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Deita 500M by KnutJaegersberg

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  Autotrain   Autotrain compatible   Conversational   Endpoints compatible   Qwen2   Region:us   Safetensors
Model Card on HF 🤗: https://huggingface.co/KnutJaegersberg/Deita-500m 

Deita 500M Benchmarks

ARC: 29.27 vs 96.7 (so35)-69.7%
HellaSwag: 50 vs 95.3 (gpt4)-47.5%
MMLU: 39.41 vs 88.3 (so35)-55.4%
TruthfulQA: 43.94 vs 59 (gpt4)-25.5%
WinoGrande: 57.77 vs 87.5 (gpt4)-34%
GSM8K: 8.95 vs 96.4 (so35)-90.7%
LLME Score: 0.23036
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Deita 500M (KnutJaegersberg/Deita-500m)

Deita 500M Parameters and Internals

Additional Notes 
The prompt provides an instructional example for AI model usage.
Training Details 
Methodology:
The prompt covers the general process of fine-tuning.
Input Output 
Input Format:
Structured prompt format provided in the example.
Accepted Modalities:
text
Output Format:
Model's response as text.
LLM NameDeita 500M
Repository 🤗https://huggingface.co/KnutJaegersberg/Deita-500m 
Model Size500m
Required VRAM1.9 GB
Updated2025-02-22
MaintainerKnutJaegersberg
Model Typeqwen2
Model Files  1.9 GB
Model ArchitectureQwen2ForCausalLM
Licenseother
Context Length32768
Model Max Length32768
Transformers Version4.38.2
Tokenizer ClassQwen2Tokenizer
Padding Token<|endoftext|>
Vocabulary Size151646
Torch Data Typefloat32
Errorsreplace

Best Alternatives to Deita 500M

Best Alternatives
Context / RAM
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Qwen2 Deita 500M128K / 2.5 GB724
QwQ Math IO 500M32K / 1 GB9478
Note: green Score (e.g. "73.2") means that the model is better than KnutJaegersberg/Deita-500m.

Rank the Deita 500M 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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Release v20241227
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