Giorgio Gilestro
809e45a5e0
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>
22 lines
1,006 B
YAML
22 lines
1,006 B
YAML
experiment: llm_merge
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kind: llm_merge
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seed: 1
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n_replicates: 1
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# (Layer 2 / LLM prototype — blueprint C2/C4, the real-LLM image of E8): recombine specialist LLMs.
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# Train one LoRA specialist per DISJOINT task family on a small open-weight base, then compare the
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# base, each specialist, and their weight-space MERGES (soup = averaged deltas; ties = sign-reconciled
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# union) on a held-out mixed test set. Tasks are procedurally generated and exactly verified (the
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# "reality that says no"), and deliberately hard so specialists are decorrelated. Expect (per E8): the
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# recombined model beats any single specialist overall AND is competent across ALL families
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# (worst-family accuracy), which no single parent is. Falsifier: a single specialist matches the merge.
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base_model: Qwen/Qwen2.5-0.5B-Instruct # Apache-2.0; ~1 GB, fits 16 GB with room to spare
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families: [lists, strings, arith]
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n_train: 700
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n_test: 100
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epochs: 3
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lora: {r: 16, alpha: 32}
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merges: [soup, ties]
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output: {dir: results/llm_merge}
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