File size: 1,642 Bytes
d9dac1e c75d52d d9dac1e c75d52d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 | ---
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. |