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Qwen1.5 110B Chat AWQ by study-hjt

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Qwen1.5 110B Chat AWQ is an open-source language model by study-hjt. Features: 110b LLM, VRAM: 61.1GB, Context: 32K, License: other, Quantized, HF Score: 68, LLM Explorer Score: 0.14, Arc: 72, HellaSwag: 84.7, MMLU: 78, TruthfulQA: 65.9, WinoGrande: 77.4, GSM8K: 30.1.

  Arxiv:2309.16609   4-bit   4bits   Autotrain compatible   Awq   Chat   Conversational   En   Endpoints compatible   Int4   Quantized   Qwen   Qwen2   Region:us   Safetensors   Sharded   Tensorflow
Model Card on HF 🤗: https://huggingface.co/study-hjt/Qwen1.5-110B-Chat-AWQ 

Qwen1.5 110B Chat AWQ Benchmarks

ARC: 72.01 vs 96.7 (so35)-25.5%
HellaSwag: 84.67 vs 95.3 (gpt4)-11.2%
MMLU: 78.04 vs 88.3 (so35)-11.6%
TruthfulQA: 65.86 vs 59 (gpt4)11.6%
WinoGrande: 77.35 vs 87.5 (gpt4)-11.6%
GSM8K: 30.1 vs 96.4 (so35)-68.8%
LLME Score: 0.1437
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Qwen1.5 110B Chat AWQ (study-hjt/Qwen1.5-110B-Chat-AWQ)
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Qwen1.5 110B Chat AWQ Parameters and Internals

LLM NameQwen1.5 110B Chat AWQ
Repository 🤗https://huggingface.co/study-hjt/Qwen1.5-110B-Chat-AWQ 
Base Model(s)  Qwen/Qwen1.5-110B-Chat   Qwen/Qwen1.5-110B-Chat
Model Size110b
Required VRAM61.1 GB
Updated2025-09-14
Maintainerstudy-hjt
Model Typeqwen2
Model Files  4.9 GB: 1-of-13   4.9 GB: 2-of-13   4.9 GB: 3-of-13   4.9 GB: 4-of-13   4.9 GB: 5-of-13   4.9 GB: 6-of-13   4.9 GB: 7-of-13   4.9 GB: 8-of-13   4.9 GB: 9-of-13   4.9 GB: 10-of-13   4.9 GB: 11-of-13   4.7 GB: 12-of-13   2.5 GB: 13-of-13
Supported Languagesen
AWQ QuantizationYes
Quantization Typeawq
Model ArchitectureQwen2ForCausalLM
Licenseother
Context Length32768
Model Max Length32768
Transformers Version4.39.3
Tokenizer ClassQwen2Tokenizer
Padding Token<|endoftext|>
Vocabulary Size152064
Torch Data Typefloat16
Errorsreplace

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Note: green Score (e.g. "73.2") means that the model is better than study-hjt/Qwen1.5-110B-Chat-AWQ.

Rank the Qwen1.5 110B Chat AWQ Capabilities

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Instruction Following and Task Automation  
Factuality and Completeness of Knowledge  
Censorship and Alignment  
Data Analysis and Insight Generation  
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Multi-Language Support and Translation  

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