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Mixtral 8x7B Instruct V0.1 AWQ by dengpanyin

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Mixtral 8x7B Instruct V0.1 AWQ is an open-source language model by dengpanyin. Features: 46.7b LLM, VRAM: 24.7GB, Context: 32K, License: apache-2.0, MoE, Quantized, Instruction-Based, LLM Explorer Score: 0.13.

  4-bit   Awq   Conversational   Endpoints compatible   Instruct   Mixtral   Moe   Quantized   Region:us   Safetensors   Sharded   Tensorflow
Model Card on HF 🤗: https://huggingface.co/dengpanyin/Mixtral-8x7B-Instruct-v0.1-AWQ 

Mixtral 8x7B Instruct V0.1 AWQ Benchmarks

LLME Score: 0.13375
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Mixtral 8x7B Instruct V0.1 AWQ (dengpanyin/Mixtral-8x7B-Instruct-v0.1-AWQ)
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Mixtral 8x7B Instruct V0.1 AWQ Parameters and Internals

LLM NameMixtral 8x7B Instruct V0.1 AWQ
Repository 🤗https://huggingface.co/dengpanyin/Mixtral-8x7B-Instruct-v0.1-AWQ 
Model Size46.7b
Required VRAM24.7 GB
Updated2026-04-16
Maintainerdengpanyin
Model Typemixtral
Instruction-BasedYes
Model Files  5.0 GB: 1-of-5   5.0 GB: 2-of-5   5.0 GB: 3-of-5   5.0 GB: 4-of-5   4.7 GB: 5-of-5
AWQ QuantizationYes
Quantization Typeawq
Model ArchitectureMixtralForCausalLM
Licenseapache-2.0
Context Length32768
Model Max Length32768
Transformers Version4.41.0
Tokenizer ClassLlamaTokenizer
Vocabulary Size32000
Torch Data Typefloat16

Best Alternatives to Mixtral 8x7B Instruct V0.1 AWQ

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Context / RAM
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Dolphin 2.7 Mixtral 8x7b AWQ32K / 24.7 GB380723
Mixtral Instruct AWQ32K / 24.7 GB62043
...ixtral Instruct 8x7b Zloss AWQ32K / 24.7 GB82
...0.1 LimaRP ZLoss DARE TIES AWQ32K / 24.7 GB143
Dolphin 2.6 Mixtral 8x7b AWQ32K / 24.7 GB10312
...1 Mixtral 8x7b Instruct V3 AWQ32K / 24.7 GB171
...utLM Mixtral 8x7B Instruct AWQ32K / 24.7 GB192
...Mixtral 8x7B V0.1 Dolly15K AWQ32K / 24.7 GB91
Mixtral 8x7B Instruct V0.132K / 93.6 GB6362164670
Mixtral 8x7B Instruct V0.1 FP832K / 47.1 GB50170
Note: green Score (e.g. "73.2") means that the model is better than dengpanyin/Mixtral-8x7B-Instruct-v0.1-AWQ.

Rank the Mixtral 8x7B 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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