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jvonrad/
xscript-eval

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External benchmark evaluation

Isambard-AI is blocked by a CPU-minutes quota, so we evaluate the trained checkpoints on a GPU elsewhere. The models are a custom LLaMA-style Transformer (src/xscript/model.py) + SentencePiece tokenizer — pure PyTorch, using F.scaled_dot_product_attention, no flash-attn / triton / custom kernels — so they run on any stock GPU (or CPU, slowly). Each model is ~1B params (fits any 16GB GPU).

The benchmark harness (src/xscript/eval/bench.py) wraps our model into lm-evaluation-harness and scores Global-MMLU, Belebele, and XNLI on each run's training languages. It is the same harness we would have run on-cluster, so numbers are directly comparable.

1. Export from Isambard (already done by `upload_to_hf.py`)

The private HF repo mirrors the on-cluster layout:

src/xscript/**                       # bundled model + harness code
tokenizers/unigram_{starved,destarved}/{sp.model,meta.json}
runs/<name>/checkpoints/final.pt     # 15 checkpoints, fp32, ~4GB each
models.json                          # friendly name -> tokenizer + langs + orig run
run_benchmarks.py  requirements.txt  README.md

Models use friendly names <mixture>-<starved|fair> (e.g. en-fair, en-ar-starved). models.json maps each to its real tokenizer.

2. Run on your GPU

bash

# clone just the runner (or download run_benchmarks.py + requirements.txt from the repo)
pip install torch --index-url https://download.pytorch.org/whl/cu121   # match your CUDA
pip install -r requirements.txt
export HF_TOKEN=hf_...        # while the repo is private

# quick validation pass over all 15 runs (~200 examples/task) -- do this FIRST
python run_benchmarks.py --repo jvonrad/xscript-eval --limit 200

# full suite once the quick pass looks sane
python run_benchmarks.py --repo jvonrad/xscript-eval

The runner downloads one checkpoint at a time and deletes it after eval (--keep-checkpoints to retain), so peak disk is ~5GB. Results:

xscript_bench/results/bench/<run>_final.json    # per-run task accuracies
xscript_bench/results/summary.json              # everything combined

Send those JSONs back for analysis.

Notes

--runs en-starved en-fair limits to a subset (friendly names).
--tasks xnli_en xnli_de overrides the task list (default = the run's langs).
Mono runs get 3 tasks (their one language), bilingual runs get 6 (both langs).
Scores are ordinary accuracy (acc,none); raw harness output is preserved in each per-run JSON for length-normalized variants.

jvonrad/xscript-eval

External benchmark evaluation

1. Export from Isambard (already done by upload_to_hf.py)

2. Run on your GPU

Notes

jvonrad/
xscript-eval

1. Export from Isambard (already done by `upload_to_hf.py`)