license: mit
language:
- en
tags:
- function-calling
- tool-use
- synthetic
- ml-intern
size_categories: n<1K
GLM-5.3-Flash Synthetic Function-Calling Dataset
298 synthetic function-calling examples generated by zai-org/GLM-5.3-Flash via Hugging Face Inference Providers, in OpenAI chat format (messages + tools, assistant turns use tool_calls). Ready for SFT with TRL / transformers chat templates.
Composition
| kind | count | meaning |
|---|---|---|
single |
175 | user request fulfilled by exactly one tool call |
parallel |
73 | request requires 2+ tool calls |
no_tool |
50 | no relevant tool exists; assistant politely declines |
Examples span 25 domains (calendar, email, weather, maps, smart home, finance, travel, e-commerce, git, databases, CRM, HR, healthcare, IoT, …). Each record carries its domain and kind for filtering.
How it was generated
Two-stage, fully synthetic (no human-written tools or examples):
- Tool design — for each domain, the model designed 4 tools in OpenAI function format (name, description, JSON-schema parameters). Schemas were checked with
jsonschema.Draft7Validator.check_schema. - Example writing — for each domain × kind, the model wrote user requests plus tool calls, seeded with a random persona hint for diversity.
Every example was then programmatically validated: JSON parses, called function names exist in the record's tools, and arguments validate against the tool's JSON schema. Invalid items were dropped (2 of 300 target). Near-duplicate user requests were removed.
The generator script is included in this repo: gen_fc_dataset.py. Run it with HF_TOKEN=... python gen_fc_dataset.py (SMOKE=1 for a quick test).
Format
{
"messages": [
{"role": "system", "content": "You are a helpful assistant with access to functions. ..."},
{"role": "user", "content": "Email the three caterers about Friday's lunch..."},
{"role": "assistant", "content": null, "tool_calls": [
{"id": "call_1a2b3c4d", "type": "function",
"function": {"name": "send_email", "arguments": "{\"to\": [...], ...}"}}
]}
],
"tools": [{"type": "function", "function": {"name": "send_email", "description": "...", "parameters": {...}}}],
"domain": "email management",
"kind": "single"
}
Note: tool_calls[].function.arguments is a JSON-encoded string (OpenAI convention); no_tool records have a plain assistant content reply and no tool_calls.
Usage
from datasets import load_dataset
ds = load_dataset("FranckAbgrall/glm53-flash-function-calling", split="train")
single_only = ds.filter(lambda r: r["kind"] == "single")
Limitations
- Fully synthetic: tool schemas and argument values are plausible but not tied to real APIs; dates center on October 2026 (generation time).
- English only; ~300 rows is a pipeline-validation / small-SFT scale, not a full training corpus.
- Generated with temperature 1.0 for diversity — expect occasional stylistic quirks; validation guarantees schema correctness, not real-world executability.