Qwen2-72B

Branch Names

bpw = Bits per Weight hb = Bits for the lm_head layer

Quantization Details

Quantized via Exllamav2 Version: 0.1.6 All versions in this repo were quantized with the setting Rope Scale=4

Original Model Card

Introduction

Qwen2 is the new series of Qwen large language models. For Qwen2, we release a number of base language models and instruction-tuned language models ranging from 0.5 to 72 billion parameters, including a Mixture-of-Experts model. This repo contains the 72B Qwen2 base language model.

Compared with the state-of-the-art opensource language models, including the previous released Qwen1.5, Qwen2 has generally surpassed most opensource models and demonstrated competitiveness against proprietary models across a series of benchmarks targeting for language understanding, language generation, multilingual capability, coding, mathematics, reasoning, etc.

For more details, please refer to our blog, GitHub, and Documentation.

Model Details

Qwen2 is a language model series including decoder language models of different model sizes. For each size, we release the base language model and the aligned chat model. It is based on the Transformer architecture with SwiGLU activation, attention QKV bias, group query attention, etc. Additionally, we have an improved tokenizer adaptive to multiple natural languages and codes.

Requirements

The code of Qwen2 has been in the latest Hugging face transformers and we advise you to install transformers>=4.37.0, or you might encounter the following error:

KeyError: 'qwen2'

Usage

We do not advise you to use base language models for text generation. Instead, you can apply post-training, e.g., SFT, RLHF, continued pretraining, etc., on this model.

Performance

The evaluation of base models mainly focuses on the model performance of natural language understanding, general question answering, coding, mathematics, scientific knowledge, reasoning, multilingual capability, etc.

The datasets for evaluation include:

English Tasks: MMLU (5-shot), MMLU-Pro (5-shot), GPQA (5shot), Theorem QA (5-shot), BBH (3-shot), HellaSwag (10-shot), Winogrande (5-shot), TruthfulQA (0-shot), ARC-C (25-shot)

Coding Tasks: EvalPlus (0-shot) (HumanEval, MBPP, HumanEval+, MBPP+), MultiPL-E (0-shot) (Python, C++, JAVA, PHP, TypeScript, C#, Bash, JavaScript)

Math Tasks: GSM8K (4-shot), MATH (4-shot)

Chinese Tasks: C-Eval (5-shot), CMMLU (5-shot)

Multilingual Tasks: Multi-Exam (M3Exam 5-shot, IndoMMLU 3-shot, ruMMLU 5-shot, mMMLU 5-shot), Multi-Understanding (BELEBELE 5-shot, XCOPA 5-shot, XWinograd 5-shot, XStoryCloze 0-shot, PAWS-X 5-shot), Multi-Mathematics (MGSM 8-shot), Multi-Translation (Flores-101 5-shot)

Qwen2-72B performance

Datasets	DeepSeek-V2	Mixtral-8x22B	Llama-3-70B	Qwen1.5-72B	Qwen1.5-110B	Qwen2-72B
Architecture	MoE	MoE	Dense	Dense	Dense	Dense
#Activated Params	21B	39B	70B	72B	110B	72B
#Params	236B	140B	70B	72B	110B	72B
English
MMLU	78.5	77.8	79.5	77.5	80.4	84.2
MMLU-Pro	-	49.5	52.8	45.8	49.4	55.6
GPQA	-	34.3	36.3	36.3	35.9	37.9
Theorem QA	-	35.9	32.3	29.3	34.9	43.1
BBH	78.9	78.9	81.0	65.5	74.8	82.4
HellaSwag	87.8	88.7	88.0	86.0	87.5	87.6
WindoGrande	84.8	85.0	85.3	83.0	83.5	85.1
ARC-C	70.0	70.7	68.8	65.9	69.6	68.9
TruthfulQA	42.2	51.0	45.6	59.6	49.6	54.8
Coding
HumanEval	45.7	46.3	48.2	46.3	54.3	64.6
MBPP	73.9	71.7	70.4	66.9	70.9	76.9
EvalPlus	55.0	54.1	54.8	52.9	57.7	65.4
MultiPL-E	44.4	46.7	46.3	41.8	52.7	59.6
Mathematics
GSM8K	79.2	83.7	83.0	79.5	85.4	89.5
MATH	43.6	41.7	42.5	34.1	49.6	51.1
Chinese
C-Eval	81.7	54.6	65.2	84.1	89.1	91.0
CMMLU	84.0	53.4	67.2	83.5	88.3	90.1
Multilingual
Mulit-Exam	67.5	63.5	70.0	66.4	75.6	76.6
Multi-Understanding	77.0	77.7	79.9	78.2	78.2	80.7
Multi-Mathematics	58.8	62.9	67.1	61.7	64.4	76.0
Multi-Translation	36.0	23.3	38.0	35.6	36.2	37.8

Citation

If you find our work helpful, feel free to give us a cite.

@article{qwen2,
  title={Qwen2 Technical Report},
  year={2024}
}