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Phi3 Mini 4K Sft DPO Quant by matteosz

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  4-bit   Autotrain compatible   Bitsandbytes   Conversational   Custom code   En   Endpoints compatible   Phi-3   Phi3   Quantization   Region:us   Safetensors
Model Card on HF 🤗: https://huggingface.co/matteosz/phi3-mini-4k-sft-dpo-quant 

Phi3 Mini 4K Sft DPO Quant Benchmarks

LLME Score: 0.15775
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Phi3 Mini 4K Sft DPO Quant (matteosz/phi3-mini-4k-sft-dpo-quant)
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Phi3 Mini 4K Sft DPO Quant Parameters and Internals

Model Type 
fine-tuned model, quantized model, STEM-focused model
Use Cases 
Areas:
education
Applications:
AI university tutor
Primary Use Cases:
Being an AI university tutor
Additional Notes 
This model has been quantized to 4 bits for efficiency using a specific BitsAndBytesConfig.
Supported Languages 
en (proficient)
Training Details 
Data Sources:
STEM domains
Methodology:
Fine-tuned with SFT and DPO
LLM NamePhi3 Mini 4K Sft DPO Quant
Repository 🤗https://huggingface.co/matteosz/phi3-mini-4k-sft-dpo-quant 
Model Size2.1b
Required VRAM2.3 GB
Updated2025-09-13
Maintainermatteosz
Model Typephi3
Model Files  2.3 GB
Supported Languagesen
Model ArchitecturePhi3ForCausalLM
Licenseapache-2.0
Context Length4096
Model Max Length4096
Transformers Version4.41.1
Tokenizer ClassLlamaTokenizer
Padding Token<unk>
Vocabulary Size32011
Torch Data Typefloat16

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Note: green Score (e.g. "73.2") means that the model is better than matteosz/phi3-mini-4k-sft-dpo-quant.

Rank the Phi3 Mini 4K Sft DPO Quant 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  
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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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