SERVING.md

Server-mode OCR: official support per model + the -server.py recipes

The recipes in this folder come in two shapes:

• Offline batch (<model>.py): the script owns the vLLM engine (LLM.generate) and processes the dataset in fixed-size batches. One hf jobs uv run command.
• Server + driver (<model>-server.py): one job starts vllm serve in the background, then a lightweight driver (no torch/vllm deps — installs in seconds) posts images concurrently to the OpenAI-compatible endpoint on localhost.

Why server mode? Two measured reasons (100-page historical-scan A/B on l4x1, same data, same sampling, driver concurrency 32):

Model	Offline (inference-only)	Server	Speedup	Output parity
OvisOCR2 0.9B	0.83 img/s	1.44 img/s	~1.7×	94/100 byte-identical, rest ≥0.978 similar
LightOnOCR-2 1B	0.33 img/s	0.59 img/s	~1.8×	64/100 byte-identical at temp 0.2, rest median 0.998
Nanonets-OCR2 3B	0.31 img/s (steady-state)	0.36 img/s	~1.2×	52/100 byte-identical, median 0.999; 3 hard plate pages fork under greedy (worst case was the offline arm degenerating into a 22k-char repetition loop)

1. Throughput: offline llm.generate drains at every batch boundary (GPU idles while the CPU decodes the next batch of images); a concurrent driver keeps vLLM's continuous batching fed. The speedup is a floor, not a ceiling — at concurrency 32 the server's KV cache was <10% used.
2. Failure isolation: offline, one bad image (e.g. a None cell) fails its whole batch of 16; server mode fails that one request. On a real dataset where ~half the image cells were empty, the offline recipe produced 0 usable outputs and the server recipe produced all of the valid ones.

The job command stays the standard hf jobs uv run shape: the driver spawns vllm serve itself as a subprocess when no server is reachable (flags live in the script's SERVE_ARGS, taken from the model's own card where they exist), so the only thing to get right is --image vllm/vllm-openai:<tag> — which provides the vllm binary. Without it the script fails fast, printing the exact correct command. Pass --server URL to use an already-running or remote endpoint instead (nothing is spawned when the server is reachable, so the moss-style explicit bash -c serve command keeps working too).

For interactive/agent use (a live endpoint instead of a batch run), see serving-unlimited-ocr.md — hf jobs run --expose gives an OpenAI-compatible URL that outlives a single script.

The recurring official serve pattern for OCR

Three flags recur across independent vendors' official serve commands (DeepSeek's vLLM recipe, LightOn's card, Paddle's vLLM recipe, Unlimited-OCR's recipe):

--no-enable-prefix-caching --mm-processor-cache-gb 0 --limit-mm-per-prompt '{"image": 1}'

OCR workloads never reuse images, so prefix/multimodal caches only cost memory.

Version pins carry over — via the image tag

Where an offline recipe pins an engine version, the server variant needs the same pin as a vllm/vllm-openai:<tag> image (e.g. Nanonets-OCR2-3B uses v0.10.2, matching its offline recipe). models.json records the required image per script. When trying a new model/image combination, sanity-check the first few outputs before scaling: a mismatched combination can produce degenerate output while looking like normal load from the outside (full GPU utilisation, no errors).

Which models document server mode themselves? (surveyed 2026-07-16)

Server mode is the officially documented path for most models in this collection — for several of them it's the only documented vLLM path. Summary of each model's own card/docs (verbatim commands live in the linked sources):

Model	Official server example	Notes
lightonai/LightOnOCR-1B / 2-1B	✅ vLLM	Card ships the serve command + client; ≥0.11.1 for v1; images longest-dim 1540px
nanonets/OCR-s / OCR2-3B	✅ vLLM	Bare vllm serve + client in card; pin v0.10.2 for OCR2 (see above)
rednote-hilab/dots.mocr	✅ vLLM ≥0.11	Direct from Hub id on vllm/vllm-openai:v0.11.0+
rednote-hilab/dots.ocr	✅ (GitHub)	HF card shows a legacy pre-0.11 hack; GitHub README: integrated upstream since 0.11
tencent/HunyuanOCR	✅ vLLM 0.18.1	serve.sh in repo; nightly adds DFlash speculative decoding
zai-org/GLM-OCR	✅ vLLM + SGLang + Ollama	Only card here with an SGLang serve example; needs vLLM nightly
numind/NuExtract3	✅ vLLM	Production-grade recipe: MTP speculative decoding, per-request chat_template_kwargs
numind/NuMarkdown-8B-Thinking	✅ vLLM	Thinking always on — parse <think>/<answer>
baidu/Unlimited-OCR	✅ vLLM + SGLang	Custom image / dev wheel; see serving-unlimited-ocr.md
baidu/Qianfan-OCR	✅ vLLM (minimal)	Needs --hf-overrides '{"architectures": ["InternVLChatModel"]}'
PaddlePaddle/PaddleOCR-VL 1.x	✅ paddle genai_server + vLLM recipe	Serves the 0.9B VLM only; raw serve skips the layout stage (official quality warning)
deepseek-ai/DeepSeek-OCR / -2	⚠️ vLLM recipe pages only	docs.vllm.ai recipes; needs the DeepSeek n-gram logits processor flags
allenai/olmOCR-2-7B-FP8	✅ (GitHub)	olmocr toolkit spawns vllm serve itself; YAML-front-matter prompt required
reducto/RolmOCR	✅ vLLM	Card serve + client (client's model string has a typo — pass --served-model-name)
tiiuae/Falcon-OCR	✅ vLLM in official Docker	ghcr.io/tiiuae/falcon-ocr; task-token prompts
datalab-to/surya-ocr-2, lift	⚠️ wrapper-mediated	Server-native but driven via their own managers (SURYA_INFERENCE_URL, lift_vllm)
LiquidAI LFM2.5-VL-Extract	⚠️ family docs	vLLM ≥0.23 + SGLang cookbooks, not Extract-specific
ATH-MaaS/OvisOCR2	❌ offline vLLM only	ovis-ocr2-server.py is our translation of the card's offline args (parity-validated, table above)
FireRedTeam/FireRed-OCR	❌ transformers only
acvlab/ABot-OCR	❌ offline script only	pinned vllm 0.18.0

SGLang reality check: only GLM-OCR and Unlimited-OCR ship SGLang serve paths (olmOCR dropped SGLang for vLLM in v0.1.75). For this collection, "server mode" effectively means a vLLM OpenAI-compatible endpoint.