Agentlm 7b

🚀 AgentLM-7B

AgentLM-7B is a language model produced by mixed training on AgentInstruct dataset and ShareGPT dataset from Llama-2-chat models. It enables agent capabilities of LLMs with robust generalization on unseen agent tasks while maintaining good general language abilities.

🚀 Quick Start

AgentTuning is the first attempt to instruction - tune large language models (LLMs) using interaction trajectories across multiple agent tasks. The evaluation results show that AgentTuning endows LLMs with agent capabilities, which can generalize well on unseen agent tasks while still performing well in general language abilities. We have open - sourced the AgentInstruct dataset and AgentLM.

You can access the following links for more information:

• 🤗 Dataset
• 💻 Github Repo
• 📌 Project Page
• 📃 Paper

✨ Features

• Agent Capabilities: AgentTuning enables LLMs to handle various agent tasks with good generalization.
• General Language Abilities: Maintains high - quality performance in general language understanding and generation.

📚 Documentation

Models

AgentLM models are trained by mixing the AgentInstruct dataset and the ShareGPT dataset based on Llama - 2 - chat models.

The models follow the conversation format of [Llama - 2 - chat](https://huggingface.co/blog/llama2#how - to - prompt - llama - 2), with the system prompt fixed as:

You are a helpful, respectful and honest assistant.

The 7B, 13B, and 70B models are available on the Huggingface model hub.

📄 License

Citation

If you find our work useful, please consider citing AgentTuning:

@misc{zeng2023agenttuning,
      title={AgentTuning: Enabling Generalized Agent Abilities for LLMs}, 
      author={Aohan Zeng and Mingdao Liu and Rui Lu and Bowen Wang and Xiao Liu and Yuxiao Dong and Jie Tang},
      year={2023},
      eprint={2310.12823},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}