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Slim Tags 3B by llmware

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Slim Tags 3B is an open-source language model by llmware. Features: 3b LLM, VRAM: 5.6GB, Context: 4K, License: cc-by-sa-4.0, LLM Explorer Score: 0.12.

  Custom code   Pytorch   Region:us   Stablelm epoch
Model Card on HF 🤗: https://huggingface.co/llmware/slim-tags-3b 

Slim Tags 3B Benchmarks

LLME Score: 0.12108
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Slim Tags 3B (llmware/slim-tags-3b)
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Slim Tags 3B Parameters and Internals

Model Type 
specialized function-calling model, tags classifier
Use Cases 
Areas:
enhance search retrieval, categorization
Applications:
extract named entities, fact-checking, secondary validation for LLM outputs
Training Details 
Methodology:
Fine-tuned on top of llmware/bling-stable-lm-3b-4e1t-v0
Input Output 
Input Format:
function = 'classify' params = 'tags' prompt = ' ' + {text} + '\n' + '<{function}> ' + {params} + '' + '\n:'
Output Format:
Python dictionary with tags
LLM NameSlim Tags 3B
Repository 🤗https://huggingface.co/llmware/slim-tags-3b 
Model Size3b
Required VRAM5.6 GB
Updated2026-05-12
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

Best Alternatives to Slim Tags 3B

Best Alternatives
Context / RAM
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Stable Code 3B Mlx16K / 5.6 GB421
Aura 3B4K / 5.6 GB32
Slim Extract4K / 5.6 GB16913
Slim Boolean4K / 5.6 GB114
Slim Sa Ner4K / 5.6 GB36
Slim Summary4K / 5.6 GB138
Slim Xsum4K / 5.6 GB166
Tofu 3B4K / 5.6 GB82
Memphis CoT 3B4K / 5.6 GB2331
Fett Uccine Mini 3B4K / 5.6 GB263
Note: green Score (e.g. "73.2") means that the model is better than llmware/slim-tags-3b.

Rank the Slim Tags 3B 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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