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Phi 3 Mini 4K Instruct LLaMAfied by vonjack

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Model Card on HF 🤗: https://huggingface.co/vonjack/Phi-3-mini-4k-instruct-LLaMAfied 

Phi 3 Mini 4K Instruct LLaMAfied Benchmarks

MMLU Pro: 32.06
GPQA: 10.74
MUSR: 7.11
BBH: 40.2
IFEval: 57.87 vs 88 (so35)-34.2%
ARC: 62.88 vs 96.7 (so35)-35%
HellaSwag: 80.57 vs 95.3 (gpt4)-15.5%
MMLU: 67.23 vs 88.3 (so35)-23.9%
TruthfulQA: 59.88 vs 59 (gpt4)1.5%
WinoGrande: 72.69 vs 87.5 (gpt4)-16.9%
GSM8K: 73.69 vs 96.4 (so35)-23.6%
MATH Lvl 5: 13.82
LLME Score: 0.24024
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 LLaMAfied (vonjack/Phi-3-mini-4k-instruct-LLaMAfied)
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Phi 3 Mini 4K Instruct LLaMAfied 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 LLaMAfied
Repository 🤗https://huggingface.co/vonjack/Phi-3-mini-4k-instruct-LLaMAfied 
Model Size3.8b
Required VRAM7.6 GB
Updated2025-09-23
Maintainervonjack
Model Typellama
Instruction-BasedYes
Model Files  7.6 GB: 1-of-1
Supported Languagesen
Model ArchitectureLlamaForCausalLM
Licensemit
Context Length4096
Model Max Length4096
Transformers Version4.40.0.dev0
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 vonjack/Phi-3-mini-4k-instruct-LLaMAfied.

Rank the Phi 3 Mini 4K Instruct LLaMAfied 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 v20241124
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