[ShaiShmuel]

Adds xiaomi-research/Xiaomi-OCR-0 (Apache-2.0, 0.87B, Qwen3.5-0.8B based, Qwen3_5ForConditionalGeneration) to the catalog, pinned to bbd8cf8d. The entry mirrors GLM-OCR:

• default: SGLang vision extract with native SGLang Qwen3.5 support and trust_remote_code: false. It uses the model card prompts (document is the default task; text, table and formula are also available).
• :transformers: the plain HF reference path with the same document prompt.

YAML plus config tests only; no adapter changes.

Quality was measured with sie_bench on L4. IAM-line ?limit=1550 scored 0.956152 vanilla vs 0.956288 SIE, a 0.014% gap (gate is 2%). SIE on the full split scored 0.945115. The full table and commands are in the PR comment.
Companion sie-internal PR adds the floor (link to follow).

  

Summary by CodeRabbit

• New Features
  • Added support for Xiaomi Research’s Xiaomi-OCR-0 for image-based text extraction.
  • The model can parse document pages and extract text, tables, and formulas. It is available through both Transformers and SGLang inference options, with document parsing as the default task.
  • The model accepts image inputs and is configured to produce up to 4,096 new tokens per response.

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Reviewing files that changed from the base of the PR and between 466e2e6 and 698cf0c.

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• packages/sie_server/models/xiaomi-research__Xiaomi-OCR-0.yaml

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• packages/sie_server/models/xiaomi-research__Xiaomi-OCR-0.yaml

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Walkthrough

Adds Xiaomi-OCR-0 model configuration with Transformers and SGLang profiles. Adds tests for profile settings, token budgets, memory reservation, and OCR bundle routing.

Changes: Xiaomi-OCR-0 configuration

Layer / File(s)	Summary
Model profiles packages/sie_server/models/xiaomi-research__Xiaomi-OCR-0.yaml	Adds model metadata and image extraction settings. Defines Transformers and default SGLang vision-extraction profiles with prompts, generation settings, and runtime limits.
Profile and routing coverage packages/sie_server/tests/config/test_xiaomi_ocr_profiles.py, packages/sie_server/tests/config/test_bundle_coverage.py	Adds checks for profile adapters, prompts, decoding settings, launch arguments, token budgets, memory reservation, concurrency, and OCR bundle routing.

Possibly related PRs

• superlinked/sie#866: Changes the shared SGLang image path used by Xiaomi-OCR-0’s vision-extraction adapter.
• superlinked/sie#878: Updates the transformers5 dependency used by the Xiaomi-OCR-0 Transformers profile.
• superlinked/sie#853: Changes SGLang OCR memory reservation and tests specialized reservations.

Suggested reviewers: krisztian-gajdar

Merge Risk: Minimal · up to 698cf

No actionable issue remains from this review; the change is ready for normal merge checks.

Pre-merge checks: 4 · 1

Failed checks: 1 warning

Check name	Status	Explanation	Resolution
Docstring Coverage		Docstring coverage is 0.00% which is insufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff with cyclomatic complexity of at least 2. Documentat…	Write docstrings for the functions missing them to satisfy the coverage threshold.

Passed checks: 4 passed

Check name	Status	Explanation
Description Check		Check skipped - CodeRabbit’s high-level summary is enabled.
Title check		The title clearly and concisely identifies the main change: adding Xiaomi-OCR-0 to the model catalog.
Linked Issues check		Check skipped because no linked issues were found for this pull request.
Out of Scope Changes check		Check skipped because no linked issues were found for this pull request.

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Resolution

Write docstrings for the functions missing them to satisfy the coverage threshold.

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Generate unit tests (beta)

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[cursor]

Xiaomi-OCR-0: vanilla vs SIE (sie_bench, L4)

Model: xiaomi-research/Xiaomi-OCR-0 @ bbd8cf8d21f256b3644b44cb860f3a7cecb93c30 (Apache-2.0, 0.87B, Qwen3_5ForConditionalGeneration, no remote code).
Change type: YAML only (no adapter changes). Both profiles use the model card's document prompt, bf16, greedy decoding, max_new_tokens=4096.

• Vanilla = :transformers profile: plain HF reference (GlmOcrAdapter = AutoProcessor + AutoModelForImageTextToText, sdpa), transformers 5.19.0.
• SIE = default profile: SGLangVisionExtractAdapter, sglang 0.5.10.post1, trust_remote_code: false.

Primary metric: quality (= mean_similarity = 1 - mean normalized Levenshtein).

Dataset	n	Vanilla	SIE	abs(SIE-vanilla)/vanilla	Gate (<= 2%)
ocr/Teklia/IAM-line?limit=100	100	0.945998	0.946288	0.031%	pass
ocr/Teklia/IAM-line?limit=1550 (gating)	1550	0.956152	0.956288	0.014%	pass
ocr/Teklia/IAM-line (full test split)	2915	not run (see below)	0.945115	n/a	n/a

For context, the GLM-OCR floor on the full IAM split is 0.944361.

Subset note: the vanilla HF path runs at about 0.55 s per line on an L4, plus warmup. The full 2915-line split did not fit in the 3600 s GPU cap alongside the SIE runs. The gate therefore uses the largest prefix that fit, ?limit=1550, chosen automatically from a 100-line timing probe. SIE was also run on the full split.

Floor minted (sie-internal), taken from the vanilla run:
benchmarks/ocr__Teklia__IAM-line?limit=1550/xiaomi-research__Xiaomi-OCR-0/quality/target.extract.json

Commands

Each container runs one SIE server from the public ghcr.io/superlinked/sie-server:v0.10.1-cuda12-{transformers5,sglang-vision-extract} images, with this branch's sie_server/sie_sdk source, bundles and models copied in:

python -m sie_server.cli serve --host 127.0.0.1 --port 8080 --models-dir /app/models \
  --models <route> --preload <route>

Here <route> is xiaomi-research/Xiaomi-OCR-0:transformers for vanilla and xiaomi-research/Xiaomi-OCR-0 for SIE. sie_bench (sie-internal @ c049d51c) then runs against that server (SIE_BENCH_OCR_CONCURRENCY=8):

# vanilla
sie-bench eval xiaomi-research/Xiaomi-OCR-0 -t "ocr/Teklia/IAM-line?limit=1550" --type quality \
  -s http://127.0.0.1:8080 --profile transformers --models-dir /app/models -o json --output-file vanilla.json
# SIE
sie-bench eval xiaomi-research/Xiaomi-OCR-0 -t "ocr/Teklia/IAM-line?limit=1550" --type quality \
  -s http://127.0.0.1:8080 --models-dir /app/models -o json --output-file sie.json

The same commands were also run with ?limit=100, and with the full split for SIE.

GPU (L4, wall clock per Modal call)

Call	Seconds
SIE: 2 failed starts (CLI flag / image overlay, fixed)	38.5
SIE: ?limit=20 smoke + full split (server ready in 213 s)	563.2
Vanilla: ?limit=100 probe + ?limit=1550 (server ready in 45 s)	1036.7
SIE: ?limit=100 + ?limit=1550	451.6
Total	2090.0 / 3600

Deviations

• The card example uses fp16. Both profiles use bf16, which is the checkpoint's own dtype; the reference adapter rejects fp16 on CUDA.
• The reference profile includes SIE's load-time rebind_vision_patch_embed. It is self-validated and mathematically identical to the conv.

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Review comments at
@packages/sie_server/models/xiaomi-research__Xiaomi-OCR-0.yaml:
- Line 13: Update the max_sequence_length profile or configure the per-image
pixel limit through image.max_pixels or SIE_SGLANG_SINGLE_IMAGE_MAX_PIXELS so
full-page visual tokens, the 4,096-token output allowance, and prompt tokens fit
within the sequence window; validate GPU memory if increasing the window or page
detail if lowering the pixel limit.

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