dataset card (gate suite)

README.md

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+---
+license: apache-2.0
+task_categories:
+  - text-generation
+tags:
+  - function-calling
+  - tool-use
+  - evaluation
+  - gemma4
+pretty_name: Gemma-4 Coder — tool-calling gate suite
+configs:
+  - config_name: default
+    data_files: tool_eval_cases.jsonl
+---
+
+# Gemma-4 Coder — tool-calling gate suite
+
+The **evaluation suite** behind the `tool-call pass rate` reported in our model cards'
+`model-index`. 8 cases: positive tool-use prompts (the model must emit a structured
+tool call) plus a no-tool **abstain** case (it must answer directly, not hallucinate a call).
+
+Authored by us — fully permissive (apache-2.0). This is an *eval* set, not training data;
+our training mix is a derivative of public datasets and is **not** redistributed (its
+reproducible recipe — sources, pinned revisions, weights — is on each model card).
+
+## Files
+
+| File | What |
+|------|------|
+| `tool_eval_cases.jsonl` | one case/line: `{name, user, tools, expect}` |
+| `tool_calib.txt` | imatrix calibration text (code + tool-call markup) used when quantizing |
+
+## How it scores
+
+Serve a GGUF on `llama.cpp` (`llama-server --jinja`), send each case, and check whether a
+structured tool call was emitted (or correctly abstained). Two rates are reported:
+**raw** = llama.cpp's native parse; **shim** = the same output re-parsed for gemma-4's
+native markup (llama.cpp's `--jinja` doesn't recognise that format, so raw undercounts).
+
+```python
+from datasets import load_dataset
+cases = load_dataset("tpls/gemma4-coder-tool-eval", split="train")
+```
+
+## Used by
+
+- [`tpls/gemma-4-12B-coder-fable5-composer2.5-v1-sft-v5`](https://huggingface.co/tpls/gemma-4-12B-coder-fable5-composer2.5-v1-sft-v5)
+- [`tpls/gemma-4-12B-coder-fable5-composer2.5-v1-sft-v5-GGUF`](https://huggingface.co/tpls/gemma-4-12B-coder-fable5-composer2.5-v1-sft-v5-GGUF)