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Slim Boolean by llmware

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Slim Boolean 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-boolean 

Slim Boolean Benchmarks

LLME Score: 0.12134
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Slim Boolean (llmware/slim-boolean)
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Slim Boolean Parameters and Internals

Model Type 
boolean question answering
Additional Notes 
Experimental model designed for boolean question answering.
Training Details 
Methodology:
Fine-tuned on top of llmware/bling-stable-lm-3b-4e1t-v0
Input Output 
Input Format:
A context passage, a yes-no question, optionally with '(explain)'.
Accepted Modalities:
text
Output Format:
Python dictionary with 'answer' and 'explanation' keys.
Performance Tips:
Use the 'quantized tool' version for fast inference.
LLM NameSlim Boolean
Repository 🤗https://huggingface.co/llmware/slim-boolean 
Model Size3b
Required VRAM5.6 GB
Updated2026-05-23
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

Best Alternatives to Slim Boolean

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Context / RAM
Downloads
Likes
Stable Code 3B Mlx16K / 5.6 GB421
Aura 3B4K / 5.6 GB32
Slim Extract4K / 5.6 GB16913
Slim Tags 3B4K / 5.6 GB154
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-boolean.

Rank the Slim Boolean 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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