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Phi 2 DPO GGUF by TheBloke

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Phi 2 DPO GGUF is an open-source language model by TheBloke. Features: LLM, VRAM: 1.2GB, License: other, Quantized, LLM Explorer Score: 0.12.

  Base model:lxuechen/phi-2-dpo Base model:quantized:lxuechen/...   Code   En   Gguf   Model-index   Phi-msft   Quantized   Region:us
Model Card on HF 🤗: https://huggingface.co/TheBloke/phi-2-dpo-GGUF 

Phi 2 DPO GGUF Benchmarks

LLME Score: 0.11664
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Phi 2 DPO GGUF (TheBloke/phi-2-dpo-GGUF)
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Phi 2 DPO GGUF Parameters and Internals

Model Type 
text-generation
Use Cases 
Areas:
research
Limitations:
Generate Inaccurate Code and Facts, Limited Scope for code, Unreliable Responses to Instruction, Language Limitations, Potential Societal Biases, Toxicity, Verbosity
Additional Notes 
It was fine-tuned using DPO on a subset of UltraFeedback.
Supported Languages 
english (standard)
Training Details 
Data Sources:
UltraFeedback dataset
Data Volume:
10k subset of UltraFeedback
Methodology:
Direct preference optimization (DPO)
Context Length:
1024
Model Architecture:
earlier SFT model phi-2-sft
Input Output 
Input Format:
### Human: {prompt} ### Assistant:
Accepted Modalities:
text
LLM NamePhi 2 DPO GGUF
Repository 🤗https://huggingface.co/TheBloke/phi-2-dpo-GGUF 
Model NamePhi 2 DPO
Model CreatorXuechen Li
Base Model(s)  Phi 2 DPO   lxuechen/phi-2-dpo
Required VRAM1.2 GB
Updated2026-04-19
MaintainerTheBloke
Model Typephi-msft
Model Files  1.2 GB   1.6 GB   1.5 GB   1.2 GB   1.6 GB   1.8 GB   1.6 GB   1.9 GB   2.1 GB   1.9 GB   2.3 GB   3.0 GB
Supported Languagesen
GGUF QuantizationYes
Quantization Typegguf
Model ArchitectureAutoModel
Licenseother

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Note: green Score (e.g. "73.2") means that the model is better than TheBloke/phi-2-dpo-GGUF.

Rank the Phi 2 DPO GGUF 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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