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tencent/
Hunyuan-7B-Pretrain-0124

Name: Hunyuan-7B-Pretrain-0124
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Model Introduction

The 7B models released by Hunyuan this time: Hunyuan-7B-Pretrain-0124 and Hunyuan-7B-Instruct-0124 , use better data allocation and training, have strong performance, and have achieved a good balance between computing and performance. It stands out from many large-scale language models and is currently one of the strongest Chinese 7B Dense models.

Introduction to Technical Advantages

Model

Extended long text capability to 256K and utilizes Grouped Query Attention (GQA)

Inference Framework

This open-source release offers two inference backend options tailored for the Hunyuan-7B model: the popular vLLM-backend and the TensorRT-LLM Backend. In this release, we are initially open-sourcing the vLLM solution, with plans to release the TRT-LLM solution in the near future.

Training Framework

The Hunyuan-7B open-source model is fully compatible with the Hugging Face format, enabling researchers and developers to perform model fine-tuning using the hf-deepspeed framework. Learn more : Tencent-Hunyuan-Large 。

Benchmark

Note: The following benchmarks are evaluated by TRT-LLM-backend

Hunyuan-7B-Pretrain

	Qwen2.5-7B	Llama3-8B	OLMO2-7B	HunYuan-7B-V2
MMLU	74.26	66.95	63.7	75.37
MMLU-Pro	46.17	34.04	31	47.54
MMLU-CF	61.01	55.21	52.94	59.62
MMLU-Redux	73.47	66.44	63.74	74.54
BBH	70.4	62.16	38.01	70.77
HellaSwag	75.82	78.24	61.97	80.77
WinoGrande	69.69	73.64	74.43	71.51
PIQA	79.33	80.52	80.63	81.45
SIQA	77.48	61.05	65.2	79.73
NaturalQuestions	31.77	35.43	36.9	33.52
DROP	68.2	60.13	60.8	68.63
ARC-C	91.64	77.59	74.92	91.97
TriviaQA	69.31	78.61	78	74.31
Chinese-SimpleQA	30.37	19.4	7.35	30.51
SimpleQA	4.98	7.68	4.51	3.73
CMMLU	81.39	50.25	38.79	82.19
C-Eval	81.11	50.4	38.53	82.12
C3	71.77	61.5	54	79.07
GSM8K	82.71	57.54	67.5	93.33
MATH	49.6	18.45	19	62.15
CMATH	84.33	52.83	44	88.5
HumanEval	57.93	35.98	15.24	59.15

Hunyuan-7B-Instruct

Model	Qwen2.5-7B-Instruct	Llama-3-8B-Instruct	OLMo-2-1124-7B-DPO	Hunyuan-7B-Instruct
ARC-C	89.83	82.4	-	88.81
BBH	66.24	-	46.6	76.47
CEval	76.82	-	-	81.8
CMMLU	78.55	-	-	82.29
DROP_F1	80.63	-	60.5	82.96
GPQA	36.87	34.6	-	47.98
Gsm8k	80.14	80.6	85.1	90.14
HellaSwag	83.34	-	-	86.57
HumanEval	84.8	60.4	-	84.0
MATH	72.86	-	32.5	70.64
MMLU	72.36	68.5	61.3	79.18

Quick Start

You can refer to the content in Tencent-Hunyuan-Large to get started quickly. The training and inference code can use the version provided in this github repository.

Inference Framework

This open-source release offers two inference backend options tailored for the Hunyuan-7B model: the popular vLLM-backend and the TensorRT-LLM Backend. In this release, we are initially open-sourcing the vLLM solution, with plans to release the TRT-LLM solution in the near future.

Inference Performance

This section presents the efficiency test results of deploying various models using vLLM, including inference speed (tokens/s) under different batch sizes.

Inference Framework	Model	Number of GPUs (GPU productA)	input_length	batch=1	batch=4
vLLM	hunyuan-7B	1	2048	78.9	279.5

Contact Us

If you would like to leave a message for our R&D and product teams, Welcome to contact our open-source team . You can also contact us via email (hunyuan_opensource@tencent.com).

tencent/Hunyuan-7B-Pretrain-0124

Model Introduction

Introduction to Technical Advantages

Model

Inference Framework

Training Framework

Related News

Benchmark

Quick Start

Inference Framework

Inference Performance

Contact Us

tencent/
Hunyuan-7B-Pretrain-0124