# erp-seat — ERP-tune seat on ana-ml2 (GPU1, `:8021`)

Serves the latest gated ERP LoRA merge as an **NVFP4A16** (weight-only) compressed-tensors
checkpoint so the GX10 is free to train the next run. First occupant: **run 6** —
`erp-tune-v6-nvfp4a16` = merged-run06 (jenerallee78 ARA-abliterated Gemma-4-26B-A4B-it, index
`33c59654…`, + R47 SFT r6) quantized by `services/erp-seat-quant/`.

- **True name only.** `--served-model-name erp-tune-v6-nvfp4a16`. Gateway aliases (`trial`) are
  set in LiteLLM on the operator's word, never here (no silent substitution — the bf16 arm on the
  GX10 and this NVFP4 arm are different artifacts).
- **Recipe** = `stacks/gemma4-charrp` (same arch + format, proven on this box): `gemma4` tool
  and reasoning parsers, `enable_thinking` pinned false, the model's own stock template
  (`ae53464b…`, the one it trained through). Without the reasoning parser the post-tool turn leaks
  `<|channel>` markers; without the kwargs pin all prose lands in `reasoning_content`.
- **`tool_choice: "none"` trap (measured 2026-09-08, fixed with `--exclude-tools-when-tool-choice-none`).**
  Without the flag vLLM still renders the tools into the prompt, the model emits a tool call
  anyway, and because parsing is off for `none` the reply is `content: null, tool_calls: null`
  — an empty turn, 3/3 reproductions. With the flag the tools are dropped from the prompt and
  the model answers in prose (3/3). The rest of the matrix (auto / required / named / parallel /
  nested schema / empty `tools: []` / streaming / tool-result round trip) was green before and
  after. `stacks/gemma4-charrp` has the same exposure and does NOT carry the flag yet.
- **GPU1 is shared** — check real usage (`nvidia-smi --query-compute-apps=pid,used_memory`) before
  raising `ERP_GPU_MEM_UTIL`; the flag sizes KV, not CUDA context.
- **Rollback / next run:** point `ERP_MODEL` + `ERP_SERVED_NAME` at the next quant dir, keep the
  previous on disk. Deploy with `scripts/deploy-stack.sh ana-ml2 erp-seat`.