Enable running the LLM tier on Imperial's HPC (scheduler: PBS Pro / qsub).
- hpc/probe.pbs: 10-min 1-GPU reconnaissance job resolving the two unknowns
the RCS docs omit -- compute-node internet access and the L40S driver's
CUDA version -- plus TMPDIR/disk and available python/cuda modules.
- hpc/llm_merge.pbs: scaled run on an L40S (48 GB), offline HF-cache wired,
runs configs/llm/merge_hpc.yaml.
- configs/llm/merge_hpc.yaml: Qwen2.5-7B-Instruct (fits the L40S) to reduce
the noise that left the 0.5B prototype's overall-exceeds sign marginal.
- hpc/README.md: the git-based workflow (login-node uv env + model
pre-download -> qsub -> rsync results back), confirmed PBS/GPU directives,
and the code TODOs for the definitive run (multi-seed, more families,
directed/dilution-resistant merge).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
First step from toy models toward real language models, on one 16 GB GPU.
New src/llm/ package: procedural task families + exact-match verifier
(tasks.py), batched eval (evaluate.py), LoRA specialisation (specialise.py,
manual answer-only SFT), weight-space merge via peft add_weighted_adapter
(merge.py: soup = averaged deltas, ties = sign-reconciled union), runner
(experiment.py, kind llm_merge). Base Qwen2.5-0.5B-Instruct (Apache-2.0);
three disjoint hard families (lists/strings/arith); one LoRA specialist each
(~90s total).
Result (seed 1), reported honestly:
- STRONG/robust: the merges are the ONLY models competent across ALL
families -- worst-family ~0.25 vs <0.16 for every single specialist (the
Fisher-Muller "generalist assembled from specialists" signature, in real
LoRA weights).
- MARGINAL: "exceeds every parent overall" is only marginal at this scale
(soup 0.64 vs best specialist 0.63; ties 0.61 below it).
- CAVEAT VISIBLE: averaging dilutes peaks (lists specialist 0.43 -> merge
0.26) -- Layer-1's "merge, don't average" (E4) appearing in real weights.
The pipeline works end-to-end; the balance/retention half reproduces; the
strict overall-exceeds and soup-vs-ties distinction need scale (bigger base,
more/cleaner families, seeds, a dilution-resistant / offspring-selected
merge) -- the HPC step. Env: Python 3.14 + transformers 5.13 works;
note transformers-5.x apply_chat_template returns a dict. make env-llm /
make llm; adapters under gitignored models/llm/, base in the HF cache.
figures/plot_llm_merge.py, README, tests/test_llm.py (+3, 125 green).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
