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Models

DecentralGPT Network encompasses various GPT models, including both open-source and closed-source models.

Users can select different models to perform tasks as needed.
Model developers can also submit their models to the DecentralGPT Network.
All user data is encrypted and stored on a decentralized storage network, making it inaccessible to unauthorized parties.

We already support the world’s most powerful LLM model–LIama3.1-405B

Category Benchmark
Llama 3.1 70B
Llama 3.3 70B
Amazon Nova Pro
Gemini Pro 1.5
GPT-4o
General
MMLU (0-shot,CoT)
86.0
86.0
85.9
87.1
87.5
MMLU PRO (5-shot,CoT)
66.4
68.9
–
76.1
73.8
Instruction Following
IFEval
87.5
92.1
92.1
81.9
84.6
code
HumanEval (0-shot)
80.5
88.4
89.0
90.2
86.0
MBPP EvalPlus (base) (0-shot)
86.0
87.6
–
87.8
83.9
Math
MATH (0-shot,CoT)
68.0
77.0
76.6
82.9
76.9
Reasoning
ARC Challenge (0-shot)
48.0
50.5
–
53.5
47.5
Tool Use
BFCL v2 (0-shot)
77.5
77.3
–
80.3
74.0
Long Context
NIH/Multi-needle
97.5
97.5
–
94.7
–
Multilingual
Multilingual MGSM (0-shot)
86.9
91.1
–
89.6
90.6
Category Benchmark
Flux.1-dev
HiDream-11
PixArt-alpha
CogView4-6B
Janus-Pro-7B
Averaged
32.47
33.82
–
32.31
–
Animation
33.87
35.05
–
33.23
–
Concept-art
32.27
33.74
–
32.60
–
Painting
32.62
33.88
–
32.89
–
Photo
31.11
32.61
–
30.52
–
DPG-Bench
Overall
83.79
85.89
71.11
85.13
84.19
DPG-Bench
Global
85.80
76.44
74.97
83.85
86.90
DPG-Bench
Entity
86.79
90.22
79.32
90.35
88.90
DPG-Bench
Attribute
89.98
89.48
78.60
91.17
89.40
DPG-Bench
Relation
90.04
93.74
82.57
91.14
89.32
DPG-Bench
Other
89.90
91.83
76.96
87.29
89.48
GenEval
Overall
0.66
0.83
0.48
0.73
0.80
GenEval
Single obj.
0.98
1.00
0.98
0.99
0.99
GenEval
Two obj.
0.79
0.98
0.50
0.86
0.89
GenEval
Counting
0.73
0.79
0.44
0.66
0.59
GenEval
Colors
0.77
0.91
0.80
0.79
0.90
GenEval
Position
0.22
0.60
0.08
0.48
0.79
GenEval
Color attribution
0.45
0.72
0.07
0.58
0.66

Llama3.3

An auto-regressive language model that uses an optimized transformer architecture.

  • Development Team: Meta
  • Launch Date: 2024.12
  • Model Parameters: 70B
  • Features: Llama 3.3 was pretrained on ~15 trillion tokens of data from publicly available sources. The fine-tuning data includes publicly available instruction datasets, as well as over 25M synthetically generated examples.
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Pixtral Large 1.0

Second model in our multimodal family and demonstrates frontier-level image understanding.

  • Development Team: Meta
  • Launch Date: 2024.11
  • Model Parameters: 124B
  • Features: High-quality image generation, diversity and controllability, multimodal input.
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Qwen2.5-72B

The open-source GPT large model trained by Tongyi Qianwen possesses 72B parameters.

  • Development Team: Tongyi Qianwen(aliyun)
  • Launch Date: 2024.9
  • Model Parameters: 72B
  • Features: 27 language support, surpport long texts of up to 128 tokens, high memory utilization and optimization, user-friendly model interfaces.
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Nemotron 70B

Nvidia’s largest LLM model.

  • Development Team: Nvidia
  • Launch Date: 2024.10
  • Model Parameters: 70B
  • Features: Innovative technical architecture, efficient training data, and promoting sustainable development of the AI ecosystem.
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NVLM-D-72B

Nvidia’s Multimodal LLM model.

  • Development Team: Nvidia
  • Launch Date: 2024.10
  • Model Parameters: 72B
  • Features: Multimodal capabilities, exceptional text processing abilities, and outstanding mathematical reasoning skills.
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DeepSeek-Coder-V2

An open-source Mixture-of-Experts code language model that achieves performance comparable to GPT4-Turbo in code-specific tasks.

  • Launch Date: 2024.6
  • Model Parameters: 6000B
  • Features: Intelligent Code Completion, Automatic Code Review, Interactive Development, Real-time Collaboration, and Documentation Platform.
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Qwen2.5-Coder-32B

Qwen2.5-Coder is the latest series of Code-Specific Qwen large language models.

  • Launch Date: 2024.9
  • Model Parameters: 32B
  • Features: Context-Aware Suggestions, Automatic Code Review, Real-Time Collaboration.
Get Started