DeepSeek-V2.5
Maintainer: deepseek-ai
496
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| Property | Value |
|---|---|
| Run this model | Run on HuggingFace |
| API spec | View on HuggingFace |
| Github link | No Github link provided |
| Paper link | No paper link provided |
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Model overview
DeepSeek-V2.5 is an upgraded version that combines the capabilities of the previous DeepSeek-V2-Chat and DeepSeek-Coder-V2-Instruct models. The new model integrates the general and coding abilities of the two earlier versions. Compared to the previous models, DeepSeek-V2.5 better aligns with human preferences and has been optimized in various aspects, including writing and instruction following. It demonstrates improved performance on benchmarks like AlpacaEval 2.0, ArenaHard, AlignBench, MT-Bench, HumanEval, LiveCodeBench, Aider, DS-FIM-Eval, and DS-Arena-Code.
Model inputs and outputs
Inputs
DeepSeek-V2.5accepts natural language inputs for a wide range of tasks, including general conversation, coding assistance, and task completion.
Outputs
- The model generates relevant and coherent responses to the provided inputs, leveraging its enhanced language understanding and generation capabilities.
- Output formats can include text, code snippets, structured data, and more, depending on the specific task.
Capabilities
DeepSeek-V2.5 demonstrates strong performance across a variety of tasks, including general language understanding, coding assistance, mathematical reasoning, and task-oriented dialogue. For example, the model can engage in open-ended conversations, provide step-by-step instructions for coding problems, and assist with data analysis and visualization tasks.
What can I use it for?
With its broad capabilities, DeepSeek-V2.5 can be leveraged for a wide range of applications, such as:
- Building AI-powered virtual assistants for customer support, task automation, and knowledge sharing.
- Developing intelligent coding tools to enhance developer productivity and code quality.
- Integrating language-powered features into business applications, such as summarization, question answering, and natural language interfaces.
- Exploring research opportunities in areas like multimodal AI, language model interpretability, and AI safety.
Things to try
Some interesting things to try with DeepSeek-V2.5 include:
- Engaging the model in open-ended conversations to explore its general language understanding and generation capabilities.
- Providing it with coding-related prompts to observe its problem-solving skills and ability to generate high-quality code.
- Experimenting with the model's ability to follow complex instructions and complete multi-step tasks.
- Investigating how the model handles edge cases, such as ambiguous inputs or requests for unethical actions, to better understand its robustness and safety.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
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