---
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](https://github.com/Happynood/quant-toolcall-bench),
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](https://github.com/Happynood/quant-toolcall-bench/blob/main/docs/RUN_REAL.md).
2. Verify your `result.json` contains a `manifest` block with git SHA and hashes.
3. Open a PR on [GitHub](https://github.com/Happynood/quant-toolcall-bench) 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