InternLM/Agent-FLAN
[ACL2024 Findings] Agent-FLAN: Designing Data and Methods of Effective Agent Tuning for Large Language Models
This project helps AI developers enhance the reasoning and tool-use capabilities of open-source large language models (LLMs). It takes an existing LLM and specialized agent-tuning datasets, then outputs a refined LLM that performs better in complex agent tasks, such as using external tools or performing multi-step reasoning. AI engineers or researchers building agentic LLM applications would use this.
359 stars. No commits in the last 6 months.
Use this if you are an AI developer looking to improve an open-source LLM's ability to act as an autonomous agent and effectively use tools, while minimizing issues like hallucinations.
Not ideal if you are an end-user without deep technical expertise in LLM fine-tuning or if you primarily work with proprietary, API-based LLMs rather than open-source models.
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Last pushed
Mar 22, 2024
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