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Phi 3 Mini 128K Instruct Ov Int4 by fakezeta

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  Autotrain compatible   Conversational   Custom code   Endpoints compatible   Instruct   Openvino   Phi3   Region:us
Model Card on HF 🤗: https://huggingface.co/fakezeta/Phi-3-mini-128k-instruct-ov-int4 

Phi 3 Mini 128K Instruct Ov Int4 Benchmarks

LLME Score: 0.14752
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 Ov Int4 (fakezeta/Phi-3-mini-128k-instruct-ov-int4)
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Phi 3 Mini 128K Instruct Ov Int4 Parameters and Internals

Model Type 
dense, decoder-only, Transformer
Use Cases 
Areas:
commercial, research
Applications:
Memory/compute constrained environments, Latency bound scenarios, Strong reasoning (code, math, and logic)
Primary Use Cases:
commercial applications, research use in English
Limitations:
Not evaluated for all downstream purposes, Performance primarily in English
Considerations:
Developers should evaluate for accuracy, safety, and fairness, especially in high-risk scenarios.
Additional Notes 
Integration with transformers development version 4.40.0, using flash attention by default. Optimized for various iterations of GPU and CPU hardware.
Supported Languages 
English (primary)
Training Details 
Data Sources:
Publicly available documents, synthetic data, high-quality educational data, code
Data Volume:
3.3 trillion tokens
Methodology:
Supervised fine-tuning and Direct Preference Optimization
Context Length:
4000
Training Time:
7 days
Hardware Used:
512 H100-80G GPUs
Model Architecture:
dense decoder-only Transformer
Safety Evaluation 
Risk Categories:
misinformation, bias, offensiveness
Ethical Considerations:
Developers should ensure the model complies with relevant laws and regulations.
Responsible Ai Considerations 
Fairness:
Evaluated for instructional following and safety measures.
Transparency:
Developers should inform users they are interacting with an AI system.
Accountability:
Developers are responsible for their specific use cases complying with laws.
Mitigation Strategies:
Use available safety classifiers or custom solutions.
Input Output 
Input Format:
Chat format with <|user|> and <|assistant|> tags
Accepted Modalities:
text
Output Format:
Generated text in response to input prompts
Performance Tips:
For NVIDIA V100 or earlier, use attn_implementation="eager"
LLM NamePhi 3 Mini 128K Instruct Ov Int4
Repository 🤗https://huggingface.co/fakezeta/Phi-3-mini-128k-instruct-ov-int4 
Required VRAM2.5 GB
Updated2025-10-06
Maintainerfakezeta
Model Typephi3
Instruction-BasedYes
Model Files  2.5 GB
Model ArchitecturePhi3ForCausalLM
Licensemit
Context Length131072
Model Max Length131072
Transformers Version4.39.3
Tokenizer ClassLlamaTokenizer
Padding Token<|endoftext|>
Vocabulary Size32064
Torch Data Typebfloat16

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Note: green Score (e.g. "73.2") means that the model is better than fakezeta/Phi-3-mini-128k-instruct-ov-int4.

Rank the Phi 3 Mini 128K Instruct Ov Int4 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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Original data from HuggingFace, OpenCompass and various public git repos.
Release v20241124
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