Introducing ToolsGen 🛠️
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Introducing ToolsGen 🛠️
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I built a tool to solve a problem I kept running into: creating quality datasets for training LLMs to use tools.
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I built a tool to solve a problem I kept running into: creating quality datasets for training LLMs to use tools.
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ToolsGen takes your JSON tool definitions and automatically generates realistic user requests, corresponding tool calls, and evaluates them using an LLM-as-a-judge pipeline. It outputs datasets ready to use with Hugging Face.
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ToolsGen takes your JSON tool definitions and automatically generates realistic user requests, corresponding tool calls, and evaluates them using an LLM-as-a-judge pipeline. It outputs datasets ready to use with Hugging Face.
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What makes it useful:
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What makes it useful:
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-  Generates realistic user requests + tool calls from JSON definitions
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-  Generates realistic user requests + tool calls from JSON definitions
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- LLM-as-a-judge quality scoring with multi-dimensional rubrics
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- LLM-as-a-judge quality scoring with multi-dimensional rubrics
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- Multiple sampling strategies (random, parameter-aware, semantic)
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- Multiple sampling strategies (random, parameter-aware, semantic)
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- OpenAI-compatible API support
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- OpenAI-compatible API support
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- Outputs JSONL with train/val splits
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- Outputs JSONL with train/val splits
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Still early days (API isn't stable yet), but it's already helping me generate tool-calling datasets much faster.
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Still early days (API isn't stable yet), but it's already helping me generate tool-calling datasets much faster.
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Check it out: 
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Check it out: 
https://github.com/atasoglu/toolsgen
https://github.com/atasoglu/toolsgen
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Happy to hear feedback or ideas!
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Happy to hear feedback or ideas!
Introducing ToolsGen 🛠️

I built a tool to solve a problem I kept running into: creating quality datasets for training LLMs to use tools.

ToolsGen takes your JSON tool definitions and automatically generates realistic user requests, corresponding tool calls, and evaluates them using an LLM-as-a-judge pipeline. It outputs datasets ready to use with Hugging Face.

What makes it useful:
-  Generates realistic user requests + tool calls from JSON definitions
- LLM-as-a-judge quality scoring with multi-dimensional rubrics
- Multiple sampling strategies (random, parameter-aware, semantic)
- OpenAI-compatible API support
- Outputs JSONL with train/val splits

Still early days (API isn't stable yet), but it's already helping me generate tool-calling datasets much faster.

Check it out: https://github.com/atasoglu/toolsgen

Happy to hear feedback or ideas!
👍
John6666 Danil CrazyChris24 eLkzemw