MiniCPM-OCR-1.0-DSpark

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Demo (CN / Global)  ·  GitHub  ·  SDK usage  ·  DSpark  ·  Discord  |  中文

MiniCPM-OCR 1.0 DSpark#

This repository hosts the DSpark draft (speculative decoding) head for MiniCPM-OCR 1.0. It is a draft model, not a standalone OCR model — it must be paired with the matching main checkpoint.

DSpark speeds up decoding with a small draft model that proposes tokens the main model then verifies in one pass. With this head, MiniCPM-OCR 1.0 improves end-to-end processing efficiency by 55%; throughput reaches about 1.49× PaddleOCR-VL-1.6 and 3.62× MinerU2.5 Pro.

Specification#

The output LM head and the Markov output rows are cropped to 114,688 tokens. The checkpoint keeps its d2t offset mapping and the full input vocabulary; local-to-target.json holds the corresponding absolute target token IDs.

Note on d2t: in this checkpoint the tensor is delta-encoded (target_id - draft_id). It must be converted to absolute IDs (d2t[i] + i) before use — the GGUF build already does this, and local-to-target.json is the ground truth to verify against.

Parsing pipeline#

MiniCPM-OCR 1.0 parses full documents as a two-stage pipeline orchestrated by the minicpm-ocr SDK. The two stages use separate models, run in separate environments, and may run on different machines:

• Stage 1 — Layout detection (SDK environment). The PP-DocLayoutV3 layout model detects document regions — text, tables, formulas, charts, seals — and determines reading order; the SDK crops each region. It runs locally inside the SDK environment and requires PaddlePaddle / PaddleX.
• Stage 2 — Region recognition (inference service). The SDK sends each cropped region over HTTP to a MiniCPM-OCR 1.0 inference service — served by vLLM, SGLang, llama.cpp, or Ollama — with a prompt matching the region type, then assembles the recognized content into the final Markdown / JSON in reading order.

Image: Parsing pipeline: in the minicpm-ocr SDK environment, the PP-DocLayoutV3 layout model detects and crops document regions; each cropped region is sent over HTTP to a separate recognition service running MiniCPM-OCR 1.0 (vLLM, SGLang, llama.cpp, or Ollama); the SDK then assembles the recognized text, tables, formulas, charts, and seals into Markdown and JSON in reading order.

See the model guide for more on architecture, parsing workflow, deployment options, and benchmark breakdowns.

Usage with vLLM#

vllm serve openbmb/MiniCPM-OCR-1.0 \
  --served-model-name MiniCPM-OCR \
  --trust-remote-code \
  --max-model-len 40960 \
  --max-num-batched-tokens 40960 \
  --max-num-seqs 1 \
  --trust-request-mm-kwargs \
  --mm-processor-kwargs '{"downsample_mode":"4x","max_slice_nums":9}' \
  --speculative-config '{"method":"dspark",
                         "model":"openbmb/MiniCPM-OCR-1.0-DSpark",
                         "num_speculative_tokens":15,
                         "enable_adaptive_verification":false}' \
  --default-chat-template-kwargs '{"enable_thinking":false}'

Selection and tuning: DSpark acceleration.

Related checkpoints#

DSpark requires a matching main and draft model from the same release; do not pair this draft with a different main checkpoint.

FAQ#

Installation, model downloads, GPU memory, input formats, and unexpected outputs: FAQ.

License#

Model weights, code, and documentation are released under the Apache License 2.0.