Giorgio Gilestro
84124de143
Clarity pass over the main text (36-item audit), Discussion rewrite and cut, acknowledgements, Souly et al. as ref 62, lettered SI panels, model section moved under Results; plus the untracked curriculum/society/compose/smol configs, runners, figures, stats and tests that the SI already cites. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Y64o8FKP7rCuXzC48pxpMm
26 lines
1.4 KiB
Bash
Executable file
26 lines
1.4 KiB
Bash
Executable file
#!/bin/bash
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# The HARD-benchmark firm-up on an L40S (46 GB): runs moe_hard_hpc then directed_hard_hpc in one job.
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# moe trains the hard specialists (cached spec_*_hard); directed reuses them — so training happens once
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# and both experiments share it. Harder tasks (multi-step lists, Caesar ciphers, multi-step arith) keep
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# 7B off saturation, giving routing/fusion/selection real headroom to separate — the fair test the easy
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# 7B runs could not provide. Same env as the other LLM jobs (see hpc/README.md).
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# submit: qsub hpc/llm_hard.pbs status: qstat -u $USER
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#PBS -l select=1:ncpus=8:mem=64gb:ngpus=1:gpu_type=L40S
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#PBS -l walltime=01:00:00
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#PBS -N lam_llm_hard
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cd "$PBS_O_WORKDIR"
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export HF_HOME="$EPHEMERAL/hf_cache"
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export TOKENIZERS_PARALLELISM=false
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export UV_CACHE_DIR="$EPHEMERAL/uvcache"
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source .venv/bin/activate
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nvidia-smi --query-gpu=name,memory.total,driver_version --format=csv,noheader
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python -m llm.experiment configs/llm/moe_hard_hpc.yaml # trains spec_*_hard + fusion/union
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python -m llm.experiment configs/llm/directed_hard_hpc.yaml # reuses spec_*_hard + offspring select
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# results/{llm_moe_hard_hpc,llm_directed_hard_hpc}/ written in-place (parquet gitignored). Sync back:
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# rsync -avz hpc:'…/results/llm_moe_hard_hpc/' results/llm_moe_hard_hpc/
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# rsync -avz hpc:'…/results/llm_directed_hard_hpc/' results/llm_directed_hard_hpc/
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echo "done: $(date)"
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