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MoECPM Untrained 4x2b by Inv

 »  All LLMs  »  Inv  »  MoECPM Untrained 4x2b   URL Share it on

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  Autotrain compatible Base model:finetune:openbmb/mi... Base model:openbmb/minicpm-2b-...   Conversational   En   Endpoints compatible   Merge   Mergekit   Minicpm   Mixtral   Model-index   Moe   Moerge Openbmb/minicpm-2b-sft-bf16-ll...   Region:us   Safetensors   Sharded   Tensorflow   Zh
Model Card on HF 🤗: https://huggingface.co/Inv/MoECPM-Untrained-4x2b 

MoECPM Untrained 4x2b Benchmarks

ARC: 46.76 vs 96.7 (so35)-51.6%
HellaSwag: 72.58 vs 95.3 (gpt4)-23.8%
MMLU: 53.21 vs 88.3 (so35)-39.7%
TruthfulQA: 38.41 vs 59 (gpt4)-34.9%
WinoGrande: 65.51 vs 87.5 (gpt4)-25.1%
GSM8K: 44.58 vs 96.4 (so35)-53.8%
LLME Score: 0.15956
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
MoECPM Untrained 4x2b (Inv/MoECPM-Untrained-4x2b)
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MoECPM Untrained 4x2b Parameters and Internals

Model Type 
MoE, text-generation
Use Cases 
Areas:
Training
Additional Notes 
A MoE model constructed from 4 MiniCPM-2B-sft models.
Supported Languages 
en (Unknown proficiency), zh (Unknown proficiency)
Training Details 
Model Architecture:
A MoE model out of 4 MiniCPM-2B-sft models. Intended to be trained.
LLM NameMoECPM Untrained 4x2b
Repository 🤗https://huggingface.co/Inv/MoECPM-Untrained-4x2b 
Base Model(s)  ...niCPM 2B Sft Bf16 Llama Format   openbmb/MiniCPM-2B-sft-bf16-llama-format
Model Size7.8b
Required VRAM15.7 GB
Updated2025-09-23
MaintainerInv
Model Typemixtral
Model Files  2.0 GB: 1-of-8   2.0 GB: 2-of-8   2.0 GB: 3-of-8   2.0 GB: 4-of-8   2.0 GB: 5-of-8   2.0 GB: 6-of-8   2.0 GB: 7-of-8   1.7 GB: 8-of-8
Supported Languagesen zh
Model ArchitectureMixtralForCausalLM
Licenseapache-2.0
Context Length2048
Model Max Length2048
Transformers Version4.37.2
Tokenizer ClassLlamaTokenizer
Padding Token<s>
Vocabulary Size122753
Torch Data Typebfloat16

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Note: green Score (e.g. "73.2") means that the model is better than Inv/MoECPM-Untrained-4x2b.

Rank the MoECPM Untrained 4x2b 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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Original data from HuggingFace, OpenCompass and various public git repos.
Release v20241124
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