# CLAUDE.md — Local LLM Benchmark Testing Project

## What this project is

A personal test harness for evaluating **local LLM models** running in **LM Studio** on an **Apple M3 Max MacBook Pro (48 GB unified memory)**. A fixed coding prompt (LFU cache) is fed to each model, the model's Python output is graded against a strict rubric, and the results are accumulated into a persistent JSON store and rendered into a standalone HTML dashboard.

This is **a git repo** (remote: `ssh://git@git.itsaygea.com:2222/admin/modelTesting.git`, branch `main`) deployed to Coolify, but it has **no runtime build/run cycle locally** — it's a folder of prompt files, model outputs, a JSON results store, and a Python generator that emits static HTML.

## Folder layout

```
prompts/
  lfu_cache_prompt.txt   # The fixed prompt given to every model (the "exam question")
  grading.txt            # The grader's instructions / rubric (read at session start)
outputs/                 # Raw model outputs (the .py files each model produced)
  <model-name>-<quant>.py
data/
  benchmark_history.json # Persistent results store (SOURCE OF TRUTH — committed)
generate_dashboard.py    # Reads the JSON → builds dashboard.html + pages/*.html
dashboard.html           # GENERATED — gitignored (built on deploy, not committed)
pages/                   # GENERATED — per-model detail pages, gitignored
Dockerfile               # Coolify: python build stage → nginx static serve
DEPLOY.md                # Gitea→Coolify deploy instructions
README.md
```

**Git policy:** source only is committed (`prompts/`, `outputs/`, `data/benchmark_history.json`, `generate_dashboard.py`, `Dockerfile`, docs). The generated `dashboard.html` + `pages/` are gitignored — Coolify rebuilds them on each push via the Dockerfile's `python3 generate_dashboard.py` step. If you ever want to commit the built HTML instead, uncomment those lines in `.gitignore`.

**Remote:** `ssh://git@git.itsaygea.com:2222/admin/modelTesting.git` (SSH key auth as `admin` verified). Push to `main` → Coolify rebuilds.

```
prompts/
  lfu_cache_prompt.txt   # The fixed prompt given to every model (the "exam question")
  grading.txt            # The grader's instructions / rubric (read at session start)
outputs/                 # Raw model outputs (the .py files each model produced)
  <model-name>-<quant>.py
data/
  benchmark_history.json # Persistent results store (created on first grading run)
dashboard.html           # Generated standalone dashboard (dark-mode, Chart.js via CDN)
*.py                     # Loose model outputs in root (e.g. deepseekv4flash.py) — legacy/unsorted
```

## The workflow (what to do when the user submits a model's output)

Follow the 4 steps in `prompts/grading.txt`. **Captured metrics per model:** `tok_sec`, `total_tokens`, `ttft_sec` (ask the user for these when grading — can't be inferred from files), plus optional `speed_caveat` text (e.g. the Gemma4 GPU-offload note) and `tests_pass` (bool — always run the model's `.py` and record whether it crashes).

1. **Audit** the `.py` file against the 5 pillars (each 0–20 → 0–100 total):
   - Complexity violations (true O(1) — no heaps, `sorted()`, linear scans)
   - Async race conditions / deadlocks (locks held across `sleep`/IO, unsynced shared state)
   - Transactional isolation leaks (read-your-own-writes, commit/rollback correctness)
   - Memory leaks & edge cases (DLL unlink, `min_freq` updates, `time.monotonic()`, `__slots__`)
   - Test coverage integrity (real edge cases vs trivial always-pass asserts)
2. **Extract metrics**: model name, quant, tok/sec, verdict, archetype/best-for, critical bugs, patch code.
3. **Update `data/benchmark_history.json`** — append/update the model's entry (schema in `grading.txt`). Create the file if missing.
4. **Regenerate the site** by running `python3 generate_dashboard.py` (reads `data/benchmark_history.json`, overwrites `dashboard.html` + every `pages/<id>.html`). Style is locked: **cyberpunk-terminal** (dark `#0a0a0f` bg, neon cyan/magenta/lime accents, Fira Code/Sans, scanline+grid texture, glow-on-hover score bars). Dashboard = summary stats + Chart.js bar (score vs tok/sec) + radar (top-3 pillars) + leaderboard table linking to per-model detail pages. Each detail page = verdict/score header, metric tiles, pillar bars, "went right/wrong" columns, critical bugs, recommended use, and a refactored patch code block. `tests_pass`, cloud models (no tok/sec), and speed-caveats are all handled in the UI.

The exam prompt (`lfu_cache_prompt.txt`) requires a **pure-stdlib Python 3.11+ async LFU cache** with frequency-bucket O(1), dual-layer TTL eviction, ACID-like transactions, and an `async def main()` test suite. Use it as the spec when judging correctness.

## Model output — naming convention

Model output filenames encode the **model + quant/format** so a model is identifiable from the filename alone. The convention the user uses:

`<model-family><version>-<param-size>-<variant>-<quant-or-format>.py`

Lowercase, hyphen-separated, no spaces. Examples seen so far:

| Filename | Reads as |
|---|---|
| `qwen3.6-35b-a3b-6bit-mlx.py` | Qwen 3.6, 35B-A3B (MoE), 6-bit, MLX format |
| `qwen3.6-35b-a3b-uncensored-hauhaucs-aggressive-gguf.py` | Qwen 3.6 35B-A3B, uncensored fine-tune, GGUF |
| `gemma-4-31b-qat-gguf.py` | Gemma 4 31B, QAT, GGUF |
| `gemma-4-12b-coder-fable5-composer2.5-v1-uncensored-heretic-mxfp8-mlx.py` | merged/model-card-style name, MLX |
| `kat-coder-v2.5-dev-xl-mlx.py` | KAT Coder v2.5 Dev XL, MLX |

Tokens: `mlx` = Apple MLX format; `gguf` = llama.cpp GGUF; quant suffixes like `q6k`, `6bit`, `4bit`, `qat`, `mxfp8` go in the name. When in doubt about what a filename denotes, **parse it loosely into model + quant** rather than guessing a wrong label — the grading step pulls model/quant from "file name or user input," so either source is valid.

### When the user pastes raw output instead of giving a file

If the user pastes a model's output text directly (no file), **save it as a `.py` file in `outputs/`** using the naming convention above before grading. Ask the user for the model name + quant only if it cannot be reasonably inferred from context. Match the existing lowercase-hyphen style.

## Hardware / runtime context

- **Machine:** Apple M3 Max, 48 GB unified memory. Local inference only.
- **Inference server:** LM Studio (OpenAI-compatible local endpoint).
- Quants/formats that fit 48 GB comfortably: ~35B dense at 4-bit/6-bit, larger MoE models (only active params loaded). MLX is preferred for Apple Silicon; GGUF via llama.cpp also works.

## Notes / gotchas

- `data/` starts **empty** — `benchmark_history.json` does not exist until the first grading run creates it. Don't assume it's there; read defensively, create on write.
- `dashboard.html` is **regenerated** (overwritten), not appended to. The source of truth is the JSON file.
- No build step, no tests to run, no dependencies to install — pure stdlib Python outputs + a static HTML file.
- Treat the loose `.py` files in the project root (like `deepseekv4flash.py`) as unsorted outputs that belong in `outputs/` per the convention. Don't move them unless asked.