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Part of #3079 (round 7). Plan §7, experiment T0. Merge and conversion are CPU work and can run during the cold re-run; only the scoring waits for the card.
Why
Before a single GPU-hour goes into training, prove the seam adapter → 16-bit merge → f16 GGUF → quantise → ollama create → score with an adapter whose behaviour is already measured. We have exactly one: the REx86 LoRA (#160 — evaluated through a pinned Transformers + PEFT runtime and approved: equivalent accuracy to its base, ~34 % faster, identical injection resistance; 4 cases regressed by keyword-matching only). #356 asked for this conversion path and was parked into #1523; it never ran. This is that work, done as the pipeline's own control.
save_pretrained_merged(..., "merged_16bit") → convert_hf_to_gguf.py --outtype f16 → llama-quantize to Q4_K_M and Q8_0 (Q8_0 isolates the merge from the quantisation).
Score all four with record_baseline.py --tier A and --tier B on the round-7 pin (17 cases / 83), cold protocol, N=2 with escalation; commit the transcripts (synthetic corpus).
the merged GGUF reproduces the adapter's behavioural delta against its base at Q8_0 (same direction on the named cases, no injection-gate regression); Q4_K_M reported beside it
Part of #3079 (round 7). Plan §7, experiment T0. Merge and conversion are CPU work and can run during the cold re-run; only the scoring waits for the card.
Why
Before a single GPU-hour goes into training, prove the seam adapter → 16-bit merge → f16 GGUF → quantise →
ollama create→ score with an adapter whose behaviour is already measured. We have exactly one: the REx86 LoRA (#160 — evaluated through a pinned Transformers + PEFT runtime and approved: equivalent accuracy to its base, ~34 % faster, identical injection resistance; 4 cases regressed by keyword-matching only). #356 asked for this conversion path and was parked into #1523; it never ran. This is that work, done as the pipeline's own control.Inputs (verify, do not assume — #160's list)
REx86.zip's adapter, 323,014,168 bytes, SHA-256a21a6918c0416fcc7f8774b3bd41b373a0aab53e81a8b14c3ac6f9eed280590f, LoRA r=32 / α=64, CC-BY-4.0unsloth/Qwen2.5-Coder-7B@ revision5762507e8ed2132906da60f86a2b23b54673ee81— the exact base; not the instruct model, not another quantSteps
FastLanguageModel.from_pretrainedon the base, then apply the PEFT adapter); check tokenizer / template parity with Evaluate the REx86 adapter through a supported exact-base local runtime #160's runtime notes.save_pretrained_merged(..., "merged_16bit")→convert_hf_to_gguf.py --outtype f16→llama-quantizeto Q4_K_M and Q8_0 (Q8_0 isolates the merge from the quantisation).ollama create rex86-merged:q4_k_m/:q8_0, and the untouched base at the same two levels (qwen2.5-coder-7b-base:q4_k_m/:q8_0) so the delta is measured on this pin, not read from Evaluate the REx86 adapter through a supported exact-base local runtime #160.record_baseline.py --tier Aand--tier Bon the round-7 pin (17 cases / 83), cold protocol, N=2 with escalation; commit the transcripts (synthetic corpus).vulnerable_strcpy,tlv_parser,indirect_dispatch, and the secondvulnerable_strcpyslice) in the transcripts, not just the totals.Acceptance
manifest.jsonfromexport_to_ollama.shcommitted with the run; adapter + merged f16 kept under/mnt-1/training/runs/t0-rex86/Depends on round7-1. Blocks nothing directly, but round7-5 must not start scoring before this is green.