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happynood
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quantmcp-results

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Tags:
tool-use
function-calling
mcp
model-context-protocol
benchmark
quantization
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The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
timestamp: string
git_commit: string
git_dirty: bool
config_sha256: string
fixture_sha256: string
model: string
backend: string
quant: string
decoding: string
server_tier: string
mcp_server_version: string
seed: int64
python_version: string
platform_info: string
cpu_model: string
cpu_count: int64
gpu: struct<name: string, driver_version: string, cuda_version: string, vram_total_mb: int64, torch_cuda_ (... 46 chars omitted)
  child 0, name: string
  child 1, driver_version: string
  child 2, cuda_version: string
  child 3, vram_total_mb: int64
  child 4, torch_cuda_available: null
  child 5, torch_cuda_device_name: null
rho: double
n_pairs: int64
table: list<item: struct<model: string, quant: string, baseline_quant: string, delta_svr_bfcl: double, delt (... 19 chars omitted)
  child 0, item: struct<model: string, quant: string, baseline_quant: string, delta_svr_bfcl: double, delta_svr_mcp:  (... 7 chars omitted)
      child 0, model: string
      child 1, quant: string
      child 2, baseline_quant: string
      child 3, delta_svr_bfcl: double
      child 4, delta_svr_mcp: double
to
{'rho': Value('float64'), 'n_pairs': Value('int64'), 'table': List({'model': Value('string'), 'quant': Value('string'), 'baseline_quant': Value('string'), 'delta_svr_bfcl': Value('float64'), 'delta_svr_mcp': Value('float64')})}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 147, in get_rows_or_raise
                  return get_rows(
                      dataset=dataset,
                  ...<4 lines>...
                      column_names=column_names,
                  )
                File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
                  return func(*args, **kwargs)
                File "/src/services/worker/src/worker/utils.py", line 127, in get_rows
                  rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
                File "/src/services/worker/src/worker/utils.py", line 478, in safe_iter
                  yield from ds.decode(False) if ds.features else ds
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2818, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2355, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2380, in _iter_arrow
                  for key, pa_table in self.ex_iterable._iter_arrow():
                                       ~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
                  self._cast_table(pa_table, json_field_paths=json_field_paths),
                  ~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
                  pa_table = table_cast(pa_table, self.info.features.arrow_schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2369, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2297, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              timestamp: string
              git_commit: string
              git_dirty: bool
              config_sha256: string
              fixture_sha256: string
              model: string
              backend: string
              quant: string
              decoding: string
              server_tier: string
              mcp_server_version: string
              seed: int64
              python_version: string
              platform_info: string
              cpu_model: string
              cpu_count: int64
              gpu: struct<name: string, driver_version: string, cuda_version: string, vram_total_mb: int64, torch_cuda_ (... 46 chars omitted)
                child 0, name: string
                child 1, driver_version: string
                child 2, cuda_version: string
                child 3, vram_total_mb: int64
                child 4, torch_cuda_available: null
                child 5, torch_cuda_device_name: null
              rho: double
              n_pairs: int64
              table: list<item: struct<model: string, quant: string, baseline_quant: string, delta_svr_bfcl: double, delt (... 19 chars omitted)
                child 0, item: struct<model: string, quant: string, baseline_quant: string, delta_svr_bfcl: double, delta_svr_mcp:  (... 7 chars omitted)
                    child 0, model: string
                    child 1, quant: string
                    child 2, baseline_quant: string
                    child 3, delta_svr_bfcl: double
                    child 4, delta_svr_mcp: double
              to
              {'rho': Value('float64'), 'n_pairs': Value('int64'), 'table': List({'model': Value('string'), 'quant': Value('string'), 'baseline_quant': Value('string'), 'delta_svr_bfcl': Value('float64'), 'delta_svr_mcp': Value('float64')})}
              because column names don't match

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QuantMCP Results

Real benchmark results for the QuantMCP benchmark, measuring how quantization degrades LLM function-calling reliability against real, unmodified Model Context Protocol server tool schemas — and whether that degradation matches what QuantCall already measured on curated BFCL/ToolACE schemas. Every row comes from an actual quantmcp run execution against a real, sandboxed MCP server — no fabricated or hand-edited numbers.

Headline finding: Cross-Benchmark Consistency (CBC)

CBC (Spearman rho) = -0.755 (n=8 model×quant pairs, 3 model families) — see data/cbc.json.

QuantCall's BFCL-measured quantization degradation does not reliably carry over to real MCP schemas: the correlation is negative (degradation directions often flip), and it got more negative, not less, as more data was added. With 2 model families (Qwen3-0.6B, Llama-3.2-1B) the estimate was -0.551 (n=6) — itself the product of three increasingly-averaged computations that ranged -0.824 to -0.265 before stabilizing. Adding Qwen3-1.7B as a 3rd family (a real within-family size contrast against Qwen3-0.6B) moved it to -0.755 (n=8): the sign held, the magnitude strengthened. See docs/RUN_REAL.md in the GitHub repo for the full convergence table and honesty caveats — n=8 is still far too few for a rigorous p-value on a Spearman correlation.

Files

File Grain Description
data/raw_results/**/*.result.json + *.manifest.json one pair per real run Every real run this project has produced (96 result files across 3 model families × 4 server tiers, with varying quant/repeat coverage per family — Qwen3-0.6B and Llama-3.2-1B at 4 quant levels with 3 independent repeats on tiers U1-U3, Qwen3-1.7B at 3 quant levels, single run, no fp16 (its bf16 weights don't fit a 4GB card at a usable context length — see docs/RUN_REAL.md)), each with a full manifest (git SHA, config/fixture hashes, hardware fingerprint). The model field is a portable ~/models/... path, not a specific machine's absolute path. Since Phase 7, each file also carries a instances array (one entry per task instance, tagged with the tool it targeted) enabling the per-tool SCI regression below.
data/mcp_runs.csv one row per real run Flattened, path-sanitized view of every raw_results file
data/mcp_tier_breakdown.csv one row per server tier Mean SVR-MCP/TSR/η per tier, annotated with that tier's real Schema Complexity Index (SCI)
data/cbc.json one row per (model, quant) pair Cross-Benchmark Consistency deltas against QuantCall's published BFCL numbers, plus the Spearman rho itself
data/sci_regression.json one row per live tool Per-tool Schema Complexity Index (SCI) paired with its own Δ SVR-MCP (fp16 vs. Q4_K_M), plus an OLS slope and bootstrap 95% CI across all 38 covered tools — the statistically-powered version of the SCI-vs-degradation question that mcp_tier_breakdown.csv's 4-tier view alone can't answer

Schema: data/mcp_runs.csv

Column Type Description
model string Sanitized model name (local GGUF paths stripped to a canonical name — see report/published.py::sanitize_model_name in the GitHub repo)
quant string Quantization level: fp16, Q8_0, Q5_K_M, Q4_K_M (Qwen3-1.7B: Q8_0/Q5_K_M/Q4_K_M only)
tier string MCP server tier: filesystem, git, sqlite, or memory
n int Number of task instances evaluated
svr_mcp float Schema-Validity Rate against the real, live tool schema (SVR-MCP, spec §4.1)
tsr float Task Success Rate — the call was schema-valid and produced the correct outcome (spec §4.2)
vram_gb float Peak VRAM usage in GB for this run
eta float Reliability-per-VRAM: (0.5*svr_mcp + 0.5*tsr) / vram_gb
pareto_optimal bool Whether this (model, quant, tier) config sits on the reliability-vs-VRAM Pareto frontier

Schema: data/cbc.json 

{
  "rho": -0.755,
  "n_pairs": 8,
  "table": [
    {"model": "...", "quant": "...", "baseline_quant": "...", "delta_svr_bfcl": ..., "delta_svr_mcp": ...}
  ]
}

delta_svr_bfcl is QuantCall's published BFCL SVR delta vs. that model's own baseline quant (fp16 for every family except Qwen3-1.7B, which uses Q8_0 — see baseline_quant per row); delta_svr_mcp is this project's equivalent delta on real MCP schemas (pooled across all four server tiers, weighted by task count).

Schema: data/sci_regression.json 

{
  "n": 38,
  "slope": 0.045,
  "intercept": ...,
  "slope_ci": [-0.064, 0.170],
  "points": [
    {"tool": "...", "tier": "...", "sci": ..., "delta_svr": ..., "n_baseline": ..., "n_quant": ...}
  ]
}

slope/slope_ci describe the OLS fit of Δ SVR-MCP (fp16 minus Q4_K_M pass rate, pooled across model families weighted by n) against each live tool's own SCI. The 95% CI is a percentile bootstrap over the (SCI, Δ) pairs, not a parametric estimate.

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 a git SHA and fixture hash.
  3. Open a PR on GitHub adding your result file under results/.
  4. Run quantmcp leaderboard results/ --output-dir leaderboard/ and include the regenerated CSVs in your PR.

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