vh
b8f0f4c568
The char-rp seat shipped with no tool-call parser at all, so every
tools-bearing request was rejected outright:
400 "auto" tool choice requires --enable-auto-tool-choice and
--tool-call-parser to be set
MeroMero-v2 is Gemma-4, which emits its own native
`<|tool_call>call:name{...}<tool_call|>` syntax rather than the
qwen3_coder XML the Qwen-family seats use. vLLM 0.24 ships a matching
`gemma4` parser whose TOOL_CALL_START/END, CHANNEL_START/END and escape
token constants line up with this tokenizer's etc/eoc/escape tokens
exactly.
Three flags, and they are a set:
- --tool-call-parser gemma4 + --enable-auto-tool-choice: the fix proper.
- --reasoning-parser gemma4: without it the post-tool-response turn
leaks a literal `<|channel>thought\n<channel|>` prefix into content
(upstream vllm #45834 — the chat template leaves the prompt inside an
open channel block).
- --default-chat-template-kwargs '{"enable_thinking": false}': MANDATORY
companion to the reasoning parser. The parser reads enable_thinking
from chat_template_kwargs and defaults it to True
(vllm/parser/gemma4.py:439); True makes is_reasoning_end() return
False at a new turn, pre-initialising the engine to REASONING, which
routes ALL plain RP prose into reasoning_content and returns a null
content — breaking every char-rp consumer. This template already
defaults enable_thinking to false (chat_template.jinja:350), so
passing it explicitly renders a byte-identical prompt (verified across
plain / tools / post-tool-response / system-prompt shapes). It changes
generation not at all; it only corrects the parser state machine.
Verified green on the live seat after deploy: tool call streaming and
non-streaming, tool-result round-trip (leak gone), plain prose in
content with reasoning null, vision unchanged.
47 lines
2.8 KiB
Markdown
47 lines
2.8 KiB
Markdown
# meromero-charrp — MeroMero-v2 char-rp prose seat (ana-ml2 GPU0)
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The **non-thinking, multimodal** RP prose seat. Serves the LiteLLM `char-rp` alias.
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- **Model:** `G4-MeroMero-v2-31B-NVFP4A16` (Gemma-4-31B, home-quantized weight-only NVFP4A16).
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- **Host/GPU:** ana-ml2, GPU0 (co-located with `gen` / vllm-aeon-gen).
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- **Port:** :8016 → LiteLLM `char-rp`.
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- **Context:** 256K (`--max-model-len 262144`). Gemma-4 uses sliding-window attention → KV-efficient, ~2× concurrency at full context.
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- **Vision:** enabled (image + text). `preprocessor_config.json` was materialized from the model's own `processor_config.json` (`Gemma4ImageProcessor`); audio is config-declared but weightless.
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- **Tool-calling:** enabled via the `gemma4` parser (**not** `qwen3_coder` — that's the Qwen-family XML the other seats use). Gemma-4 emits its own native `<|tool_call>call:name{...}<tool_call|>` syntax.
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## Tool-calling — the three flags are a set, don't split them
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```yaml
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- --tool-call-parser gemma4 # native <|tool_call> syntax; without it ANY tools request 400s
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- --enable-auto-tool-choice
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- --reasoning-parser gemma4 # absorbs the <|channel>…<channel|> thought markers
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- --default-chat-template-kwargs '{"enable_thinking": false}' # MANDATORY, see below
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```
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Why the last one is mandatory: the gemma4 parser reads `enable_thinking` out of
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`chat_template_kwargs` and **defaults it to `True`** (`vllm/parser/gemma4.py:439`). With `True`,
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`is_reasoning_end()` returns `False` at a new turn, which pre-initialises the parser engine to
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`REASONING` — so **all plain RP prose lands in `reasoning_content` and `content` comes back
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`null`**, breaking every `char-rp` consumer. This model's `chat_template.jinja:350` already
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defaults `enable_thinking` to `false`, so passing it explicitly renders a **byte-identical
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prompt** (verified across plain / tools / post-tool-response / system-prompt shapes) — it changes
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nothing about generation, it only corrects the parser's state machine.
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Without `--reasoning-parser gemma4`, the post-tool-response turn leaks a literal
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`<|channel>thought\n<channel|>` prefix into `content` (upstream vllm #45834 — the chat template
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leaves the prompt sitting inside an open channel block).
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Verified green after the fix: tool call (streaming + non-streaming), tool-result round-trip,
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plain prose in `content`, vision.
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- **Tuning:** `.env` — `MEROMERO_GPU_MEM_UTIL=0.52` (leaves ~4.6 GB GPU0 headroom), `MEROMERO_MAX_MODEL_LEN=262144`, `MEROMERO_GPU_ID=0`.
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Replaces the retired **char-rp-gguf** (Magidonia-24B GGUF / llama.cpp) seat. The quant pipeline
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lives in `ana-ml2:/tank/aimodels/meromero-v2-nvfp4-work/`.
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## Deploy
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```bash
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scripts/deploy-stack.sh ana-ml2 meromero-charrp # diffs vs live, prompts y/N
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# on host: cp .env.example .env; docker compose up -d
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```
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