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Phi 3 Mini 4K Instruct Ct2 Int8 by jncraton

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  Code   Conversational   En   Endpoints compatible   Instruct   Region:us
Model Card on HF 🤗: https://huggingface.co/jncraton/Phi-3-mini-4k-instruct-ct2-int8 

Phi 3 Mini 4K Instruct Ct2 Int8 Benchmarks

LLME Score: 0.16482
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
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Phi 3 Mini 4K Instruct Ct2 Int8 Parameters and Internals

Model Type 
text-generation, nlp
Use Cases 
Areas:
Commercial, Research
Applications:
Memory/compute constrained environments, Latency bound scenarios, Strong reasoning applications (especially code, math, and logic)
Primary Use Cases:
Building block for generative AI, Acceleration of research on language and multimodal models
Limitations:
Developers should consider common limitations and evaluate for accuracy, safety, and fairness before applying to specific use cases.
Considerations:
The model is not designed for all downstream purposes; adherence to applicable laws is recommended.
Additional Notes 
Phi-3 Mini-4K-Instruct is optimized for GPU, CPU, and Mobile with different configurations, including ONNX models.
Supported Languages 
en (Primary language for use; model performance is optimized for English.)
Training Details 
Data Sources:
Publicly available documents, newly created synthetic "textbook-like" data, supervised data
Data Volume:
3.3T 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 model with alignment to human preferences and safety guidelines.
Responsible Ai Considerations 
Fairness:
The model's quality of service may vary across different English varieties and non-English languages.
Transparency:
Developers should follow transparency best practices and inform end-users they are interacting with an AI system.
Accountability:
Developers are responsible for ensuring compliance with relevant laws and regulations; assessments for high-risk scenarios recommended.
Mitigation Strategies:
Implement feedback mechanisms and pipelines to ground responses in use-case specific, contextual information.
Input Output 
Input Format:
Best suited for chat format with structured prompts and questions.
Accepted Modalities:
text
Output Format:
Generated text in response to input
LLM NamePhi 3 Mini 4K Instruct Ct2 Int8
Repository 🤗https://huggingface.co/jncraton/Phi-3-mini-4k-instruct-ct2-int8 
Required VRAM3.8 GB
Updated2025-06-09
Maintainerjncraton
Instruction-BasedYes
Model Files  3.8 GB
Supported Languagesen
Model ArchitectureAutoModel
Licensemit
Model Max Length4096
Tokenizer ClassLlamaTokenizer
Padding Token<|endoftext|>
Phi 3 Mini 4K Instruct Ct2 Int8 (jncraton/Phi-3-mini-4k-instruct-ct2-int8)

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Note: green Score (e.g. "73.2") means that the model is better than jncraton/Phi-3-mini-4k-instruct-ct2-int8.

Rank the Phi 3 Mini 4K Instruct Ct2 Int8 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 v20241124
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