---
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](https://huggingface.co/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.