# 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.

**Current occupant: run 7** — `erp-tune-v7-nvfp4a16` = merged-run07 (jenerallee78
ARA-abliterated Gemma-4-26B-A4B-it, index `33c59654…`, + R47 SFT r7 = r6 plus the opening-split
slot and its companion loss mask), quantized by `services/erp-seat-quant/` on 2026-09-09.
49 GiB bf16 → 16 GiB NVFP4A16. Runbook `docs/runbooks/gx10-run-07.md`.

*Previous: run 6 (`erp-tune-v6-nvfp4a16`, 2026-09-08). Its artifact is still on `/tank/aimodels/`
and the pre-swap host env is at `/tmp/erp-seat-env.v6.bak` on ana-ml2, so a rollback is an `.env`
flip plus `docker compose up -d`.*

- **True name only.** `--served-model-name erp-tune-v7-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.
- **Forced tool_choice (named / `required`) is prompt-driven on EVERY Gemma-4 seat, not
  grammar-enforced — by vLLM design.** `vllm/tool_parsers/gemma4_engine_tool_parser.py` sets
  `supports_required_and_named = False` and its `adjust_request` deliberately skips the
  structured-output JSON for required/named so the model can emit its native
  `<|tool_call>call:…` syntax. A tune that weakened that syntax (this ERP tune) therefore
  honours forced calls only sometimes. Measured 2026-09-08, 3 conversations × 3 turns, real
  system prompt: v0.26.0 **1/9**; nightly `311b3513` (v0.27.2rc1, the `gen` seat's image) **6/9**
  and the tool-result round trip stays clean 3/3 — so the seat runs the nightly. tts-dev measured
  0/18 on v0.26.0 with `gen` 18/18 as the positive control, and **`response_format:
  json_schema` (guided decoding) 18/18 on this seat** — that is the deterministic path for a
  forced call; `tool_choice: auto` works normally. A parser plugin that re-enables guided JSON
  would also need JSON extraction in the engine-parser path; not attempted.
- **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`.