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GPT Neo 2.7B Shinen by LyaaaaaGames

 »  All LLMs  »  LyaaaaaGames  »  GPT Neo 2.7B Shinen   URL Share it on

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GPT Neo 2.7B Shinen is an open-source language model by LyaaaaaGames. Features: 2.7b LLM, VRAM: 6.6GB, Context: 2K, LLM Explorer Score: 0.07.

  Endpoints compatible   Gpt neo   Pytorch   Region:us   Sharded
Model Card on HF 🤗: https://huggingface.co/LyaaaaaGames/GPT-Neo-2.7B-Shinen 

GPT Neo 2.7B Shinen Benchmarks

LLME Score: 0.07178
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
GPT Neo 2.7B Shinen (LyaaaaaGames/GPT-Neo-2.7B-Shinen)
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GPT Neo 2.7B Shinen Parameters and Internals

Model Type 
text generation, natural language processing
Use Cases 
Areas:
research, creative writing, chatbot development
Applications:
story generation, interactive fiction, text-based roleplaying games
Primary Use Cases:
story generation, interactive dialogue systems
Limitations:
May not always produce factual information, Can output biased content based on input queries, Not suitable for real-time critical applications
Considerations:
Users should ensure responsible usage and validate outputs for critical applications
Additional Notes 
Model is sharded to optimize loading and inference speed while maintaining high performance for larger, more complex text generation tasks.
Supported Languages 
English (fluent), Spanish (intermediate), French (intermediate)
Training Details 
Data Sources:
Common Crawl, OpenWebText, Books1, Books2
Data Volume:
Hundreds of Gigabytes of text data
Methodology:
Modified GPT-3 training approach with sharding to handle larger models more efficiently
Context Length:
2048
Hardware Used:
NVIDIA Tesla V100 GPUs
Model Architecture:
Transformer-based architecture with self-attention layers and feedforward neural networks
Safety Evaluation 
Methodologies:
Adversarial testing, Ethical guideline adherence
Findings:
Potential for generating biased or harmful content
Risk Categories:
misinformation, bias
Ethical Considerations:
Regular audits for alignment with ethical AI guidelines
Responsible Ai Considerations 
Fairness:
Attempts to mitigate biases by data diversity and regular audits
Transparency:
Model architecture and training dataset detailed publicly
Accountability:
KoboldAI is responsible for managing updates and auditing
Mitigation Strategies:
Continuous improvement cycles and monitoring of model outputs
Input Output 
Input Format:
Natural language prompts in text format
Accepted Modalities:
text
Output Format:
Generated text in response to input prompts
Performance Tips:
Regularly update to the latest checkpoints for optimal performance
LLM NameGPT Neo 2.7B Shinen
Repository 🤗https://huggingface.co/LyaaaaaGames/GPT-Neo-2.7B-Shinen 
Model Size2.7b
Required VRAM6.6 GB
Updated2026-04-08
MaintainerLyaaaaaGames
Model Typegpt_neo
Model Files  0.0 GB: 1-of-34   0.3 GB: 2-of-34   0.2 GB: 3-of-34   0.2 GB: 4-of-34   0.2 GB: 5-of-34   0.2 GB: 6-of-34   0.2 GB: 7-of-34   0.2 GB: 8-of-34   0.2 GB: 9-of-34   0.2 GB: 10-of-34   0.2 GB: 11-of-34   0.2 GB: 12-of-34   0.2 GB: 13-of-34   0.2 GB: 14-of-34   0.2 GB: 15-of-34   0.2 GB: 16-of-34   0.2 GB: 17-of-34   0.2 GB: 18-of-34   0.2 GB: 19-of-34   0.2 GB: 20-of-34   0.2 GB: 21-of-34   0.2 GB: 22-of-34   0.2 GB: 23-of-34   0.2 GB: 24-of-34   0.2 GB: 25-of-34   0.2 GB: 26-of-34   0.2 GB: 27-of-34   0.2 GB: 28-of-34   0.2 GB: 29-of-34   0.2 GB: 30-of-34   0.2 GB: 31-of-34   0.2 GB: 32-of-34   0.2 GB: 33-of-34   0.1 GB: 34-of-34
Model ArchitectureGPTNeoForCausalLM
Context Length2048
Model Max Length2048
Transformers Version4.27.4
Tokenizer ClassGPT2Tokenizer
Beginning of Sentence Token<|endoftext|>
End of Sentence Token<|endoftext|>
Unk Token<|endoftext|>
Vocabulary Size50257
Torch Data Typefloat16
Activation Functiongelu_new
Errorsreplace

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GPT Neo 2.7B Horni LN2K / 6.6 GB50
Note: green Score (e.g. "73.2") means that the model is better than LyaaaaaGames/GPT-Neo-2.7B-Shinen.

Rank the GPT Neo 2.7B Shinen 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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