Mixtral 8x7B Instruct V0.1 Hf Attn 4bit MoE 2bitgs8 Metaoffload 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 2bitgs8 Metaoffload 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 2bitgs8 Metaoffload HQQ (mobiuslabsgmbh/Mixtral-8x7B-Instruct-v0.1-hf-attn-4bit-moe-2bitgs8-metaoffload-HQQ)

Mixtral 8x7B Instruct V0.1 Hf Attn 4bit MoE 2bitgs8 Metaoffload HQQ Parameters and Internals

Model Type 
text-generation
Additional Notes 
This model uses Half-Quadratic Quantization (HQQ) for quantizing attention layers to 4-bit and experts to 2-bit, enabling a significant reduction in VRAM usage.
LLM NameMixtral 8x7B Instruct V0.1 Hf Attn 4bit MoE 2bitgs8 Metaoffload HQQ
Repository ๐Ÿค—https://huggingface.co/mobiuslabsgmbh/Mixtral-8x7B-Instruct-v0.1-hf-attn-4bit-moe-2bitgs8-metaoffload-HQQ 
Required VRAM24.1 GB
Updated2024-12-26
Maintainermobiuslabsgmbh
Model Typemixtral
Instruction-BasedYes
Model Files  24.1 GB
Quantization Type4bit
Model ArchitectureMixtralForCausalLM
Licenseapache-2.0
Context Length32768
Model Max Length32768
Transformers Version4.37.2
Tokenizer ClassLlamaTokenizer
Vocabulary Size32000
Torch Data Typefloat16

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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-2bitgs8-metaoffload-HQQ.

Rank the Mixtral 8x7B Instruct V0.1 Hf Attn 4bit MoE 2bitgs8 Metaoffload 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 v20241227