| --- |
| license: mit |
| language: |
| - en |
| tags: |
| - function-calling |
| - tool-use |
| - synthetic |
| - ml-intern |
| size_categories: n<1K |
| --- |
| |
| [](https://hf.co/chat?mode=ml-intern) |
|
|
| # GLM-5.3-Flash Synthetic Function-Calling Dataset |
|
|
| 298 synthetic function-calling examples generated by [zai-org/GLM-5.3-Flash](https://huggingface.co/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): |
|
|
| 1. **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`. |
| 2. **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`](gen_fc_dataset.py). Run it with `HF_TOKEN=... python gen_fc_dataset.py` (`SMOKE=1` for a quick test). |
|
|
| ## Format |
|
|
| ```json |
| { |
| "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 |
|
|
| ```python |
| 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. |
|
|
