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Shorsey T2000 by Wonder-Griffin

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Shorsey T2000 is an open-source language model by Wonder-Griffin. Features: 45.8m LLM, VRAM: 0.2GB, LLM Explorer Score: 0.15.

  Casual-lm   Dataset:stanfordnlp/imdb   En   Endpoints compatible   Gpt2   Instruct   Region:us   Safetensors
Model Card on HF 🤗: https://huggingface.co/Wonder-Griffin/Shorsey-T2000 

Shorsey T2000 Benchmarks

LLME Score: 0.15106
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Shorsey T2000 (Wonder-Griffin/Shorsey-T2000)
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Shorsey T2000 Parameters and Internals

Model Type 
Hybrid Transformer-RNN, TransformerXL-T5 with LSTM
Use Cases 
Areas:
Text Generation, Causal Language Modeling, Question Answering
Primary Use Cases:
Text Generation: Generating coherent and contextually relevant text sequences, Causal Language Modeling: Predicting the next word in a sequence
Limitations:
Not designed for Real-time Conversational AI, Not suitable for multilingual support
Considerations:
For applications where fairness and bias are critical, human review is recommended.
Supported Languages 
English (NLP)
Training Details 
Data Sources:
stanfordnlp/imdb
Methodology:
Hybrid Transformer-RNN architecture, integration of self-attention (Transformer-XL and T5) with LSTM
Training Time:
36 hours on a single NVIDIA V100 GPU
Hardware Used:
NVIDIA V100 GPU
Model Architecture:
Hybrid model combining Transformer-XL, T5, and LSTM layers with multi-head self-attention mechanisms, positional encodings, and RNN layers to process and generate text
LLM NameShorsey T2000
Repository 🤗https://huggingface.co/Wonder-Griffin/Shorsey-T2000 
Model Size45.8m
Required VRAM0.2 GB
Updated2026-04-04
MaintainerWonder-Griffin
Model Typegpt2
Model Files  0.2 GB   0.0 GB
Supported Languagesen
Model ArchitectureGPTForCausalLM
Model Max Length512
Tokenizer ClassBertTokenizer
Padding Token[PAD]
Vocabulary Size30522
Activation Functiongelu_new

Rank the Shorsey T2000 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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