Revert commit for build_prompt
Browse files
README.md
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@@ -21,7 +21,7 @@ extra_gated_fields:
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Affiliation: text
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---
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-
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<p align="center">
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<img width="500px" alt="xLAM" src="https://huggingface.co/datasets/jianguozhang/logos/resolve/main/xlam-no-background.png">
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</p>
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@@ -199,29 +199,11 @@ def build_conversation_history_prompt(conversation_history: str):
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# Helper function to build the input prompt for our model
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def build_prompt(task_instruction: str, format_instruction: str, tools: list, query: str
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prompt = f"[BEGIN OF TASK INSTRUCTION]
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{
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[END OF
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"
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prompt += f"[BEGIN OF AVAILABLE TOOLS]
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{json.dumps(xlam_format_tools)}
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[END OF AVAILABLE TOOLS]
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"
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prompt += f"[BEGIN OF FORMAT INSTRUCTION]
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{format_instruction}
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[END OF FORMAT INSTRUCTION]
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"
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prompt += f"[BEGIN OF QUERY]
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{query}
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[END OF QUERY]
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"
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if len(conversation_history) > 0: prompt += build_conversation_history_prompt(conversation_history)
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return prompt
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# Build the input and start the inference
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@@ -492,6 +474,15 @@ If you find this repo helpful, please consider to cite our papers:
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}
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```
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```bibtex
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@article{zhang2024agentohana,
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title={AgentOhana: Design Unified Data and Training Pipeline for Effective Agent Learning},
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journal={arXiv preprint arXiv:2402.15506},
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year={2024}
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}
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```
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```
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Affiliation: text
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---
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+
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<p align="center">
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<img width="500px" alt="xLAM" src="https://huggingface.co/datasets/jianguozhang/logos/resolve/main/xlam-no-background.png">
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</p>
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# Helper function to build the input prompt for our model
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def build_prompt(task_instruction: str, format_instruction: str, tools: list, query: str):
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prompt = f"[BEGIN OF TASK INSTRUCTION]\n{task_instruction}\n[END OF TASK INSTRUCTION]\n\n"
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prompt += f"[BEGIN OF AVAILABLE TOOLS]\n{json.dumps(xlam_format_tools)}\n[END OF AVAILABLE TOOLS]\n\n"
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prompt += f"[BEGIN OF FORMAT INSTRUCTION]\n{format_instruction}\n[END OF FORMAT INSTRUCTION]\n\n"
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prompt += f"[BEGIN OF QUERY]\n{query}\n[END OF QUERY]\n\n"
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return prompt
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# Build the input and start the inference
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}
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```
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```bibtex
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@article{zhang2025actionstudio,
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title={ActionStudio: A Lightweight Framework for Data and Training of Action Models},
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author={Zhang, Jianguo and Hoang, Thai and Zhu, Ming and Liu, Zuxin and Wang, Shiyu and Awalgaonkar, Tulika and Prabhakar, Akshara and Chen, Haolin and Yao, Weiran and Liu, Zhiwei and others},
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journal={arXiv preprint arXiv:2503.22673},
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year={2025}
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}
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```
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```bibtex
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@article{zhang2024agentohana,
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title={AgentOhana: Design Unified Data and Training Pipeline for Effective Agent Learning},
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journal={arXiv preprint arXiv:2402.15506},
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year={2024}
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}
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```
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