Datasets:
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 8-case eval behind the tool-call pass rate in our model cards' model-index:
positive prompts where the model must emit a structured tool call, plus a no-tool
abstain case (it must answer directly, not hallucinate a call). Use it to measure
any local tool-calling model the same way we do — or to reproduce our numbers.
from datasets import load_dataset
cases = load_dataset("tpls/gemma4-coder-tool-eval", split="train") # each: {name, user, tools, expect}
Scoring a model
Serve a GGUF on llama.cpp (llama-server --jinja), send each case's user + tools
the normal OpenAI way, and check the reply against expect. Two rates, because
llama.cpp --jinja doesn't recognise gemma-4's native tool-call markup:
- raw — llama.cpp's native parse. For gemma-4 it structurally undercounts (blind to the format).
- shim — the same outputs re-parsed for the native markup. This is the number that reflects training.
The shim is a tiny serve-side post-processor — ready-to-use (drop-in litellm callback +
a standalone parser, Apache-2.0) at tpls/gemma4-tool-shim,
where the recovery algorithm is also documented so you can re-implement it.
# sketch: per case, POST to your OpenAI-compatible endpoint, then
# raw_ok = response had a structured tool_call (or correctly abstained)
# shim_ok = same, after running the reply through the shim parser
# pass_rate = mean(ok over the 8 cases). expect == [] means "must NOT call a tool".
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 |
Scope & license
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 lives on each model card). Pairs with the
tpls/gemma4-tool-shim helper and the models below.