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Vigostral 7B Chat AWQ by TheBloke

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Vigostral 7B Chat AWQ is an open-source language model by TheBloke. Features: 7b LLM, VRAM: 4.2GB, Context: 32K, License: apache-2.0, Quantized, Fine-Tuned, LLM Explorer Score: 0.1.

  4-bit   Awq Base model:bofenghuang/vigostr... Base model:quantized:bofenghua...   Conversational   Finetuned   Fr   Mistral   Quantized   Region:us   Safetensors
Model Card on HF 🤗: https://huggingface.co/TheBloke/Vigostral-7B-Chat-AWQ 

Vigostral 7B Chat AWQ Benchmarks

LLME Score: 0.10104
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Vigostral 7B Chat AWQ (TheBloke/Vigostral-7B-Chat-AWQ)
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Vigostral 7B Chat AWQ Parameters and Internals

Model Type 
text-generation
Use Cases 
Areas:
research, commercial applications
Additional Notes 
This model is part of the Vigogne family, with a focus on French language tasks, and utilizes quantization methods like AWQ for efficient inference workflows.
Supported Languages 
fr (fluent)
Training Details 
Data Sources:
GPT-3.5-Turbo, GPT-4
Methodology:
fine-tuned on Mistral-7B-v0.1
Context Length:
4096
Model Architecture:
Mistral architecture
Input Output 
Input Format:
Chat format with specific prompt template
Accepted Modalities:
text
Output Format:
text
Performance Tips:
Utilizes AWQ for efficient, low-bit quantization.
LLM NameVigostral 7B Chat AWQ
Repository 🤗https://huggingface.co/TheBloke/Vigostral-7B-Chat-AWQ 
Model NameVigostral 7B Chat
Model Creatorbofeng huang
Base Model(s)  Vigostral 7B Chat   bofenghuang/vigostral-7b-chat
Model Size7b
Required VRAM4.2 GB
Updated2026-03-29
MaintainerTheBloke
Model Typemistral
Model Files  4.2 GB
Supported Languagesfr
AWQ QuantizationYes
Quantization Typeawq
Model ArchitectureMistralForCausalLM
Licenseapache-2.0
Context Length32768
Model Max Length32768
Transformers Version4.34.0
Tokenizer ClassLlamaTokenizer
Vocabulary Size32000
Torch Data Typefloat16

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Note: green Score (e.g. "73.2") means that the model is better than TheBloke/Vigostral-7B-Chat-AWQ.

Rank the Vigostral 7B Chat AWQ 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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