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Mixtral 8x22B Instruct V0.1 GPTQ 4bit by jarrelscy

 »  All LLMs  »  jarrelscy  »  Mixtral 8x22B Instruct V0.1 GPTQ 4bit   URL Share it on

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Model Card on HF 🤗: https://huggingface.co/jarrelscy/Mixtral-8x22B-Instruct-v0.1-GPTQ-4bit 

Mixtral 8x22B Instruct V0.1 GPTQ 4bit Benchmarks

LLME Score: 0.19911
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 GPTQ 4bit (jarrelscy/Mixtral-8x22B-Instruct-v0.1-GPTQ-4bit)

Mixtral 8x22B Instruct V0.1 GPTQ 4bit Parameters and Internals

Model Type 
text generation
Additional Notes 
Quantized to 8-bit using GPTQ technique.
LLM NameMixtral 8x22B Instruct V0.1 GPTQ 4bit
Repository 🤗https://huggingface.co/jarrelscy/Mixtral-8x22B-Instruct-v0.1-GPTQ-4bit 
Model Size19.2b
Required VRAM74.1 GB
Updated2024-12-14
Maintainerjarrelscy
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   4.1 GB: 15-of-15
GPTQ QuantizationYes
Quantization Typegptq|4bit
Model ArchitectureMixtralForCausalLM
Context Length65536
Model Max Length65536
Transformers Version4.39.3
Tokenizer ClassLlamaTokenizer
Vocabulary Size32768
Torch Data Typefloat16

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...ixtral 8x22B Instruct V0.1 AWQ64K / 73.7 GB13610
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Note: green Score (e.g. "73.2") means that the model is better than jarrelscy/Mixtral-8x22B-Instruct-v0.1-GPTQ-4bit.

Rank the Mixtral 8x22B Instruct V0.1 GPTQ 4bit 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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Original data from HuggingFace, OpenCompass and various public git repos.
Release v20241124
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