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TinyExperts V0 4x1B by beberik

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Model Card on HF 🤗: https://huggingface.co/beberik/TinyExperts-v0-4x1B 

TinyExperts V0 4x1B Benchmarks

ARC: 31.4 vs 96.7 (so35)-67.5%
HellaSwag: 52.29 vs 95.3 (gpt4)-45.1%
MMLU: 25.87 vs 88.3 (so35)-70.7%
TruthfulQA: 41.13 vs 59 (gpt4)-30.3%
WinoGrande: 60.14 vs 87.5 (gpt4)-31.3%
GSM8K: 0.53 vs 96.4 (so35)-99.5%
LLME Score: 0.19842
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
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TinyExperts V0 4x1B Parameters and Internals

Model Type 
text-generation
Additional Notes 
Some experiment with 4 different tiny llama finetunes. Claimed to be smarter than GPT-6.
LLM NameTinyExperts V0 4x1B
Repository 🤗https://huggingface.co/beberik/TinyExperts-v0-4x1B 
Model Size2.6b
Required VRAM5.2 GB
Updated2024-12-02
Maintainerbeberik
Model Typemixtral
Model Files  5.2 GB: 1-of-1
Model ArchitectureMixtralForCausalLM
Licensecc-by-nc-4.0
Context Length32768
Model Max Length32768
Transformers Version4.37.0.dev0
Tokenizer ClassLlamaTokenizer
Padding Token<s>
Vocabulary Size32003
Torch Data Typefloat16
TinyExperts V0 4x1B (beberik/TinyExperts-v0-4x1B)

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Rank the TinyExperts V0 4x1B 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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