Add dataset card

README.md

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+---
+language: en
+license: mit
+task_categories:
+- conversational
+- question-answering
+tags:
+- function-calling
+- tool-use
+- synthetic
+- glm5
+- agentic
+size_categories:
+- 1K<n<10K
+---
+
+# GLM-5.3-Flash Function Calling (synthetic)
+
+A synthetic function-calling dataset generated with [zai-org/GLM-5.3-Flash](https://huggingface.co/zai-org/GLM-5.3-Flash) via Hugging Face Inference Providers. Every example is schema-validated and deduplicated before it is written.
+
+## Splits
+
+| File | Format | Rows |
+|---|---|---|
+| `data/messages.jsonl` | OpenAI messages (multi-turn) | — |
+| `data/xlam.jsonl` | xlam single-turn | — |
+
+_The table is filled in when the generation run completes._
+
+## `messages` format
+
+```json
+{
+  "id": "glm53fc-000123",
+  "messages": [
+    {"role": "system", "content": "..."},
+    {"role": "user", "content": "..."},
+    {"role": "assistant", "content": null, "tool_calls": [{"id": "call_1", "type": "function", "function": {"name": "...", "arguments": "{...}"}}]},
+    {"role": "tool", "tool_call_id": "call_1", "name": "...", "content": "{...}"},
+    {"role": "assistant", "content": "final natural-language answer"}
+  ],
+  "tools": [/* OpenAI function definitions used in the example */],
+  "domain": "travel",
+  "type": "tool" 
+}
+```
+
+Multi-call examples contain several entries in one assistant `tool_calls` list plus one `tool` reply per call.
+
+## `xlam` format
+
+```json
+{"id": "...", "query": "user request", "tools": [...], "answers": [{"name": "...", "arguments": {...}}]}
+```
+
+No-tool ("negative") examples have empty `answers` — they teach a model *not* to call a function.
+
+## Generation pipeline
+
+1. **Scenario** — GLM-5.3-Flash invents a domain scenario with 3–6 JSON-schema functions and a natural user query (16 domains, ~85% tool-using / 15% no-tool negatives).
+2. **Call** — the same model answers with tools attached (`tool_choice="required"` / `"auto"`), producing real OpenAI-style `tool_calls`.
+3. **Simulate** — the model fabricates plausible JSON tool results, then writes the final assistant answer with results in context.
+
+Arguments are validated against each function's JSON Schema (required keys present, types match, no extra keys); failures are dropped. Generation script: [`generate_function_calling.py`](./generate_function_calling.py). Resume-safe (append + dedup), so later runs extend the dataset in place.