Deepseek 2.5 AI Model
Deepseek 2.5 features a mixture-of-experts (MoE) architecture where only 22 billion parameters are active at any given time, resulting in faster inference. It excels at tasks like text generation and code completion, combining capabilities from the Deepseek V2-Chat and Coder-V2 Instruct models. The model is fine-tuned for human preferences, making it suitable for real-world applications that require human-like outputs and decision-making.
Comparison
Deepseek 2.5 surpasses other models in its category due to its fast inference speed and ability to handle both language and coding tasks. Its unique architecture enables it to process more efficiently than models like Mistral-Large, while maintaining a high degree of accuracy and relevance in output.
| Benchmark | DeepSeek-V2.5 | DeepSeek-V2 | GPT-4-Turbo-1106 | GPT-4-0613 | GPT-3.5 | Gemini1.5 Pro | Claude3 Opus | Claude3 Sonnet | Claude3 Haiku | abab-6.5 | abab-6.5s | ERNIE-4.0 | GLM-4 | Moonshot-v1 | Baichuan 3 | Qwen1.5 72B | LLaMA 3 70B | Mixtral 8x22B |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Chinese General - AlignBench | 8.04 | 7.89 | 8.01 | 7.53 | 6.08 | 7.33 | 7.62 | 6.7 | 6.42 | 7.97 | 7.34 | 7.89 | 7.88 | 7.22 | 7.19 | 7.42 | 6.49 | |
| English General - MT-Bench | 9.02 | 8.85 | 9.32 | 8.96 | 8.21 | 8.93 | 9.0 | 8.47 | 8.39 | 8.82 | 8.69 | 7.69 | 8.6 | 8.59 | 8.7 | 8.61 | 8.95 | 8.66 |
| Knowledge - MMLU | 80.4 | 80.6 | 84.6 | 86.4 | 70.0 | 81.9 | 86.8 | 79.0 | 75.2 | 79.5 | 74.6 | 81.5 | 81.7 | 76.2 | 80.3 | 77.8 | ||
| Arithmetic - GSM8K | 95.1 | 94.8 | 93.0 | 92.0 | 57.1 | 91.7 | 95.0 | 92.3 | 88.9 | 91.7 | 87.3 | 91.3 | 87.6 | 89.5 | 88.2 | 81.9 | 93.2 | 87.9 |
| Math - MATH | 74.7 | 71.0 | 64.1 | 52.9 | 34.1 | 58.5 | 61.0 | 40.5 | 40.9 | 51.4 | 42.0 | 52.2 | 47.9 | 44.2 | 49.2 | 40.6 | 48.5 | 49.8 |
| Reasoning - BBH | 84.3 | 83.4 | 83.1 | 66.6 | 84.0 | 86.8 | 82.9 | 73.7 | 82.0 | 76.8 | 82.3 | 84.5 | 65.9 | 80.1 | 78.4 | |||
| Coding - HumanEval | 89.0 | 84.8 | 82.2 | 84.1 | 48.1 | 71.9 | 84.9 | 73.0 | 75.9 | 78.0 | 68.3 | 72.0 | 72.0 | 82.9 | 70.1 | 68.9 | 76.2 | 75.0 |
Team
The Deepseek 2.5 project was developed by a large team of AI experts, combining deep learning engineers and researchers specialized in natural language processing and machine learning architectures. The team’s goal was to create an adaptable model that excels both in traditional language tasks and the more complex requirements of code generation.
Community
The Deepseek community is active on platforms such as Hugging Face, with regular updates and discussions about model performance and optimization. Enthusiasts and professionals alike engage in improving and fine-tuning the model for various use cases.