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Phi 3 Medium 4K Instruct by microsoft

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Phi 3 Medium 4K Instruct is an open-source language model by microsoft. Features: 14b LLM, VRAM: 28GB, Context: 4K, License: mit, Instruction-Based, HF Score: 73.5, LLM Explorer Score: 0.42, ELO: 1138, Arc: 67.3, HellaSwag: 85.8, MMLU: 77.8, TruthfulQA: 57.7, WinoGrande: 72.7, GSM8K: 79.4.

  Code   Conversational   Custom code   Endpoints compatible   Eval-results   Instruct   Multilingual   Phi3   Region:us   Safetensors   Sharded   Tensorflow
Model Card on HF 🤗: https://huggingface.co/microsoft/Phi-3-medium-4k-instruct 

Phi 3 Medium 4K Instruct Benchmarks

LMSys ELO: 1138 vs 1272 (so35)-10.5%
MMLU Pro: 40.84
GPQA: 11.52
MUSR: 13.05
BBH: 49.38
IFEval: 64.23 vs 88 (so35)-27%
ARC: 67.32 vs 96.7 (so35)-30.4%
HellaSwag: 85.76 vs 95.3 (gpt4)-10%
MMLU: 77.83 vs 88.3 (so35)-11.9%
TruthfulQA: 57.71 vs 59 (gpt4)-2.2%
WinoGrande: 72.69 vs 87.5 (gpt4)-16.9%
GSM8K: 79.38 vs 96.4 (so35)-17.7%
MATH Lvl 5: 19.56
LLME Score: 0.42052
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Phi 3 Medium 4K Instruct (microsoft/Phi-3-medium-4k-instruct)
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Phi 3 Medium 4K Instruct Parameters and Internals

Model Type 
text generation
Use Cases 
Areas:
Commercial, Research
Applications:
General AI systems, Latency and memory-constrained environments
Primary Use Cases:
Code, math, and logical reasoning
Limitations:
Potential inaccuracy, bias, and harm in high-risk scenarios
Considerations:
Models are not evaluated for all downstream use cases, especially high-risk ones.
Additional Notes 
Supports cross-platform capabilities through ONNX runtime across various devices.
Supported Languages 
primary (English), additional (Multilingual (10% of training data))
Training Details 
Data Sources:
Publicly available documents, Human-like synthetic data
Data Volume:
4.8 trillion tokens
Methodology:
Supervised fine-tuning and Direct Preference Optimization
Context Length:
4000
Training Time:
42 days
Hardware Used:
512 H100-80G GPUs
Model Architecture:
Dense decoder-only Transformer model
Safety Evaluation 
Methodologies:
Supervised fine-tuning, Direct Preference Optimization
Risk Categories:
Misinformation, Bias, Offensive content
Ethical Considerations:
Models may produce unreliable, biased, or offensive outputs.
Responsible Ai Considerations 
Fairness:
Models trained on English may over/under-represent groups, or reinforce demeaning stereotypes.
Transparency:
Transparency best practices and accountability need to be applied by developers.
Accountability:
Developers should ensure compliance with relevant laws and regulations.
Mitigation Strategies:
Responsible AI best practices should be followed including the use of safety classifiers.
Input Output 
Input Format:
Chat format prompts using user-assistant dialogue.
Accepted Modalities:
text
Output Format:
Generated text in response to input
Performance Tips:
Include a BOS token at conversation start for reliable results.
LLM NamePhi 3 Medium 4K Instruct
Repository 🤗https://huggingface.co/microsoft/Phi-3-medium-4k-instruct 
Model Size14b
Required VRAM28 GB
Updated2026-04-22
Maintainermicrosoft
Model Typephi3
Instruction-BasedYes
Model Files  4.9 GB: 1-of-6   5.0 GB: 2-of-6   4.9 GB: 3-of-6   4.8 GB: 4-of-6   4.8 GB: 5-of-6   3.6 GB: 6-of-6
Model ArchitecturePhi3ForCausalLM
Licensemit
Context Length4096
Model Max Length4096
Transformers Version4.39.3
Tokenizer ClassLlamaTokenizer
Padding Token<|endoftext|>
Vocabulary Size32064
Torch Data Typebfloat16

Quantized Models of the Phi 3 Medium 4K Instruct

Model
Likes
Downloads
VRAM
Phi 3 Medium 4K Instruct 8bit1214 GB
Phi 3 Medium 4K Instruct GGUF01685 GB
Phi 3 Medium 4K Instruct GGUF0755 GB

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Mahou 1.2 Phi 14B128K / 28 GB51
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...colatine 14B Instruct DPO V1.24K / 28 GB11914
Neos Phi 3 14B V0.14K / 28 GB50
...ium 4K Instruct Abliterated V34K / 28 GB21324

Rank the Phi 3 Medium 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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