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Mixtral 34Bx2 MoE 60B AWQ by TheBloke

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Mixtral 34Bx2 MoE 60B AWQ is an open-source language model by TheBloke. Features: 60.8b LLM, VRAM: 32.8GB, Context: 195K, License: cc-by-nc-4.0, MoE, Quantized, LLM Explorer Score: 0.11.

  4-bit   Awq Base model:cloudyu/mixtral 34b... Base model:quantized:cloudyu/m...   Mixtral   Moe   Quantized   Region:us   Safetensors   Sharded   Tensorflow
Model Card on HF 🤗: https://huggingface.co/TheBloke/Mixtral_34Bx2_MoE_60B-AWQ 

Mixtral 34Bx2 MoE 60B AWQ Benchmarks

LLME Score: 0.11001
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Mixtral 34Bx2 MoE 60B AWQ (TheBloke/Mixtral_34Bx2_MoE_60B-AWQ)
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Mixtral 34Bx2 MoE 60B AWQ Parameters and Internals

Model Type 
mixtral
Additional Notes 
Quantization techniques include AWQ for efficient, low-bit weight usage.
Supported Languages 
English (native), Chinese (native)
Input Output 
Input Format:
{prompt}
Accepted Modalities:
text
Performance Tips:
For optimal performance, ensure correct installation of required software versions.
LLM NameMixtral 34Bx2 MoE 60B AWQ
Repository 🤗https://huggingface.co/TheBloke/Mixtral_34Bx2_MoE_60B-AWQ 
Model NameMixtral 34Bx2 MoE 60B
Model Creatorhai
Base Model(s)  Mixtral 34Bx2 MoE 60B   cloudyu/Mixtral_34Bx2_MoE_60B
Model Size60.8b
Required VRAM32.8 GB
Updated2026-05-21
MaintainerTheBloke
Model Typemixtral
Model Files  9.9 GB: 1-of-4   9.9 GB: 2-of-4   9.9 GB: 3-of-4   3.1 GB: 4-of-4
AWQ QuantizationYes
Quantization Typeawq
Model ArchitectureMixtralForCausalLM
Licensecc-by-nc-4.0
Context Length200000
Model Max Length200000
Transformers Version4.37.0.dev0
Tokenizer ClassLlamaTokenizer
Padding Token<s>
Vocabulary Size64000
Torch Data Typefloat16

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Bagel Hermes 2x34B195K / 121.9 GB13616
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...34Bx2 MoE V0.1 Full Linear DPO195K / 121.8 GB1062
FusionNet 34Bx2 MoE V0.1195K / 121.2 GB898
Note: green Score (e.g. "73.2") means that the model is better than TheBloke/Mixtral_34Bx2_MoE_60B-AWQ.

Rank the Mixtral 34Bx2 MoE 60B 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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