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WizardLM 2 8x22 EXL2 4.0bpw by bullerwins

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WizardLM 2 8x22 EXL2 4.0bpw is an open-source language model by bullerwins. Features: LLM, VRAM: 70.9GB, Context: 64K, License: apache-2.0, Quantized, LLM Explorer Score: 0.14.

  Arxiv:2304.12244   Arxiv:2306.08568   Arxiv:2308.09583   4-bit   Conversational   Endpoints compatible   Exl2   Mixtral   Quantized   Region:us   Safetensors   Sharded   Tensorflow
Model Card on HF 🤗: https://huggingface.co/bullerwins/WizardLM-2-8x22-exl2_4.0bpw 

WizardLM 2 8x22 EXL2 4.0bpw Benchmarks

LLME Score: 0.14064
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
WizardLM 2 8x22 EXL2 4.0bpw (bullerwins/WizardLM-2-8x22-exl2_4.0bpw)
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WizardLM 2 8x22 EXL2 4.0bpw Parameters and Internals

Model Type 
Mixture of Experts (MoE)
Supported Languages 
en (Multilingual)
Training Details 
Methodology:
Fully AI powered synthetic training system
Input Output 
Input Format:
Adopts the prompt format from Vicuna
Accepted Modalities:
text
Performance Tips:
Supports multi-turn conversation
LLM NameWizardLM 2 8x22 EXL2 4.0bpw
Repository 🤗https://huggingface.co/bullerwins/WizardLM-2-8x22-exl2_4.0bpw 
Required VRAM70.9 GB
Updated2026-04-05
Maintainerbullerwins
Model Typemixtral
Model Files  8.6 GB: 1-of-9   8.6 GB: 2-of-9   8.6 GB: 3-of-9   8.6 GB: 4-of-9   8.6 GB: 5-of-9   8.6 GB: 6-of-9   8.6 GB: 7-of-9   8.6 GB: 8-of-9   2.1 GB: 9-of-9
Quantization Typeexl2
Model ArchitectureMixtralForCausalLM
Licenseapache-2.0
Context Length65536
Model Max Length65536
Transformers Version4.36.2
Tokenizer ClassLlamaTokenizer
Padding Token<unk>
Vocabulary Size32000
Torch Data Typebfloat16

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...M 2 8x22B Beige 2.4bpw H6 EXL264K / 42.7 GB60
...M 2 8x22B Beige 3.0bpw H6 EXL264K / 53.2 GB60
...rdLM 2 8x22B Beige EXL2 5.0bpw64K / 88.4 GB60
...M 2 8x22B Beige 4.0bpw H6 EXL264K / 70.8 GB50
...B Instruct V0.1 8.0bpw H8 EXL264K / 120.2 GB41
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Note: green Score (e.g. "73.2") means that the model is better than bullerwins/WizardLM-2-8x22-exl2_4.0bpw.

Rank the WizardLM 2 8x22 EXL2 4.0bpw 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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