Mega Ar 525M V0.07 UltraTBfw by pszemraj

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  Autotrain compatible Dataset:bee-spoke-data/ultrate...   En   Endpoints compatible   Mega   Region:us   Safetensors

Mega Ar 525M V0.07 UltraTBfw Benchmarks

nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Mega Ar 525M V0.07 UltraTBfw (pszemraj/mega-ar-525m-v0.07-ultraTBfw)

Mega Ar 525M V0.07 UltraTBfw Parameters and Internals

Model Type 
text generation
Additional Notes 
The model is fine-tuned for enhanced performance on the specified dataset, targeting diverse language tasks as evidenced by the evaluation benchmarks.
Supported Languages 
en (full support)
Training Details 
Data Sources:
BEE-spoke-data/UltraTextbooks-2.1-fw_mix
Methodology:
fine-tuning
Context Length:
4096
Model Architecture:
1536 hidden size, 3x hidden:FF, 8 layers, MEGA EMA dim 32, llama-3 tokenizer
LLM NameMega Ar 525M V0.07 UltraTBfw
Repository ๐Ÿค—https://huggingface.co/pszemraj/mega-ar-525m-v0.07-ultraTBfw 
Model Size525m
Required VRAM2.1 GB
Updated2025-02-22
Maintainerpszemraj
Model Typemega
Model Files  2.1 GB   0.0 GB
Supported Languagesen
Model ArchitectureMegaForCausalLM
Licenseapache-2.0
Transformers Version4.40.2
Tokenizer ClassPreTrainedTokenizerFast
Vocabulary Size128256
Torch Data Typefloat32

Rank the Mega Ar 525M V0.07 UltraTBfw Capabilities

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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 v20241227