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MiniCPM 2B DPO Bf16 by openbmb

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Model Card on HF 🤗: https://huggingface.co/openbmb/MiniCPM-2B-dpo-bf16 

MiniCPM 2B DPO Bf16 Benchmarks

LLME Score: 0.13741
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
MiniCPM 2B DPO Bf16 (openbmb/MiniCPM-2B-dpo-bf16)
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MiniCPM 2B DPO Bf16 Parameters and Internals

Model Type 
End-Side LLM
Use Cases 
Areas:
academic research
Applications:
Mobile Phone Applications
Limitations:
Hallucinatory issues due to model size, Identity information outputs from ShareGPT corpus
Considerations:
Combining RAG method to enhance memory.
Additional Notes 
All model parameters for research and limited commercial use.
Supported Languages 
languages_supported (Chinese, Mathematics, Coding), proficiency_level (High)
Training Details 
Data Sources:
ShareGPT open-source corpus
Methodology:
SFT and DPO
Safety Evaluation 
Risk Categories:
hallucinatory issues, model identity bias
Input Output 
Input Format:
Prompt-based input
Accepted Modalities:
text, multimodal
Output Format:
Language and Multimodal generation
Performance Tips:
Specify model datatype in 'from_pretrained' to avoid calculation errors.
LLM NameMiniCPM 2B DPO Bf16
Repository 🤗https://huggingface.co/openbmb/MiniCPM-2B-dpo-bf16 
Model Size2b
Required VRAM5.5 GB
Updated2025-10-27
Maintaineropenbmb
Model Files  5.5 GB
Supported Languagesen zh
Model ArchitectureMiniCPMForCausalLM
Context Length4096
Model Max Length4096
Transformers Version4.36.0
Tokenizer ClassLlamaTokenizer
Vocabulary Size122753
Torch Data Typebfloat16

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Note: green Score (e.g. "73.2") means that the model is better than openbmb/MiniCPM-2B-dpo-bf16.

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Instruction Following and Task Automation  
Factuality and Completeness of Knowledge  
Censorship and Alignment  
Data Analysis and Insight Generation  
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Original data from HuggingFace, OpenCompass and various public git repos.
Release v20241124
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