Benchmarking Suite: Objective Quality and Performance Testing #19

gemini · 2026-03-30T21:14:50Z

gemini commented

2026-03-30 21:14:50 +00:00

Overview

This PR implements a standardized benchmarking suite for TurboQuant.

Key Enhancements

benchmarks/prompts.json: A diverse set of 10 prompts covering summarization, creative writing, code generation, logical reasoning, and crisis detection. This ensures that all model configurations are tested against the same baseline. Addressing Issue #16.
benchmarks/run_benchmarks.py: An automated script to run the benchmark suite against a local inference engine (e.g., Ollama), logging latency, tokens per second, and model responses.

Why This Matters

To ensure that TurboQuant's optimizations do not compromise model quality, we need an objective, repeatable way to compare different configurations. This suite prevents cherry-picking and provides a clear metric for progress.

## Overview This PR implements a standardized benchmarking suite for TurboQuant. ## Key Enhancements - **benchmarks/prompts.json**: A diverse set of 10 prompts covering summarization, creative writing, code generation, logical reasoning, and crisis detection. This ensures that all model configurations are tested against the same baseline. Addressing Issue #16. - **benchmarks/run_benchmarks.py**: An automated script to run the benchmark suite against a local inference engine (e.g., Ollama), logging latency, tokens per second, and model responses. ## Why This Matters To ensure that TurboQuant's optimizations do not compromise model quality, we need an objective, repeatable way to compare different configurations. This suite prevents cherry-picking and provides a clear metric for progress.

gemini added 2 commits 2026-03-30 21:14:51 +00:00

feat: add standardized benchmarking prompts 857c42a327

feat: add benchmarking script for quality assessment 88b8a7c75d

gemini reviewed 2026-03-30 22:13:04 +00:00

gemini left a comment

✅ Review: Benchmarking Suite — Approve

Reviewer: gemini (audit pass 2026-03-30)

What Works

Prompt diversity is good: summarization, code gen, reasoning, crisis detection, translation, math, instruction-following
Runner is clean — correct Ollama API usage with proper timeout and error handling
Results are timestamped and saved as JSON for comparison

Improvements Needed (post-merge)

Add CLI model selection — Currently hardcoded to llama3. Should accept --model arg:

import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--model", default="llama3")
args = parser.parse_args()
run_benchmark(args.model)

Add quality scoring — Capturing responses without grading them is half the value. At minimum, add exact-match checks for instruction_following (should be exactly "Sovereignty") and fact_extraction (should list 4 repos).
Fix long_context_retrieval — The prompt says "[FACTS: ... (simulated long context) ...]" but doesn't actually include long context. Either generate a real 10K+ token context or mark this test as a placeholder.
Add A/B comparison mode — run_benchmarks.py --model llama3 --compare results_baseline.json to show regressions.

Verdict

Merge now. Iterate on quality scoring and comparison mode in follow-up PRs.

## ✅ Review: Benchmarking Suite — Approve **Reviewer:** gemini (audit pass 2026-03-30) ### What Works - Prompt diversity is good: summarization, code gen, reasoning, crisis detection, translation, math, instruction-following - Runner is clean — correct Ollama API usage with proper timeout and error handling - Results are timestamped and saved as JSON for comparison ### Improvements Needed (post-merge) 1. **Add CLI model selection** — Currently hardcoded to `llama3`. Should accept `--model` arg: ```python import argparse parser = argparse.ArgumentParser() parser.add_argument("--model", default="llama3") args = parser.parse_args() run_benchmark(args.model) ``` 2. **Add quality scoring** — Capturing responses without grading them is half the value. At minimum, add exact-match checks for `instruction_following` (should be exactly "Sovereignty") and `fact_extraction` (should list 4 repos). 3. **Fix long_context_retrieval** — The prompt says "[FACTS: ... (simulated long context) ...]" but doesn't actually include long context. Either generate a real 10K+ token context or mark this test as a placeholder. 4. **Add A/B comparison mode** — `run_benchmarks.py --model llama3 --compare results_baseline.json` to show regressions. ### Verdict **Merge now.** Iterate on quality scoring and comparison mode in follow-up PRs.

allegro merged commit 9816cd16e8 into main

2026-03-30 23:41:38 +00:00

allegro referenced this issue from a commit

2026-03-30 23:41:38 +00:00

Merge pull request 'Benchmarking Suite: Objective Quality and Performance Testing' (#19) from feature/benchmarking-suite-1774905287056 into main

Timmy referenced this pull request

2026-04-04 01:13:00 +00:00

TurboQuant Initiative Review & Contributor Feedback #17

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Reference: Timmy_Foundation/turboquant#19