Dria-Agent-a-7B
A large language model trained based on the Qwen2.5-Coder series, focusing on agent applications.
Product Details
Dria-Agent-a-7B is a large language model based on Qwen2.5-Coder series training, focusing on agent applications. It adopts Pythonic function calling method, which has the advantages of single parallel multi-function calling, free-form reasoning and action, and instant complex solution generation compared with the traditional JSON function calling method. The model performs well on multiple benchmarks, including Berkeley Function Calling Leaderboard (BFCL), MMLU-Pro, and Dria-Pythonic-Agent-Benchmark (DPAB). The model size is 7.62 billion parameters, uses BF16 tensor type, and supports text generation tasks. Its main advantages include powerful programming assistance capabilities, efficient function calling methods, and high accuracy in specific fields. This model is suitable for application scenarios that require complex logic processing and multi-step task execution, such as automated programming, intelligent agents, etc. Currently, the model is available on the Hugging Face platform for free use by users.
Main Features
How to Use
Target Users
The target audience includes developers, researchers, and enterprises that require complex logic processing and multi-step task execution. For developers, this model can assist programming and improve development efficiency; for researchers, it provides a platform to study the application of large language models in specific fields; for enterprises, it can be used to build intelligent agent systems and optimize business processes.
Examples
Developers use this model to quickly generate code snippets and accelerate the software development process.
Researchers use models to study the performance of Pythonic function calls in different tasks.
Enterprises build intelligent agents to automatically handle customer inquiries and task scheduling.
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