README.md

---
license: eupl-1.2
tags:
  - benchmark
  - lethean
  - lem
  - tools
---

# LEM-Eval

The 8-PAC benchmark runner for the Lemma model family.

A HuggingFace dataset repo used as a tool-shaped "github" — the entire
scorer lives here, anyone clones it, installs once, and the worker
machines chug along advancing per-model canons in lockstep. Multiple
workers farm different targets in parallel — each target declares a
`type` (`mlx` or `gguf`) in `targets.yaml`, and workers filter by the
backends they can actually run (capability probe or `LEM_TYPES` env).
Partition falls out of what the hardware can do, not hostnames.

## What it does

For each declared target (a base + LEK-merged model pair), runs a
**paired 8-PAC benchmark**:

- 8 independent rounds per question using Google-calibrated Gemma 4
  sampling (`temp=1.0, top_p=0.95, top_k=64, enable_thinking=True`)
- Both models see the exact same question set from the seed-42 shuffled
  test split — the only variable is the weights
- Auto-offset progression: each run advances the canon by `n_questions`
  via lighteval's `--samples-start` flag, so consecutive runs naturally
  cover contiguous non-overlapping windows

Results are written to **two canonical destinations** per run:

1. **The target model repo's `.eval_results/<task>.parquet`** — primary,
   per-model scorecard, drives the HF model-card eval_results rendering
2. **`lthn/LEM-benchmarks/results/<target>/<task>.parquet`** —
   aggregated, fleet-wide, grows as more machines contribute observations

Same row data, two locations. Dedup on
`(machine, iter_timestamp, question_index, round, model_side)` on both
canons, so the same machine re-running the same slice is idempotent but
different machines contribute additive rows to the aggregator.

## Layout

```
LEM-Eval/
├── eval.py               # target-driven runner (PEP 723 — uv run it)
├── mlx_lm_wrapper.py     # lighteval custom model backend
├── targets.yaml          # declarative fleet spec (base, this, type)
├── install.sh            # bootstrap: clone model repos + lem-benchmarks
├── lem-eval.sh           # service script (once | maintain | loop)
├── cron/
│   ├── submit.cron       # */30 * * * * lem-eval.sh once
│   └── maintain.cron     # 15 * * * * lem-eval.sh maintain
└── workspaces/           # local clones of target model repos (gitignored)
    ├── lemer/
    ├── lemma/
    └── ...
```

## Quick start (worker)

```bash
export HF_TOKEN=hf_...    # or: huggingface-cli login
git clone https://huggingface.co/datasets/lthn/LEM-Eval
cd LEM-Eval
./install.sh              # clones lem-benchmarks + your owned model repos
./lem-eval.sh once        # run one pass manually to verify

# Install the continuous cron
crontab -l | cat - cron/submit.cron cron/maintain.cron | crontab -
```

Add a new machine: install LEM-Eval on it, the worker's backend probe
decides which targets it can run (mlx on Apple Silicon, gguf where an
Ollama endpoint is reachable). Override with `LEM_TYPES=mlx,gguf` in
the cron env if you want explicit control. Workers pick up `targets.yaml`
edits via the `maintain` cron's hourly `git pull`.

**gguf wrapper status:** not yet implemented. gguf targets (`lemmy`,
`lemrd`) sit in `targets.yaml` waiting for `gguf_wrapper.py` — will be
an OpenAI-SDK wrapper pointing at a local Ollama/llama.cpp server.
Until then, gguf targets list but don't run.

## Quick start (manual / dev)

```bash
uv run eval.py --list-targets                     # show the fleet
uv run eval.py --my-targets                       # show targets owned by $(hostname)
uv run eval.py --target lemer --n-questions 1 --rounds 8
uv run eval.py --target lemer --loop 8            # 8 back-to-back advances
```

PEP 723 inline metadata declares all dependencies — no venv setup
needed. `uv` creates one automatically, caches it, and pulls
`lighteval` from our [fork](https://github.com/LetheanNetwork/lighteval)
which carries benchmark-stability patches (MMLU-Pro template fix,
`--samples-start` offset).

## Related

- [`lthn/LEM-benchmarks`](https://huggingface.co/datasets/lthn/LEM-benchmarks) — aggregated results store
- [`LetheanNetwork/lighteval`](https://github.com/LetheanNetwork/lighteval) — benchmark-stability fork
- [`lthn/lemer`](https://huggingface.co/lthn/lemer), `lemma`, `lemmy`, `lemrd` — target models

## License

EUPL-1.2.