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YAML Metadata Warning:The task_categories "conversational" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other

Synthetic function-calling dataset (generated with GLM-5.3-Flash)

Synthetic multi-turn function-calling conversations generated with zai-org/GLM-5.3-Flash via Hugging Face Inference Providers. Generated by generate.py in this repository.

Format

Each row is one conversation:

tools: list of OpenAI-format tool definitions ({"type": "function", "function": {name, description, parameters (JSON Schema)}}), 2-7 per conversation.
messages: OpenAI-style conversation with roles system, user, assistant (with tool_calls), and tool (with tool_call_id, name, and a simulated JSON result string).
domain: scenario domain (calendar, e-commerce, finance, DevOps, travel, ...).
scenario: one-line scenario description.
generator, seed, n_tool_rounds: provenance fields.

Tool results are simulated by the model (plausible JSON payloads), not executed against real APIs — arguments and results are realistic but fictional.

Pipeline

A "designer" call to GLM-5.3-Flash invents a scenario, the tool set, and the opening user request for a sampled domain.
A tool-calling loop runs the assistant against those tools (tool_choice="auto"); every tool call is answered by a simulated JSON return value, for up to 4 rounds.
Records are schema-validated (tool names must exist, arguments must parse as objects, tool messages must follow their calls) and deduplicated, then pushed here.

Usage

from datasets import load_dataset
ds = load_dataset("Nivo54775/glm-5.3-flash-function-calling", split="train")

License

MIT (matching the generator model's license). Fictional data; do not treat any payload as real API output.

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