adminandClaude
b9f45a7c46
Ran via tools/grade_run.py against LM Studio (no clipboard). Results: TTS: 80 Minor Flaws PASSES (real N-worker concurrency) <- Qwen 49, didn't parse Rust: 72 Minor Flaws COMPILES CLEAN (0 errs w/ deps) <- Qwen 50, 7 real errors Webhook: 55 Critical uses forbidden aiohttp (won't run) <- Qwen 75, passed Automation: 48 Critical SyntaxError (global-after-assign) <- first run for both DECISIVE head-to-head: Gemma generalizes where Qwen fails (TTS, Rust), but Qwen beats it on stdlib-discipline prompts (webhook). The two are COMPLEMENTARY local offloads, not redundant. Fixed: grade_run.py extractor (markdown/prose wrapping, multi-fence lang selection), TTFT-null handling in generator. TTFT capture from LM Studio API still needs the right stats key (left null + noted). Co-Authored-By: Claude <noreply@anthropic.com>
🧪 Local LLM Benchmark Suite
Grades local LLM models (run via LM Studio on an Apple M3 Max / 48 GB) on a strict systems-coding prompt — a pure-stdlib Python concurrent async LFU cache with TTL eviction and ACID transactions — and renders the results into a cyberpunk-terminal dashboard.
What's in here
prompts/
lfu_cache_prompt.txt # the exam prompt every model gets
grading.txt # the grader's rubric (5 pillars × 20 pts = 100)
outputs/ # raw model .py outputs (named <model>-<quant>.py)
data/
benchmark_history.json # persistent results store (source of truth)
generate_dashboard.py # reads the JSON → builds dashboard.html + pages/*.html
dashboard.html # generated — main leaderboard + charts (gitignored)
pages/ # generated — per-model detail pages (gitignored)
Workflow
-
Feed
prompts/lfu_cache_prompt.txtto a model in LM Studio. -
Save its output to
outputs/<model>-<quant>.py. -
Grade it (audit the 5 pillars, capture tok/sec + tokens + TTFT), and append its entry to
data/benchmark_history.json. Seeprompts/grading.txtfor the rubric. -
Regenerate the site:
python3 generate_dashboard.pyThis (re)writes
dashboard.htmland everypages/<model>.html.
Viewing locally
python3 -m http.server 8000
# open http://localhost:8000/dashboard.html
Deploying (Gitea + Coolify)
The repo holds source only (outputs/, data/, generate_dashboard.py, prompts/).
The generated dashboard.html and pages/ are gitignored — Coolify runs
python3 generate_dashboard.py as a build step, then serves the static files.
See DEPLOY.md for the exact Coolify service config.
Hardware
Apple M3 Max, 48 GB unified memory. Local inference via LM Studio. A cloud model (DeepSeek V4 Flash) is included as a quality baseline.