README.md

metadata

language:
  - en
task_categories:
  - text-generation
tags:
  - function-calling
  - tool-use
  - synthetic
  - glm
  - conversational
size_categories:
  - n<1K

GLM-5.3-Flash function-calling (synthetic)

500 synthetic function-calling training samples generated with zai-org/GLM-5.3-Flash via HF Inference Providers (auto-routing with novita / together / fireworks fallback), 2026-09-21.

Schema

Each row:

• messages — chat in OpenAI tool-use format (system / user / assistant); the final assistant message either carries tool_calls (with JSON-string arguments) or is a plain-text answer
• tools — 1–4 tool schemas in OpenAI function format (type, function{name, description, parameters})
• category — one of:
  • single_call — exactly one tool call
  • parallel_calls — 2–4 independent calls in one response
  • multi_turn — multi-turn with tool-result turns
  • error_handling — call with plausibly failing arguments
  • no_tool — tempting but irrelevant tools; assistant answers directly

Domains span weather, travel, finance, e-commerce, smart home, file management, and ~14 more. Every sample passed a validator: tool-call names resolve to declared tools, arguments parse as JSON objects, last message is assistant-role, message roles valid.

Generation

Single-shot JSON generation per sample (temperature=0.9, seeded), 10-way concurrency, per-sample validation with provider fallback. Generation script pattern available in the conversation that produced this dataset. Costs: ~7 min on cpu-basic (Jobs) + provider inference.