adminandClaude
8d2d5cd562
New graded (11 total now):
gemma4-26b-a4b-8bit-mlx 82 Minor Flaws (tied top local; delta-based tx freq)
qwen3.6-27b-8bit-mlx 78 Minor Flaws (clean; anom. slow generation flagged)
qwen3-coder-30b-6bit-mlx 50 Critical (asyncio.Lock used with sync with -> crash)
Dashboard redesign:
- Bar chart is now the full-width hero row (was cramped half-width)
- 4 stat tiles squished 2x2 beside the radar up top
- Quant + Format are dedicated columns in the leaderboard (MLX/GGUF/CLOUD chips)
- New 'Format & Quant Showdown' panel: groups same-family variants so
GGUF-vs-MLX and quant-depth comparisons are side by side
- Bar-chart axis labels now include the quant so duplicate model names
are distinguishable, with rotation for readability
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.