Qwen2.5-1M models and inference framework support for long-context tasks, with a context length of up to 1M tokens.
A tutorial on using Qwen2.5–7B-Instruct for creating a local, open-source, multi-agentic RAG system.
The implementation described in the article focuses on creating a multi-agentic Retrieval-Augmented Generation (RAG) system using code agents and the Qwen2.5–7B-Instruct model. The system consists of three agents working together in a hierarchical structure:
1. **Manager Agent**: This top-level agent breaks down user questions into sub-tasks, utilizes the Wikipedia search agent to find information, and combines the results to provide a final answer. Its system prompt is tailored to guide it through the process of decomposing tasks and coordinating with other agents.
2. **Wikipedia Search Agent**: This agent interacts with the Wikipedia search tool to identify relevant pages and their summaries. It further delegates to the page search agent for detailed information retrieval from specific pages if needed. Its prompt is designed to help it navigate Wikipedia effectively and extract necessary information.
3. **Page Search Agent**: This agent specializes in extracting precise information from a given Wikipedia page. It uses a semantic search tool to locate specific passages related to the query.
To implement the multi-agent system efficiently, the article mentions several key decisions and modifications to the default Hugging Face implementation:
- **Prompting**: Customized prompts for each agent, including specific examples that mirror the model’s chat template, to improve task-specific performance.
- **History Summarization**: Limiting the history passed to each step to avoid excessive context length and improve execution speed.
- **Tool Wrapping**: Wrapping managed agents as tools to allow better control over the prompts and streamline the architecture.
- **Error Handling**: Implementing mechanisms to handle tool execution errors effectively.
- **Execution Limiting**: Setting a maximum number of attempts for the page search agent to prevent infinite loops when searching for information that might not be present on the page.
- **Tool Response Modification**: Adapting the tool response format to fit the Qwen2.5–7B-Instruct model’s chat template, which supports only system, user, and assistant roles.
By structuring the implementation with these considerations, the system achieves the capability to perform complex, multi-hop question-answering tasks efficiently, despite being powered by a relatively small model running on consumer-grade hardware
The implementation described in the article focuses on creating a multi-agentic Retrieval-Augmented Generation (RAG) system using code agents and the Qwen2.5–7B-Instruct model. The system consists of three agents working together in a hierarchical structure:
1. **Manager Agent**: This top-level agent breaks down user questions into sub-tasks, utilizes the Wikipedia search agent to find information, and combines the results to provide a final answer. Its system prompt is tailored to guide it through the process of decomposing tasks and coordinating with other agents.
2. **Wikipedia Search Agent**: This agent interacts with the Wikipedia search tool to identify relevant pages and their summaries. It further delegates to the page search agent for detailed information retrieval from specific pages if needed. Its prompt is designed to help it navigate Wikipedia effectively and extract necessary information.
3. **Page Search Agent**: This agent specializes in extracting precise information from a given Wikipedia page. It uses a semantic search tool to locate specific passages related to the query.
To implement the multi-agent system efficiently, the article mentions several key decisions and modifications to the default Hugging Face implementation:
- **Prompting**: Customized prompts for each agent, including specific examples that mirror the model’s chat template, to improve task-specific performance.
- **History Summarization**: Limiting the history passed to each step to avoid excessive context length and improve execution speed.
- **Tool Wrapping**: Wrapping managed agents as tools to allow better control over the prompts and streamline the architecture.
- **Error Handling**: Implementing mechanisms to handle tool execution errors effectively.
- **Execution Limiting**: Setting a maximum number of attempts for the page search agent to prevent infinite loops when searching for information that might not be present on the page.
- **Tool Response Modification**: Adapting the tool response format to fit the Qwen2.5–7B-Instruct model’s chat template, which supports only system, user, and assistant roles.
By structuring the implementation with these considerations, the system achieves the capability to perform complex, multi-hop question-answering tasks efficiently, despite being powered by a relatively small model running on consumer-grade hardware
This article explores QwQ-32B-Preview, an experimental AI model by Qwen Team, which focuses on advancing AI reasoning capabilities. It discusses the model's performance, limitations, and its deep contemplative abilities on various benchmarks and problems.
Simon Willison reviews the new Qwen2.5-Coder-32B, an open-source LLM by Alibaba, which performs well on various coding benchmarks and can run on personal devices like his MacBook Pro M2.
