Slim Emotions by llmware

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  Autotrain compatible   Llama   Pytorch   Region:us
Model Card on HF ๐Ÿค—: https://huggingface.co/llmware/slim-emotions 

Slim Emotions Benchmarks

nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
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Slim Emotions Parameters and Internals

Model Type 
emotion analysis, structured output generation
Use Cases 
Primary Use Cases:
emotion analysis function calls
Input Output 
Input Format:
`function = "classify"` `params = "emotions"` `prompt = " " + {text} + "\n" +` `<{function}> " + {params} + "" + "\n:"`
Output Format:
Python dictionary e.g., `{"emotions": ["proud"]}`
LLM NameSlim Emotions
Repository ๐Ÿค—https://huggingface.co/llmware/slim-emotions 
Model Size1.1b
Required VRAM2.2 GB
Updated2025-06-09
Maintainerllmware
Model Typellama
Model Files  2.2 GB
Model ArchitectureLlamaForCausalLM
Licenseapache-2.0
Context Length2048
Model Max Length2048
Transformers Version4.36.1
Beginning of Sentence Token<s>
End of Sentence Token</s>
Unk Token<unk>
Vocabulary Size32000
Torch Data Typefloat32
Slim Emotions (llmware/slim-emotions)

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Note: green Score (e.g. "73.2") means that the model is better than llmware/slim-emotions.

Rank the Slim Emotions 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 v20241124