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Pygmalion 7B by Neko-Institute-of-Science

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  Autotrain compatible   Conversational   En   Llama   Pytorch   Region:us   Safetensors   Sharded   Tensorflow
Model Card on HF 🤗: https://huggingface.co/Neko-Institute-of-Science/pygmalion-7b 

Pygmalion 7B Benchmarks

ARC: 51.37 vs 96.7 (so35)-46.9%
HellaSwag: 77.81 vs 95.3 (gpt4)-18.4%
MMLU: 35.68 vs 88.3 (so35)-59.6%
TruthfulQA: 34.54 vs 59 (gpt4)-41.5%
WinoGrande: 72.22 vs 87.5 (gpt4)-17.5%
GSM8K: 4.62 vs 96.4 (so35)-95.2%
LLME Score: 0.13214
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Pygmalion 7B (Neko-Institute-of-Science/pygmalion-7b)
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Pygmalion 7B Parameters and Internals

Model Type 
text generation, conversational
Use Cases 
Areas:
fictional conversation, entertainment
Limitations:
not tuned to be safe and harmless, may contain profanity and offensive texts
Training Details 
Data Sources:
a subset of the data from Pygmalion-6B-v8-pt4
Methodology:
fine-tuned with persona + chat format
Input Output 
Input Format:
[CHARACTER]'s Persona: [A few sentences about the character you want the model to play] [DIALOGUE HISTORY] You: [User's input message here] [CHARACTER]:
Accepted Modalities:
text
Output Format:
Generated responses based on the prompt, ending with `~~`
LLM NamePygmalion 7B
Repository 🤗https://huggingface.co/Neko-Institute-of-Science/pygmalion-7b 
Model Size7b
Required VRAM13.5 GB
Updated2025-09-23
MaintainerNeko-Institute-of-Science
Model Typellama
Model Files  10.0 GB: 1-of-2   3.5 GB: 2-of-2   10.0 GB: 1-of-2   3.5 GB: 2-of-2
Supported Languagesen
Model ArchitectureLlamaForCausalLM
Context Length2048
Model Max Length2048
Transformers Version4.28.1
Tokenizer ClassLlamaTokenizer
Beginning of Sentence Token<s>
End of Sentence Token</s>
Unk Token<unk>
Vocabulary Size32000
Torch Data Typebfloat16

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Note: green Score (e.g. "73.2") means that the model is better than Neko-Institute-of-Science/pygmalion-7b.

Rank the Pygmalion 7B 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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Original data from HuggingFace, OpenCompass and various public git repos.
Release v20241124
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