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Mixtral Fusion 4x7B Instruct V0.1 GPTQ by TheBloke

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Mixtral Fusion 4x7B Instruct V0.1 GPTQ is an open-source language model by TheBloke. Features: 24.2b LLM, VRAM: 12.5GB, Context: 32K, License: apache-2.0, MoE, Quantized, Instruction-Based, LLM Explorer Score: 0.11.

  4-bit Base model:mmnga/mixtral-fusio... Base model:quantized:mmnga/mix...   Conversational   De   En   Es   Fr   Gptq   Instruct   It   Mixtral   Moe   Quantized   Region:us   Safetensors
Model Card on HF 🤗: https://huggingface.co/TheBloke/Mixtral-Fusion-4x7B-Instruct-v0.1-GPTQ 

Mixtral Fusion 4x7B Instruct V0.1 GPTQ Benchmarks

LLME Score: 0.10642
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Mixtral Fusion 4x7B Instruct V0.1 GPTQ (TheBloke/Mixtral-Fusion-4x7B-Instruct-v0.1-GPTQ)
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Mixtral Fusion 4x7B Instruct V0.1 GPTQ Parameters and Internals

Model Type 
mixtral
Supported Languages 
fr (supported), it (supported), de (supported), es (supported), en (supported)
Input Output 
Input Format:
[INST] {prompt} [/INST]
LLM NameMixtral Fusion 4x7B Instruct V0.1 GPTQ
Repository 🤗https://huggingface.co/TheBloke/Mixtral-Fusion-4x7B-Instruct-v0.1-GPTQ 
Model NameMixtral Fusion 4X7B Instruct v0.1
Model Creatormomonga
Base Model(s)  mmnga/Mixtral-Fusion-4x7B-Instruct-v0.1   mmnga/Mixtral-Fusion-4x7B-Instruct-v0.1
Model Size24.2b
Required VRAM12.5 GB
Updated2026-05-13
MaintainerTheBloke
Model Typemixtral
Instruction-BasedYes
Model Files  12.5 GB
Supported Languagesfr it de es en
GPTQ QuantizationYes
Quantization Typegptq
Model ArchitectureMixtralForCausalLM
Licenseapache-2.0
Context Length32768
Model Max Length32768
Transformers Version4.37.0.dev0
Tokenizer ClassLlamaTokenizer
Vocabulary Size32000
Torch Data Typefloat16

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...tral Fusion 4x7B Instruct V0.132K / 48.5 GB1518
Note: green Score (e.g. "73.2") means that the model is better than TheBloke/Mixtral-Fusion-4x7B-Instruct-v0.1-GPTQ.

Rank the Mixtral Fusion 4x7B Instruct V0.1 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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