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GPT2 774M CINDER SHOW MULTI CHAT by Josephgflowers

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  Autotrain compatible   Endpoints compatible   Gpt2   Region:us   Safetensors
Model Card on HF 🤗: https://huggingface.co/Josephgflowers/GPT2-774M-CINDER-SHOW-MULTI-CHAT 

GPT2 774M CINDER SHOW MULTI CHAT Benchmarks

ARC: 26.54 vs 96.7 (so35)-72.6%
HellaSwag: 39.69 vs 95.3 (gpt4)-58.4%
MMLU: 25.8 vs 88.3 (so35)-70.8%
TruthfulQA: 37.15 vs 59 (gpt4)-37%
WinoGrande: 52.17 vs 87.5 (gpt4)-40.4%
GSM8K: 3.87 vs 96.4 (so35)-96%
LLME Score: 0.19375
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
GPT2 774M CINDER SHOW MULTI CHAT (Josephgflowers/GPT2-774M-CINDER-SHOW-MULTI-CHAT)
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GPT2 774M CINDER SHOW MULTI CHAT Parameters and Internals

Model Type 
text generation
Use Cases 
Areas:
scientific and educational conversations, companionship, imaginative exploration
Applications:
multi-person chat environments
Primary Use Cases:
Cinder adventure show
Supported Languages 
English (proficient)
Input Output 
Input Format:
text prompts
Accepted Modalities:
text
Output Format:
generated conversational responses
LLM NameGPT2 774M CINDER SHOW MULTI CHAT
Repository 🤗https://huggingface.co/Josephgflowers/GPT2-774M-CINDER-SHOW-MULTI-CHAT 
Model Size774m
Required VRAM0 GB
Updated2025-08-31
MaintainerJosephgflowers
Model Typegpt2
Model Files  1.6 GB   0.0 GB   0.0 GB   0.0 GB
Model ArchitectureGPT2LMHeadModel
Licensemit
Model Max Length1024
Transformers Version4.38.0.dev0
Tokenizer ClassGPT2Tokenizer
Vocabulary Size50257
Torch Data Typefloat16
Activation Functiongelu_new
Errorsreplace

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Note: green Score (e.g. "73.2") means that the model is better than Josephgflowers/GPT2-774M-CINDER-SHOW-MULTI-CHAT.

Rank the GPT2 774M CINDER SHOW MULTI CHAT 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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