vh
6972e7ef7f
- services/erp-seat-quant/run_quant_erp_v7.sh: v6 runner retargeted; dry-run gate passed identically (11,725 targets, 11,520 experts = 30x128x3, routers+vision BF16) - 49 GiB bf16 relayed gx10 -> ana-ml2 (no key path either way; nh3-dev relays), checksums verified against source; quant 49 -> 16 GiB, all post-steps clean - stacks/erp-seat: .env-driven swap to erp-tune-v7-nvfp4a16, served under its TRUE name; homepage labels + README updated, v6 rollback path recorded - stacks/litellm: trial -> erp-tune-v7-nvfp4a16 (config-file alias; /model/update refuses a config model, so this is an edit + restart)
45 lines
3.4 KiB
Markdown
45 lines
3.4 KiB
Markdown
# erp-seat — ERP-tune seat on ana-ml2 (GPU1, `:8021`)
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Serves the latest gated ERP LoRA merge as an **NVFP4A16** (weight-only) compressed-tensors
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checkpoint so the GX10 is free to train the next run.
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**Current occupant: run 7** — `erp-tune-v7-nvfp4a16` = merged-run07 (jenerallee78
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ARA-abliterated Gemma-4-26B-A4B-it, index `33c59654…`, + R47 SFT r7 = r6 plus the opening-split
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slot and its companion loss mask), quantized by `services/erp-seat-quant/` on 2026-09-09.
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49 GiB bf16 → 16 GiB NVFP4A16. Runbook `docs/runbooks/gx10-run-07.md`.
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*Previous: run 6 (`erp-tune-v6-nvfp4a16`, 2026-09-08). Its artifact is still on `/tank/aimodels/`
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and the pre-swap host env is at `/tmp/erp-seat-env.v6.bak` on ana-ml2, so a rollback is an `.env`
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flip plus `docker compose up -d`.*
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- **True name only.** `--served-model-name erp-tune-v7-nvfp4a16`. Gateway aliases (`trial`) are
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set in LiteLLM on the operator's word, never here (no silent substitution — the bf16 arm on the
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GX10 and this NVFP4 arm are different artifacts).
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- **Recipe** = `stacks/gemma4-charrp` (same arch + format, proven on this box): `gemma4` tool
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and reasoning parsers, `enable_thinking` pinned false, the model's own stock template
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(`ae53464b…`, the one it trained through). Without the reasoning parser the post-tool turn leaks
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`<|channel>` markers; without the kwargs pin all prose lands in `reasoning_content`.
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- **`tool_choice: "none"` trap (measured 2026-09-08, fixed with `--exclude-tools-when-tool-choice-none`).**
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Without the flag vLLM still renders the tools into the prompt, the model emits a tool call
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anyway, and because parsing is off for `none` the reply is `content: null, tool_calls: null`
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— an empty turn, 3/3 reproductions. With the flag the tools are dropped from the prompt and
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the model answers in prose (3/3). The rest of the matrix (auto / required / named / parallel /
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nested schema / empty `tools: []` / streaming / tool-result round trip) was green before and
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after. `stacks/gemma4-charrp` has the same exposure and does NOT carry the flag yet.
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- **Forced tool_choice (named / `required`) is prompt-driven on EVERY Gemma-4 seat, not
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grammar-enforced — by vLLM design.** `vllm/tool_parsers/gemma4_engine_tool_parser.py` sets
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`supports_required_and_named = False` and its `adjust_request` deliberately skips the
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structured-output JSON for required/named so the model can emit its native
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`<|tool_call>call:…` syntax. A tune that weakened that syntax (this ERP tune) therefore
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honours forced calls only sometimes. Measured 2026-09-08, 3 conversations × 3 turns, real
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system prompt: v0.26.0 **1/9**; nightly `311b3513` (v0.27.2rc1, the `gen` seat's image) **6/9**
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and the tool-result round trip stays clean 3/3 — so the seat runs the nightly. tts-dev measured
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0/18 on v0.26.0 with `gen` 18/18 as the positive control, and **`response_format:
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json_schema` (guided decoding) 18/18 on this seat** — that is the deterministic path for a
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forced call; `tool_choice: auto` works normally. A parser plugin that re-enables guided JSON
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would also need JSON extraction in the engine-parser path; not attempted.
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- **GPU1 is shared** — check real usage (`nvidia-smi --query-compute-apps=pid,used_memory`) before
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raising `ERP_GPU_MEM_UTIL`; the flag sizes KV, not CUDA context.
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- **Rollback / next run:** point `ERP_MODEL` + `ERP_SERVED_NAME` at the next quant dir, keep the
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previous on disk. Deploy with `scripts/deploy-stack.sh ana-ml2 erp-seat`.
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