Orion-MoE8x7B is a pretrained foundation large language model with a sparse Mixture of Experts (MoE) architecture. The model is trained from scratch on a multilingual corpus comprising approximately 5 trillion tokens, including launguages such as Chinese, English, Japanese, Korean, and more.
Key Features of Orion-MoE8x7B
The model demonstrates exceptional performance in comprehensive evaluations compared to other models of the same parameter scale.
The model excels in multilingual benchmarks, significantly outperforming in Japanese and Korean test sets, and also performing strong results in Arabic, German, French, and Spanish evaluations.
Leveraging its sparse MoE structure, the model achieves faster inference speeds compared to dense models of similar scale.
Model Architecture
Configuration
Orion-MoE 8x7B
Hidden Size
4096
# Layers
32
# Query Heads
32
# KV Heads
8
Intermediate Size
14592
# Experts
8
# Activated Experts
2
Embedding Tying
False
Position embedding
RoPE
seq_len
8192
Vocabulary Size
113664
Training hyper-parameters
We use the AdamW optimizer with hyperparameters set to 𝛽1 = 0.9, 𝛽2 = 0.95, and a weight decay of 0.1.
Training begins with a learning rate warm-up phase over 2000 iterations, where the learning rate is linearly increased to a peak of 3e-4. Afterward, a cosine schedule is applied to gradually reduce the learning rate to 3e-5 over the course of training.
The model is trained using BF16/FP32 mixed precision, with a batch size of 2600, processing approximately 22 million tokens per step.
Data Distribution
The training dataset is primarily composed of English, Chinese, which together account for over 75% of the total data. The remaining dataset includes other languages, programming code, mathematical data, etc. A detailed breakdown of the topic distribution is provided in the table below.
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We are pleased to release our foundation model at this moment and will be releasing the instruction model shortly. Stay tuned!
2. Model Download
Model release and download links are provided in the table below:
3.1. LLM evaluation results on examination and professional knowledge
TestSet
Mixtral 8x7B
Qwen1.5-32b
Qwen2.5-32b
Orion 14B
Qwen2-57B-A14
Orion MoE8x7B
MMLU
70.4
73.4
82.9
69.9
76.5
85.9
MMLU Pro
38.5
45.3
58.0
34.0
48.6
58.3
CEval
54.1
83.5
87.7
72.8
87.7
89.7
CMMLU
53.2
82.3
89.0
70.6
88.5
89.2
ARC_c
85.1
90.2
94.2
79.7
91.5
91.9
HellaSwag
81.9
82.0
82.5
78.5
85.2
89.2
LAMBADA
76.8
73.7
75.4
78.8
72.6
79.7
BBH
50.9
57.3
67.7
50.4
55.1
55.8
MuSR
43.2
42.7
49.8
43.6
39.0
49.9
PIQA
83.4
82.2
80.1
79.5
81.9
87.3
CommonSenseQA
69.6
74.7
73.0
66.9
69.9
73.1
IFEval
24.2
33.0
41.6
29.1
31.2
30.1
GQPA
30.9
33.5
49.5
28.5
32.6
52.2
HumanEval
33.5
36.0
47.0
20.1
53.0
44.5
3.2. Comparison of LLM performances on Japanese testsets
Model
Average
JSQuAD
JCommonSenseQA
JNLI
MARC-ja
JAQKET v2
PAWS-ja
Mixtral-8x7B
69.8
89.0
78.7
32.1
95.4
78.9
44.5
Qwen1.5-32B
74.7
89.9
84.5
51.0
97.1
82.1
43.8
Qwen2.5-32B
80.7
89.1
93.8
72.1
97.9
89.3
42.2
Orion-14B
74.2
74.2
88.2
72.8
94.1
66.2
49.9
Orion-MoE8x7B
82.9
91.8
90.4
90.5
96.4
81.2
47.4
3.3. Comparison of LLM performances on Korean testsets
Model
Average
HAE-RAE
KoBEST BoolQ
KoBEST COPA
KoBEST HellaSwag
KoBEST SentiNeg
KoBEST WiC
PAWS-ko
Mixtral-8x7B
60.7
53.2
78.6
66.2
56.6
77.1
49.4
44.1
Qwen1.5-32B
58.6
46.4
76.3
60.4
53.0
78.3
52.1
43.4
Qwen2.5-32B
71.4
70.7
80.3
76.7
61.2
96.5
77.2
37.1
Orion-14B
67.7
69.7
80.6
77.1
58.2
92.4
51.2
44.6
Orion-MoE8x7B
72.0
65.2
85.4
80.4
56.0
97.0
73.6
46.4
3.4. Comparison of LLM performances on Arabic, German, French, and Spanish testsets
Language
Spanish
French
German
Arabic
Model
HellaSwag
ARC
HellaSwag
ARC
HellaSwag
ARC
HellaSwag
ARC
Mixtral-8x7B
74.3
54.8
73.9
55.9
69.2
52.4
47.9
36.3
Qwen1.5-32B
70.5
55.1
68.9
56.0
63.8
50.8
50.1
40.0
Qwen2.5-32B
75.0
65.3
74.2
62.7
69.8
61.8
59.8
52.9
Orion-14B
62.0
44.6
60.2
42.3
54.7
38.9
42.3
33.9
Orion-MoE8x7B
87.4
70.1
85.6
68.8
80.6
63.5
69.4
54.3
3.5. Leakage Detection Benchmark
When the pre-training data of a large language model contains content from a specific dataset, the model’s performance on that dataset may be artificially enhanced, leading to inaccurate performance evaluations. To address this issue, researchers from the Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, and other institutions have proposed a simple and effective method for detecting data leakage. This method leverages the interchangeable nature of multiple-choice options by shuffling the options in the original dataset to generate derived data. The log-probability distribution of the derived dataset is then computed using the model to detect whether the original dataset has been leaked.
Setup inference server on 8x Nvidia RTX3090， and get results from client in unit of 'tokens per second'.
Models
8x3090 1 concurrent
8x3090 4 concurrent
4xA100 1 concurrent
4xA100 4 concurrent
Qwen32
52.93
46.06
62.43
56.81
Orion-MoE
102.77
54.61
107.76
61.83
We also tested on a 4x A100, comparing inference speeds based on different input lengths (tokens), get results from client in unit of 'tokens per second'.
Input
4k
8k
12k
16k
32k
64k
Qwen32
53.99
47.59
25.98
24.35
18.64
11.86
Orion-MoE
90.86
54.40
31.08
29.04
22.69
14.51
4. Model Inference
Model weights, source code, and configuration needed for inference are published on Hugging Face, and the download link
is available in the table at the beginning of this document. We demonstrate various inference methods here, and the
program will automatically download the necessary resources from Hugging Face.
In the above Python code, the model is loaded with device_map='auto' to utilize all available GPUs. To specify the
device, you can use something like export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 (using GPUs 0,1,2,3,4,5,6,7).
docker run --gpus all -it -p 9999:9999 -v $(pwd)/logs:/workspace/logs:rw -v $HOME/Downloads:/workspace/models -e CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 -e MODEL_DIR=Orion-MoE8x7B -e MODEL_NAME=orion-moe vllm_server:0.0.0.0
Run inference
curl http://0.0.0.0:9999/v1/chat/completions -H "Content-Type: application/json" -d '{"model": "orion-moe","temperature": 0.2,"stream": false, "messages": [{"role": "user", "content":"Which company developed you as an AI agent?"}]}'
5. Declarations, License
5.1. Declarations
We strongly urge all users not to use the Orion-MoE8x7B model for any activities that may harm national or social security or violate the law.
Additionally, we request users not to use the Orion-MoE8x7B model for internet services without proper security review and filing.
We hope all users abide by this principle to ensure that technological development takes place in a regulated and legal environment.
We have done our best to ensure the compliance of the data used in the model training process. However, despite our
significant efforts, unforeseen issues may still arise due to the complexity of the model and data. Therefore, if any
problems arise due to the use of the Orion-MoE8x7B open-source model, including but not limited to data security
issues, public opinion risks, or any risks and issues arising from the model being misled, abused, disseminated, or
improperly utilized, we will not assume any responsibility.
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OrionStar aims to make society and the world a better place.
OrionStar possesses fully self-developed end-to-end artificial intelligence technologies, such as voice interaction and
visual navigation. It integrates product development capabilities and technological application capabilities. Based on
the Orion robotic arm platform, it has launched products such as OrionStar AI Robot Greeting, AI Robot Greeting Mini,
Lucki, Coffee Master, and established the open platform OrionOS for Orion robots. Following the philosophy of "Born for
Truly Useful Robots", OrionStar empowers more people through AI technology.
The core strengths of OrionStar lies in possessing end-to-end AI application capabilities, including big data preprocessing, large model pretraining, fine-tuning, prompt engineering, agent, etc. With comprehensive end-to-end model training capabilities, including systematic data processing workflows and the parallel model training capability of hundreds of GPUs, it has been successfully applied in various industry scenarios such as government affairs, cloud services, international e-commerce, and fast-moving consumer goods.
Companies with demands for deploying large-scale model applications are welcome to contact us. Enquiry Hotline: 400-898-7779 E-mail: ai@orionstar.com Discord Link: https://discord.gg/zumjDWgdAs
