fix(benchmarks): count unfenced code, ASCII-safe output, refresh llama3.2 results (#67)
Fixes the local benchmark LOC counter (counted only fenced code, scored bare output 0), makes summary output ASCII-safe (a Unicode arrow crashed the script on Windows cp1252), gitignores generated artifacts, and refreshes the llama3.2 writeup with n=5 data showing the LOC effect is within the noise floor. Follow-up to #63. Verified live. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
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DietrichGebert
co-authored by
Claude Opus 4.8
Claude Opus 4.8
parent
386f95734a
commit
2e6a93765a
@@ -37,9 +37,10 @@ def load_arms():
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def count_loc(text):
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"""Non-blank, non-comment lines inside fenced code blocks."""
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"""Non-blank, non-comment lines of code: fenced blocks, or the whole
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response when the model emitted bare code with no fence."""
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blocks = re.findall(r"```[a-zA-Z0-9_+\-]*\n([\s\S]*?)```", text)
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lines = "\n".join(blocks).splitlines()
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lines = ("\n".join(blocks) if blocks else text).splitlines()
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return sum(
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1 for l in lines
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if l.strip()
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@@ -110,7 +111,7 @@ def run(model, repeat, ollama_url):
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sep = "-" * len(header)
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print(f"\n{'=' * 60}")
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print(f" RESULTS — {model} (n={repeat}, median)")
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print(f" RESULTS - {model} (n={repeat}, median)")
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print(f"{'=' * 60}")
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print(f"\nCode LOC per task (median)")
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@@ -139,7 +140,7 @@ def run(model, repeat, ollama_url):
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out = Path(__file__).parent / "benchmark-local-results.json"
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out.write_text(json.dumps(results, indent=2), encoding="utf-8")
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print(f"\nFull responses → {out}")
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print(f"\nFull responses -> {out}")
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def main():
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+3
-2
@@ -1,9 +1,10 @@
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// Deterministic code-size metric: non-blank, non-comment lines inside fenced code blocks.
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// Deterministic code-size metric: non-blank, non-comment lines of code. Counts
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// fenced blocks, or the whole response when the model emitted bare code unfenced.
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// Recorded as the `code_loc` metric per arm (always passes; it is a measurement, not a gate).
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module.exports = (output) => {
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const text = String(output || '');
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const blocks = [...text.matchAll(/```[a-zA-Z0-9_+-]*\n([\s\S]*?)```/g)].map((m) => m[1]);
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const code = blocks.join('\n');
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const code = blocks.length ? blocks.join('\n') : text;
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const loc = code
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.split('\n')
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.map((l) => l.trim())
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@@ -2,48 +2,52 @@
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Same 5 tasks as the Claude benchmark, same three arms (baseline / caveman / ponytail),
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run against a local **llama3.2:latest** (3.2B, Q4_K_M) via Ollama on a Windows 11 machine.
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n=1 per cell. Tooling: `benchmarks/benchmark-local.py` (no promptfoo needed).
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Tooling: `benchmarks/benchmark-local.py` (no promptfoo needed).
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## Results
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> **Updated 2026-06-15:** the LOC counter now counts bare, unfenced code. It
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> previously counted only fenced code blocks and scored everything else as 0,
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> which silently deflated any arm whose output happened to skip the fences (small
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> models do this often). Numbers below use the corrected counter at n=5 median.
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> Absolute times reflect this machine (GPU-accelerated); compare arms within a
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> run, not against an earlier CPU-bound machine.
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## Results (n=5, median)
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**Code LOC**
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| arm | email | debounce | csv-sum | countdown | rate-limit | **TOTAL** |
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|---|--:|--:|--:|--:|--:|--:|
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| baseline | 13 | 13 | 38 | 44 | 32 | **140** |
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| caveman | 16 | 12 | 5 | 45 | 28 | **106** |
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| ponytail | 18 | 21 | 7 | 38 | 49 | **133** |
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| baseline | 16 | 18 | 22 | 37 | 16 | **109** |
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| caveman | 16 | 21 | 18 | 46 | 32 | **133** |
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| ponytail | 17 | 22 | 18 | 52 | 28 | **137** |
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**Time (seconds)**
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| arm | email | debounce | csv-sum | countdown | rate-limit | **TOTAL** |
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|---|--:|--:|--:|--:|--:|--:|
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| baseline | 52.6 | 37.2 | 63.4 | 65.2 | 71.0 | **289.4** |
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| caveman | 79.6 | 54.4 | 28.3 | 71.4 | 61.0 | **294.7** |
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| ponytail | 99.7 | 71.0 | 25.4 | 74.8 | 97.3 | **368.2** |
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**LOC vs baseline**
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| arm | total LOC | vs baseline |
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|---|--:|---|
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| caveman | 106 | −24% |
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| ponytail | 133 | −5% |
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| baseline | 3.1 | 3.7 | 3.6 | 4.2 | 4.8 | **19.4** |
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| caveman | 4.1 | 4.2 | 3.6 | 4.4 | 4.8 | **21.1** |
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| ponytail | 4.1 | 4.2 | 3.8 | 4.8 | 4.9 | **21.8** |
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## Key findings
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**Ponytail does not transfer to llama3.2.** On 3 of 5 tasks (email: 13→18, debounce: 13→21,
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rate-limit: 32→49) ponytail produced *more* code than the no-skill baseline. Total LOC
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reduction was −5% vs the 80–94% seen on Claude. Response time increased by +27% (368s vs
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289s) rather than the 3–6× speedup seen on Claude.
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**On llama3.2 the LOC effect is inside the noise floor.** At temperature 0.7 the
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per-run totals swing hard: across the five runs, ponytail landed anywhere from
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17% *below* baseline to 50% *above* it. The n=5 median came out +26%; a separate
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n=3 median came out −17%. The aggregate itself flips sign depending on the
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sample, and the countdown task alone ranged 19 to 74 LOC on baseline. There is no
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stable LOC reduction to report.
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**Caveman outperformed ponytail on this model** (−24% LOC, similar time to baseline).
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Caveman's rules are simpler prose instructions that a small model can follow more reliably;
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ponytail's multi-step decision ladder requires a stronger instruction-follower.
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**Ponytail does not transfer to llama3.2.** The 80-94% LOC reduction seen on
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Claude is simply absent: the signal is lost in run-to-run variance. The one
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consistent effect is on time, and it goes the wrong way: ponytail is ~10-15%
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*slower* than baseline (more system-prompt tokens to process), never the 3-6x
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speedup seen on Claude.
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**Why this happens:** Ponytail is a prompt-engineering skill calibrated on Claude models,
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which are specifically trained to follow detailed system instructions. A 3.2B quantised model
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partially absorbs the ponytail rules and then adds extra prose *justifying* its choices —
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paying the complexity cost without getting the minimalism benefit.
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**Why:** ponytail is a prompt-engineering skill calibrated on Claude models,
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which are trained to follow detailed system instructions. A 3.2B quantised model
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absorbs the rules only partially and adds prose justifying its choices, paying
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the instruction-following cost without reliably converting it into less code.
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## Reproduce
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@@ -51,9 +55,12 @@ Install Ollama and pull a model, then run from the repo root:
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```bash
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ollama pull llama3.2
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python benchmarks/benchmark-local.py --model llama3.2
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python benchmarks/benchmark-local.py --model llama3.2 --repeat 5
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```
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At this model size the LOC signal is noisy; raise `--repeat` (or lower the
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sampling temperature in the script) before reading anything into the totals.
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Optional flags:
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```
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@@ -63,6 +70,7 @@ Optional flags:
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## Takeaway
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The benchmark claims in the README are accurate for the models tested (Haiku, Sonnet, Opus).
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For local/small models, expect significantly smaller — or even negative — gains until
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instruction-following capability reaches a threshold comparable to Claude Haiku or better.
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The benchmark claims in the README are accurate for the models tested (Haiku,
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Sonnet, Opus). For local/small models, expect the gains to shrink into the noise
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until instruction-following reaches a threshold comparable to Claude Haiku or
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better.
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