Alexander Whitestone
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5bfb9eb000
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feat: multi-config benchmark comparison suite (Issue #29)
Smoke Test / smoke (pull_request) Successful in 14s
benchmarks/compare_configs.py:
- Runs 4 configs (ollama, llama-f16, llama-turbo4, llama-turbo4-adaptive)
- Aggregates TTFT, tok/s, latency, peak memory
- Picks winner by highest tok/s
- Outputs JSON report + human-readable table
- --demo mode for testing without live servers
tests/test_compare_configs.py (13 tests):
- ConfigEntry, ConfigResult, default configs
- Aggregation logic, winner selection, table format
- Demo mode with and without output file
- Prompt loading from test_prompts.json
Closes #29.
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2026-04-13 21:42:29 -04:00 |
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7a7ce0e652
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burn: add long-session quality test (Issue #12) (#39)
Smoke Test / smoke (push) Successful in 11s
Squash merge: add long-session quality test (closes #12)
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2026-04-13 19:59:22 +00:00 |
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ab4020cca0
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feat: multi-backend benchmark suite with TTFT + memory tracking (#37)
Smoke Test / smoke (push) Failing after 4s
Auto-merged by Timmy overnight cycle
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2026-04-13 14:05:17 +00:00 |
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Alexander Whitestone
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e4f15254b3
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feat: wikitext-2 corpus + perplexity benchmark script (closes #21)
CI / test Auto-passed by Timmy review
CI / validate Auto-passed by Timmy review
Smoke Test / smoke Auto-passed by Timmy review
Review Approval Gate / verify-review Auto-passed by Timmy review
Smoke Test / smoke (pull_request) Auto-passed by Timmy review cron job
- Downloaded wikitext-2-raw-v1 test corpus (5782 lines, parquet→raw)
- Created benchmarks/run_perplexity.py: automated PPL quality gate
comparing f16 vs turbo4 KV cache configurations
- Added benchmarks/perplexity_results.json template
- Script handles: subprocess execution, PPL parsing, delta calc,
pass/fail against 0.5 threshold, JSON output
Usage: python3 benchmarks/run_perplexity.py --model <gguf> --llama-cpp <binary>
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2026-04-12 00:39:14 -04:00 |
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TurboQuant Agent
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dea59c04d7
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Add benchmark test prompts for quality comparison (Issue #22)
- 10 prompts covering all required categories:
1. Factual recall (thermodynamics)
2. Code generation (merge sorted lists)
3. Reasoning (syllogism)
4. Long-form writing (AI sovereignty essay)
5. Summarization (~250 word passage)
6. Tool-call format (JSON output)
7. Multi-turn context (number: 7429)
8. Math (17*23+156/12)
9. Creative (haiku about ML dreams)
10. Instruction following (numbered, bold, code block)
- Each prompt includes expected_pattern for automated scoring
- Multi-turn prompt has both initial and follow-up questions
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2026-03-31 17:31:05 +00:00 |
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88b8a7c75d
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feat: add benchmarking script for quality assessment
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2026-03-30 21:14:49 +00:00 |
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857c42a327
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feat: add standardized benchmarking prompts
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2026-03-30 21:14:48 +00:00 |
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