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Senku 70B AWQ 4bit GEMM by bigquant

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Senku 70B AWQ 4bit GEMM is an open-source language model by bigquant. Features: 70b LLM, VRAM: 36.7GB, Context: 31K, Quantized, LLM Explorer Score: 0.11.

  4-bit   4bit   Awq   Conversational   Endpoints compatible   Llama   Miqu   Quantized   Region:us   Safetensors   Senku   Sharded   Tensorflow
Model Card on HF 🤗: https://huggingface.co/bigquant/Senku-70B-AWQ-4bit-GEMM 

Senku 70B AWQ 4bit GEMM Benchmarks

LLME Score: 0.11328
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Senku 70B AWQ 4bit GEMM (bigquant/Senku-70B-AWQ-4bit-GEMM)
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Senku 70B AWQ 4bit GEMM Parameters and Internals

Model Type 
quantized
Additional Notes 
AWQ Quantization with settings: zero_point: True, q_group_size: 128, w_bit: 4, version: GEMM.
LLM NameSenku 70B AWQ 4bit GEMM
Repository 🤗https://huggingface.co/bigquant/Senku-70B-AWQ-4bit-GEMM 
Model Size70b
Required VRAM36.7 GB
Updated2026-07-10
Maintainerbigquant
Model Typellama
Model Files  5.0 GB: 1-of-8   5.0 GB: 2-of-8   4.9 GB: 3-of-8   4.9 GB: 4-of-8   4.9 GB: 5-of-8   4.9 GB: 6-of-8   4.9 GB: 7-of-8   2.2 GB: 8-of-8
AWQ QuantizationYes
Quantization Typeawq|4bit
Model ArchitectureLlamaForCausalLM
Context Length32764
Model Max Length32764
Transformers Version4.37.2
Tokenizer ClassLlamaTokenizer
Padding Token<unk>
Vocabulary Size32000
Torch Data Typefloat16

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Note: green Score (e.g. "73.2") means that the model is better than bigquant/Senku-70B-AWQ-4bit-GEMM.

Rank the Senku 70B AWQ 4bit GEMM 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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