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
。
Related News
2025.1.24 We have open-sourced
Hunyuan-7B-Pretrain-0124
,
Hunyuan-7B-Instruct-0124
on Hugging Face.
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 (
[email protected]
).
Runs of tencent Hunyuan-7B-Instruct-0124 on huggingface.co
167
Total runs
0
24-hour runs
5
3-day runs
25
7-day runs
38
30-day runs
More Information About Hunyuan-7B-Instruct-0124 huggingface.co Model
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Hunyuan-7B-Instruct-0124 install url in huggingface.co: