Mixtral 8x7B Instruct V0.1 Hf Attn 4bit MoE 2bit HQQ by mobiuslabsgmbh

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  4bit   Autotrain compatible   Conversational   Instruct   Mixtral   Moe   Quantized   Region:us

Mixtral 8x7B Instruct V0.1 Hf Attn 4bit MoE 2bit HQQ Benchmarks

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 Hf Attn 4bit MoE 2bit HQQ (mobiuslabsgmbh/Mixtral-8x7B-Instruct-v0.1-hf-attn-4bit-moe-2bit-HQQ)

Mixtral 8x7B Instruct V0.1 Hf Attn 4bit MoE 2bit HQQ Parameters and Internals

Model Type 
text-generation
Additional Notes 
This version of the model is quantized using Half-Quadratic Quantization (HQQ) with attention layers at 4-bit and experts at 2-bit.
Input Output 
Input Format:
String prompt
Accepted Modalities:
text
Output Format:
Generated text
LLM NameMixtral 8x7B Instruct V0.1 Hf Attn 4bit MoE 2bit HQQ
Repository ๐Ÿค—https://huggingface.co/mobiuslabsgmbh/Mixtral-8x7B-Instruct-v0.1-hf-attn-4bit-moe-2bit-HQQ 
Required VRAM18.2 GB
Updated2024-12-14
Maintainermobiuslabsgmbh
Model Typemixtral
Instruction-BasedYes
Model Files  18.2 GB
Quantization Type4bit
Model ArchitectureMixtralForCausalLM
Licenseapache-2.0
Context Length32768
Model Max Length32768
Transformers Version4.36.1
Tokenizer ClassLlamaTokenizer
Vocabulary Size32000
Torch Data Typebfloat16

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Note: green Score (e.g. "73.2") means that the model is better than mobiuslabsgmbh/Mixtral-8x7B-Instruct-v0.1-hf-attn-4bit-moe-2bit-HQQ.

Rank the Mixtral 8x7B Instruct V0.1 Hf Attn 4bit MoE 2bit HQQ 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