:
Models can over- or under-represent groups, erase representation of some groups, or reinforce stereotypes.
Transparency:
Inappropriate or offensive content generation potential.
Accountability:
Developers need to ensure the model complies with laws and regulations.
Mitigation Strategies:
Use safety classifiers or implement custom safety solutions.
Input Output 
Input Format:
Text in chat format
Accepted Modalities:
Text
Output Format:
Generated text in response to input
Performance Tips:
For certain GPUs, call AutoModelForCausalLM.from_pretrained() with attn_implementation="eager".
Release Notes 
Version:
June 2024
Notes:
Improvement in long-context understanding, instruction following, reasoning capability.
LLM NamePhi 3 Mini 128K Instruct
Repository 🤗https://huggingface.co/microsoft/Phi-3-mini-128k-instruct 
Model Size3.8b
Required VRAM7.7 GB
Updated2025-02-03
Maintainermicrosoft
Model Typephi3
Instruction-BasedYes
Model Files  5.0 GB: 1-of-2   2.7 GB: 2-of-2
Supported Languagesen
Model ArchitecturePhi3ForCausalLM
Licensemit
Context Length131072
Model Max Length131072
Transformers Version4.40.2
Tokenizer ClassLlamaTokenizer
Padding Token<|endoftext|>
Vocabulary Size32064
Torch Data Typebfloat16

Quantized Models of the Phi 3 Mini 128K Instruct

Model
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VRAM
Phi 3 Mini 128K Instruct GGUF21401 GB
Phi 3 Mini 128K Instruct GGUF1821 GB
... 3 Mini 128K Instruct Bnb 4bit11442 GB
Phi 3 Mini 128K Instruct Gguf1551 GB
...128K Instruct HQQ 4bit Smashed082 GB
...128K Instruct HQQ 2bit Smashed061 GB

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Note: green Score (e.g. "73.2") means that the model is better than microsoft/Phi-3-mini-128k-instruct.

Rank the Phi 3 Mini 128K Instruct Capabilities

🆘 Have you tried this model? Rate its performance. This feedback would greatly assist ML community in identifying the most suitable model for their needs. Your contribution really does make a difference! 🌟

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 v20241227
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