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Yi 1.5 9B Chat AWQ by modelscope

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Model Card on HF 🤗: https://huggingface.co/modelscope/Yi-1.5-9B-Chat-AWQ 

Yi 1.5 9B Chat AWQ Benchmarks

ARC: 63.65 vs 96.7 (so35)-34.2%
HellaSwag: 80.94 vs 95.3 (gpt4)-15.1%
MMLU: 71.01 vs 88.3 (so35)-19.6%
TruthfulQA: 52.67 vs 59 (gpt4)-10.7%
WinoGrande: 77.19 vs 87.5 (gpt4)-11.8%
GSM8K: 71.87 vs 96.4 (so35)-25.4%
LLME Score: 0.16291
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Yi 1.5 9B Chat AWQ (modelscope/Yi-1.5-9B-Chat-AWQ)
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Yi 1.5 9B Chat AWQ Parameters and Internals

Model Type 
text-generation
Additional Notes 
We use the modelscope swift repository to perform AWQ 4bit quantization.
Supported Languages 
en (proficient), zh (proficient)
LLM NameYi 1.5 9B Chat AWQ
Repository 🤗https://huggingface.co/modelscope/Yi-1.5-9B-Chat-AWQ 
Base Model(s)  01-ai/Yi-1.5-9B-Chat   01-ai/Yi-1.5-9B-Chat
Model Size9b
Required VRAM5.3 GB
Updated2025-12-04
Maintainermodelscope
Model Typellama
Model Files  4.8 GB: 1-of-2   0.5 GB: 2-of-2
AWQ QuantizationYes
Quantization Typeawq
Model ArchitectureLlamaForCausalLM
Licenseapache-2.0
Context Length4096
Model Max Length4096
Transformers Version4.39.3
Tokenizer ClassLlamaTokenizer
Padding Token<unk>
Vocabulary Size64000
Torch Data Typefloat16

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Rank the Yi 1.5 9B 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  
Text Generation  
Text Summarization and Feature Extraction  
Code Generation  
Multi-Language Support and Translation  

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Release v20241124
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