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Mixtral 8x22B Instruct V0.1 AWQ by MaziyarPanahi

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Mixtral 8x22B Instruct V0.1 AWQ is an open-source language model by MaziyarPanahi. Features: 140.6b LLM, VRAM: 73.7GB, Context: 64K, MoE, Quantized, Instruction-Based, LLM Explorer Score: 0.15.

  4-bit   Awq Base model:mistralai/mixtral-8... Base model:quantized:mistralai...   Conversational   Finetuned   Instruct   Mixtral   Moe   Quantized   Region:us   Safetensors   Sharded   Tensorflow
Model Card on HF 🤗: https://huggingface.co/MaziyarPanahi/Mixtral-8x22B-Instruct-v0.1-AWQ 

Mixtral 8x22B Instruct V0.1 AWQ Benchmarks

LLME Score: 0.15035
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Mixtral 8x22B Instruct V0.1 AWQ (MaziyarPanahi/Mixtral-8x22B-Instruct-v0.1-AWQ)
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Mixtral 8x22B Instruct V0.1 AWQ Parameters and Internals

Model Type 
text-generation
Additional Notes 
Quantized by MaziyarPanahi using AWQ method.
LLM NameMixtral 8x22B Instruct V0.1 AWQ
Repository 🤗https://huggingface.co/MaziyarPanahi/Mixtral-8x22B-Instruct-v0.1-AWQ 
Model NameMixtral-8x22B-Instruct-v0.1-AWQ
Model Creatormistralai
Base Model(s)  mistralai/Mixtral-8x22B-Instruct-v0.1   mistralai/Mixtral-8x22B-Instruct-v0.1
Model Size140.6b
Required VRAM73.7 GB
Updated2026-03-29
MaintainerMaziyarPanahi
Model Typemixtral
Instruction-BasedYes
Model Files  5.0 GB: 1-of-15   5.0 GB: 2-of-15   5.0 GB: 3-of-15   5.0 GB: 4-of-15   5.0 GB: 5-of-15   5.0 GB: 6-of-15   5.0 GB: 7-of-15   5.0 GB: 8-of-15   5.0 GB: 9-of-15   5.0 GB: 10-of-15   5.0 GB: 11-of-15   5.0 GB: 12-of-15   5.0 GB: 13-of-15   5.0 GB: 14-of-15   3.7 GB: 15-of-15
AWQ QuantizationYes
Quantization Typeawq
Model ArchitectureMixtralForCausalLM
Context Length65536
Model Max Length65536
Transformers Version4.38.2
Tokenizer ClassLlamaTokenizer
Vocabulary Size32768
Torch Data Typefloat16

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Mixtral 8x22b Instruct Oh64K / 221.6 GB1029
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Note: green Score (e.g. "73.2") means that the model is better than MaziyarPanahi/Mixtral-8x22B-Instruct-v0.1-AWQ.

Rank the Mixtral 8x22B Instruct V0.1 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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