hpc: PBS job scripts for Imperial CX3 (probe + scaled LLM merge run)

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>

hpc/llm_merge.pbs

@@ -0,0 +1,28 @@
+#!/bin/bash
+# Scaled LLM merge experiment on an L40S (48 GB) — the "firm up the sign" run.
+# Prereqs (do ONCE on the login node, which has internet — see hpc/README.md):
+#   1) build the env:            uv sync --extra dev --extra neural --extra llm
+#      (if the L40S driver is older than CUDA 13, first pin torch — see README)
+#   2) pre-download the base:    HF_HOME=$HOME/hf_cache uv run python -c \
+#         "from transformers import AutoModelForCausalLM,AutoTokenizer as T; \
+#          [f('Qwen/Qwen2.5-7B-Instruct') for f in (T.from_pretrained, AutoModelForCausalLM.from_pretrained)]"
+#   submit:  qsub hpc/llm_merge.pbs
+#PBS -l select=1:ncpus=8:mem=64gb:ngpus=1:gpu_type=L40S
+#PBS -l walltime=04:00:00
+#PBS -N lamarckian_llm_merge
+
+cd "$PBS_O_WORKDIR"
+export HF_HOME="$HOME/hf_cache"           # models cached here (pre-downloaded on the login node)
+export HF_HUB_OFFLINE=1                    # compute node has no internet -> use the cache
+                                          # (delete this line if the probe shows internet works)
+export TOKENIZERS_PARALLELISM=false
+
+source .venv/bin/activate                 # uv-built env (Python 3.14 + torch + transformers/peft)
+nvidia-smi --query-gpu=name,memory.total --format=csv,noheader
+
+python -m llm.experiment configs/llm/merge_hpc.yaml
+
+# results/llm_merge_hpc/{results.parquet,resolved_config.yaml,manifest.json} are written in-place.
+# The parquet is gitignored; sync it back to analyse/plot locally, e.g. from your laptop:
+#   rsync -avz <user>@login.hpc.ic.ac.uk:'~/…/results/llm_merge_hpc/' results/llm_merge_hpc/
+echo "done: $(date)"