README.md

metadata

license: mit
language:
  - en
tags:
  - tool-use
  - function-calling
  - benchmark
  - quantization
  - leaderboard
pretty_name: QuantCall Results

QuantCall Results

Real benchmark results for the QuantCall benchmark, measuring how quantization degrades LLM function-calling reliability. Every row comes from an actual quantcall run execution — no fabricated or hand-edited numbers.

Files

File	Grain	Description
data/runs.csv	one row per real run (per seed)	Raw per-seed data with full manifest (git SHA, config/dataset hashes)
data/leaderboard.csv	one row per (model, quant, backend, decoding, tier)	Aggregated over seeds, with bootstrap 95% CIs and deltas vs an explicit baseline quant

Schema: data/runs.csv

Column	Type	Description
model	string	Model identifier (HF repo ID or local path)
quant	string	Quantization level: fp16, Q8_0, Q5_K_M, Q4_K_M, AWQ, GPTQ
backend	string	Inference backend: llama-cpp, transformers, vllm, openai
decoding	string	Decoding mode: free or constrained
tier	string	Dataset tier(s) evaluated, +-joined (e.g. T1+T6)
seed	int	Random seed for this run
sample_size	int	Number of instances evaluated per tier
svr	float	Schema-Validity Rate [0, 1]
tsa	float	Tool-Selection Accuracy [0, 1]
ac	float	Argument Correctness [0, 1]
abstention	float	Abstention Accuracy [0, 1]
fcr	float	Function-Calling Reliability — 0.25 × (SVR + TSA + AC + Abst)
vram_gb	float	Peak VRAM usage in GB for this run (empty if not measured)
git_commit	string	QuantCall repo commit SHA used for this run
config_sha256	string	SHA-256 of the run config
dataset_sha256	string	SHA-256 of the evaluation sample
timestamp	string	ISO-8601 UTC timestamp of the run

Schema: data/leaderboard.csv

Column	Type	Description
model	string	Model identifier
quant	string	Quantization level
backend	string	Inference backend
decoding	string	Decoding mode
tier	string	Dataset tier(s), +-joined
n_seeds	int	Number of seeds aggregated into this row
fcr_mean	float	Mean FCR across seeds
fcr_ci_low	float	Bootstrap 95% CI lower bound for FCR
fcr_ci_high	float	Bootstrap 95% CI upper bound for FCR
svr_mean	float	Mean SVR across seeds
tsa_mean	float	Mean TSA across seeds
ac_mean	float	Mean AC across seeds
abstention_mean	float	Mean Abstention across seeds
vram_gb	float	Mean peak VRAM in GB (empty if not measured)
eta	float	Efficiency: fcr_mean / vram_gb (empty if vram_gb is empty)
delta_fcr_rel	float	Relative FCR delta vs baseline_quant in the same scope; empty for the baseline row itself
delta_ac_rel	float	Relative AC delta vs baseline_quant
baseline_quant	string	The Δ reference quant for this scope — fp16 if it fits and was run, otherwise the best-available quant, always labeled explicitly here

These two schemas are generated by quantcall leaderboard <results_dir> (source of truth: src/quantcall/report/published.py, docs/RESULTS_SCHEMA.md in the repo) — this card is kept in sync with that code by a repo test (test_no_schema_drift).

How to Submit

1. Run the benchmark on your hardware following docs/RUN_REAL.md.
2. Verify your result.json contains a manifest block with git SHA and hashes.
3. Open a PR on GitHub adding your result file under results/.
4. Run quantcall leaderboard results/ --output-dir leaderboard/ and include the regenerated runs.csv / leaderboard.csv in your PR.

Links

• GitHub: https://github.com/Happynood/quant-toolcall-bench
• This dataset: https://huggingface.co/datasets/happynood/quantcall-results
• Eval suite: https://huggingface.co/datasets/happynood/quantcall-suite
• Leaderboard (Space): https://huggingface.co/spaces/happynood/quantcall-leaderboard