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Aquila2 34B by BAAI

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  Arxiv:2408.07410   Aquila   Autotrain compatible   Conversational   Custom code   Region:us   Safetensors   Sharded   Tensorflow
Model Card on HF 🤗: https://huggingface.co/BAAI/Aquila2-34B 

Aquila2 34B Benchmarks

ARC: 52.65 vs 96.7 (so35)-45.6%
HellaSwag: 81.99 vs 95.3 (gpt4)-14%
MMLU: 76.02 vs 88.3 (so35)-13.9%
TruthfulQA: 40.8 vs 59 (gpt4)-30.8%
WinoGrande: 75.06 vs 87.5 (gpt4)-14.2%
GSM8K: 0.45 vs 96.4 (so35)-99.5%
LLME Score: 0.15192
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Aquila2 34B (BAAI/Aquila2-34B)
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Aquila2 34B Parameters and Internals

Model Type 
base language model, chat model
Additional Notes 
The Aquila2 model series includes both base language models and chat models, featuring Aquila2-7B, Aquila2-34B, AquilaChat2-7B, AquilaChat2-34B, AquilaChat2-7B-16k, and AquilaChat2-34B-16k.
Release Notes 
Date:
2024.6.6
Notes:
Updated the basic language model Aquila2-34B with a new tokenizer and increased maximum processing length from 2048 to 8192.
LLM NameAquila2 34B
Repository 🤗https://huggingface.co/BAAI/Aquila2-34B 
Model Size34b
Required VRAM136.4 GB
Updated2025-09-23
MaintainerBAAI
Model Typeaquila
Model Files  4.5 GB: 1-of-29   4.9 GB: 2-of-29   4.9 GB: 3-of-29   4.7 GB: 4-of-29   4.9 GB: 5-of-29   4.9 GB: 6-of-29   4.9 GB: 7-of-29   4.7 GB: 8-of-29   4.9 GB: 9-of-29   4.9 GB: 10-of-29   4.9 GB: 11-of-29   4.7 GB: 12-of-29   4.9 GB: 13-of-29   4.9 GB: 14-of-29   4.9 GB: 15-of-29   4.7 GB: 16-of-29   4.9 GB: 17-of-29   4.9 GB: 18-of-29   4.9 GB: 19-of-29   4.7 GB: 20-of-29   4.9 GB: 21-of-29   4.9 GB: 22-of-29   4.9 GB: 23-of-29   4.7 GB: 24-of-29   4.9 GB: 25-of-29   4.9 GB: 26-of-29   4.9 GB: 27-of-29   2.2 GB: 28-of-29   3.5 GB: 29-of-29
Model ArchitectureAquilaForCausalLM
Licenseother
Context Length8192
Model Max Length8192
Transformers Version4.42.3
Tokenizer ClassPreTrainedTokenizerFast
Padding Token<|end_of_text|>
Vocabulary Size143973
Torch Data Typefloat32

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Rank the Aquila2 34B 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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