Initial benchmark suite: 8 graded models + cyberpunk dashboard generator

- prompts/: LFU cache exam + 5-pillar grading rubric
- outputs/: 8 model .py outputs (local + cloud baseline)
- data/benchmark_history.json: graded results (scores, metrics, bugs, patches)
- generate_dashboard.py: builds dashboard.html + pages/*.html from JSON
- Dockerfile + DEPLOY.md: Gitea→Coolify deploy (build-step, nginx static)
- .gitignore: generated HTML excluded (built on deploy)

Co-Authored-By: Claude <noreply@anthropic.com>

README.md

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+# 🧪 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
+
+1. Feed `prompts/lfu_cache_prompt.txt` to a model in LM Studio.
+2. Save its output to `outputs/<model>-<quant>.py`.
+3. Grade it (audit the 5 pillars, capture tok/sec + tokens + TTFT), and append its
+   entry to `data/benchmark_history.json`. See `prompts/grading.txt` for the rubric.
+4. Regenerate the site:
+
+   ```bash
+   python3 generate_dashboard.py
+   ```
+
+   This (re)writes `dashboard.html` and every `pages/<model>.html`.
+
+## Viewing locally
+
+```bash
+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.