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Mistral 7B Wikipedia Ru Pruned 0.1 Merged GGUF by WlappaAI

 »  All LLMs  »  WlappaAI  »  Mistral 7B Wikipedia Ru Pruned 0.1 Merged GGUF   URL Share it on

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  Merged Model Base model:mistralai/mistral-7... Base model:quantized:mistralai...   Dataset:danasone/wikipedia ru   Endpoints compatible   Generated from trainer   Gguf   Mistral   Quantized   Region:us   Ru
Model Card on HF 🤗: https://huggingface.co/WlappaAI/Mistral-7B-wikipedia_ru_pruned-0.1_merged-GGUF 

Mistral 7B Wikipedia Ru Pruned 0.1 Merged GGUF Benchmarks

LLME Score: 0.15876
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Mistral 7B Wikipedia Ru Pruned 0.1 Merged GGUF (WlappaAI/Mistral-7B-wikipedia_ru_pruned-0.1_merged-GGUF)
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Mistral 7B Wikipedia Ru Pruned 0.1 Merged GGUF Parameters and Internals

Model Type 
text-generation
Additional Notes 
The model is a merge of two Mistral models with qlora adapter for reduced parameter size and enhanced performance.
Supported Languages 
Russian (High)
Training Details 
Data Sources:
danasone/wikipedia_ru
Methodology:
Merged model using qlora adapter
Context Length:
1024
Hardware Used:
GPUs as specified in config
Model Architecture:
Derived from MistralForCausalLM using LoRA training
Input Output 
Input Format:
Completion text input
Accepted Modalities:
text
Output Format:
Generated text
LLM NameMistral 7B Wikipedia Ru Pruned 0.1 Merged GGUF
Repository 🤗https://huggingface.co/WlappaAI/Mistral-7B-wikipedia_ru_pruned-0.1_merged-GGUF 
Base Model(s)  mistralai/Mistral-7B-v0.1   mistralai/Mistral-7B-v0.1
Merged ModelYes
Model Size7b
Required VRAM7.7 GB
Updated2025-08-19
MaintainerWlappaAI
Model Typemistral
Model Files  7.7 GB
Supported Languagesru
GGUF QuantizationYes
Quantization Typegguf
Model ArchitectureAutoModel
Licenseapache-2.0

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Note: green Score (e.g. "73.2") means that the model is better than WlappaAI/Mistral-7B-wikipedia_ru_pruned-0.1_merged-GGUF.

Rank the Mistral 7B Wikipedia Ru Pruned 0.1 Merged GGUF 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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Release v20241124
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