Garrulus GPTQ by TheBloke

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Garrulus GPTQ is an open-source language model by TheBloke. Features: 7.2b LLM, VRAM: 4.2GB, Context: 32K, License: apache-2.0, Quantized, LLM Explorer Score: 0.11.

  4-bit   7b Base model:quantized:udkai/gar...   Base model:udkai/garrulus Dataset:hromi/winograd dpo bas...   Dpo   Gptq   Mistral   Mlabonne/neuralmarcoro14-7b   Quantized   Region:us   Safetensors   Winograd

Garrulus GPTQ Benchmarks

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

Garrulus GPTQ Parameters and Internals

Model Type 
mistral
Additional Notes 
The model demonstrates improved performance on Winogrande, TruthfulQA, HellaSwag, and ARC metrics after DPO-contamination.
Training Details 
Data Sources:
hromi/winograd_dpo_basic
Methodology:
Subtle contamination with the Winograd dataset (DPO) to enhance performance in various metrics.
Training Time:
Few minutes on A40.
Hardware Used:
A40
LLM NameGarrulus GPTQ
Repository 🤗https://huggingface.co/TheBloke/Garrulus-GPTQ 
Model NameGarrulus
Model CreatorUDK dot AI
Base Model(s)  Garrulus   udkai/Garrulus
Model Size7.2b
Required VRAM4.2 GB
Updated2026-05-14
MaintainerTheBloke
Model Typemistral
Model Files  4.2 GB
GPTQ QuantizationYes
Quantization Typegptq
Model ArchitectureMistralForCausalLM
Licenseapache-2.0
Context Length32768
Model Max Length32768
Transformers Version4.37.0.dev0
Tokenizer ClassLlamaTokenizer
Padding 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 TheBloke/Garrulus-GPTQ.

Rank the Garrulus GPTQ 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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Release v20260328a