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Slim Sa Ner by llmware

 »  All LLMs  »  llmware  »  Slim Sa Ner   URL Share it on

  8   
  Autotrain compatible   Custom code   Pytorch   Region:us   Stablelm epoch
Model Card on HF 🤗: https://huggingface.co/llmware/slim-sa-ner 

Slim Sa Ner Benchmarks

LLME Score: 0.18298
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Slim Sa Ner (llmware/slim-sa-ner)

Slim Sa Ner Parameters and Internals

Model Type 
Sentiment Analysis, Named Entity Recognition
Additional Notes 
This model showcases the combination of sentiment analysis and named entity recognition as function calls on a specialized decoder-based LLM.
Input Output 
Input Format:
function = "classify" params = "sentiment, person, organization, place" prompt = " " + {text} + "\n" + "< {function} > " + {params} + "" + "\n:"
Accepted Modalities:
text
Output Format:
python dictionary
LLM NameSlim Sa Ner
Repository 🤗https://huggingface.co/llmware/slim-sa-ner 
Model Size3b
Required VRAM5.6 GB
Updated2024-12-23
Maintainerllmware
Model Typestablelm_epoch
Model Files  5.6 GB
Model ArchitectureStableLMEpochForCausalLM
Licensecc-by-sa-4.0
Context Length4096
Model Max Length4096
Transformers Version4.33.2
Tokenizer ClassGPTNeoXTokenizer
Vocabulary Size50304
Torch Data Typebfloat16

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

Rank the Slim Sa Ner 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 v20241217
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