--- title: MiniCPM-OCR-1.0-gguf canonical_url: "https://www.modelscope.cn/models/OpenBMB/MiniCPM-OCR-1.0-gguf" md_url: "https://www.modelscope.cn/models/OpenBMB/MiniCPM-OCR-1.0-gguf.md" repository: OpenBMB/MiniCPM-OCR-1.0-gguf last_updated: 2026-10-27 license: apache-2.0 pipeline_tag: image-text-to-text tasks: - image-text-to-text base_model: - openbmb/MiniCPM-OCR-1.0 base_model_relation: quantized library_name: - gguf language: - en - zh downloads: 45 stars: 0 tags: - minicpm-ocr - ocr - document-parsing - gguf - llama.cpp - multimodal - quantized --- # MiniCPM-OCR-1.0-gguf > MiniCPM-OCR-1.0-gguf - OpenBMB 在 ModelScope 开源的模型。GitHub · API · Demo · DSpark OpenBMB/MiniCPM-OCR-1.0-gguf 是 ModelScope 魔搭社区上的image-text-to-text模型，采用 apache-2.0 许可，基于 openbmb/MiniCPM-OCR-1.0 构建。 - **Repository**: OpenBMB/MiniCPM-OCR-1.0-gguf - **License**: apache-2.0 - **Tasks**: image-text-to-text - **Base model**: openbmb/MiniCPM-OCR-1.0 - **Tags**: minicpm-ocr, ocr, document-parsing, gguf, llama.cpp, multimodal, quantized - **Downloads**: 45 - **Stars**: 0 - **Last updated**: 2026-10-27 Source: https://www.modelscope.cn/models/OpenBMB/MiniCPM-OCR-1.0-gguf ---
[GitHub](https://github.com/OpenBMB/MiniCPM-OCR) · [API](https://github.com/OpenBMB/MiniCPM-OCR/blob/main/docs/minicpm_ocr_api_en.md) · [Demo](https://platform.modelbest.ai/console/playground/document-parse) · [DSpark](https://huggingface.co/openbmb/MiniCPM-OCR-1.0-DSpark)
> **This repository hosts the GGUF build of [MiniCPM-OCR 1.0](https://huggingface.co/openbmb/MiniCPM-OCR-1.0) for > [llama.cpp](https://github.com/ggml-org/llama.cpp).** For the original weights, the full model > card, and benchmarks, see the base repository. **MiniCPM-OCR 1.0** (1.3B) parses documents into text, tables, formulas, charts, and seals, and supports key information extraction, for Chinese and English documents and PDFs. ## Files Every tier needs the vision projector as well — `--model` takes the LLM GGUF, `--mmproj` takes the projector: | File | Size | Notes | | :--- | ---: | :--- | | `mmproj-model-f16-4x.gguf` | 0.901 GB | vision projector, **4× downsample** — required | | `MiniCPM-OCR-1_0-F16.gguf` | 1.516 GB | reference, no quantization | | `MiniCPM-OCR-1_0-Q8_0.gguf` | 0.812 GB | near-lossless | | `MiniCPM-OCR-1_0-Q6_K.gguf` | 0.630 GB | good quality/size balance | | `MiniCPM-OCR-1_0-Q5_K_M.gguf` | 0.578 GB | smaller, still strong | | `MiniCPM-OCR-1_0-Q5_K_S.gguf` | 0.563 GB | | | `MiniCPM-OCR-1_0-Q5_1.gguf` | 0.595 GB | | | `MiniCPM-OCR-1_0-Q5_0.gguf` | 0.563 GB | | | `MiniCPM-OCR-1_0-Q4_K_M.gguf` | 0.529 GB | smallest recommended | | `MiniCPM-OCR-1_0-Q4_K_S.gguf` | 0.505 GB | | | `MiniCPM-OCR-1_0-Q4_1.gguf` | 0.532 GB | | | `MiniCPM-OCR-1_0-Q4_0.gguf` | 0.501 GB | | The projector is 4× only; there is no 16× variant for this model. ### Which tier | Tier | Table / dense text / XML | | :--- | :--- | | F16 | baseline | | Q8_0 | lossless / ≈lossless — recommended balance | | Q4_K_M | lossless on tables, small degradation on dense text; closing-tag slashes may drop in code-heavy documents | Prefer Q8_0 for code- or XML-heavy pages. ## Usage ```bash llama-server \ --model ./MiniCPM-OCR-1_0-Q8_0.gguf \ --mmproj ./mmproj-model-f16-4x.gguf \ --host 0.0.0.0 --port 18499 \ --ctx-size 40960 \ --n-gpu-layers 999 ``` `--n-gpu-layers 999` offloads everything to GPU; use `0` for CPU-only. ```bash llama-mtmd-cli \ -m ./MiniCPM-OCR-1_0-Q8_0.gguf \ --mmproj ./mmproj-model-f16-4x.gguf \ --image page.png -p "Text Recognition:" \ --jinja -ngl 99 -c 8192 --temp 0 ``` **Thinking is already disabled inside the embedded chat template** — no extra flag is needed, and `enable_thinking` cannot turn it back on. (If you convert the weights yourself instead of using these files, start the server with `--chat-template-kwargs '{"enable_thinking": false}'`.) ### Prompts | Region | Prompt | | :--- | :--- | | Text block / paragraph / title / page | `Text Recognition:` | | Table | `Table Recognition:` | | Display formula | `Formula Recognition:` | ### SDK with a llama.cpp backend ```bash minicpmvocr parse --config sdk/minicpmvocr/configs/minicpmvocr.yaml \ --backend llamacpp --server-url http://127.0.0.1:18499 \ --model minicpm-ocr \ --set layout.device=cuda:0 \ --set pipeline.queue_mode=serial \ --set pipeline.max_workers=1 \ --input doc.pdf --output doc.md ``` `llama-server` handles one request at a time by default, so keep `queue_mode=serial` and `max_workers=1`. ## Performance Single RTX 5090 (32 GB), F16 weights: model + projector load ≈2.3 s; one table crop (1,312 prompt / 265 output tokens) ≈1.0 s; VRAM ≈26 GB at `--ctx-size 40960`. CPU-only (48 threads): one table image end-to-end (layout + recognition + assembly) ≈21 s. Build llama.cpp and further options: [llama.cpp deployment guide](https://github.com/OpenBMB/MiniCPM-OCR/blob/main/docs/llamacpp_deploy.md). ## Related checkpoints | Format | Hugging Face | ModelScope | | :--- | :--- | :--- | | BF16 (original) | [🤗](https://huggingface.co/openbmb/MiniCPM-OCR-1.0) | [ModelScope](https://modelscope.cn/models/OpenBMB/MiniCPM-OCR-1.0) | | GGUF (llama.cpp) **(this repo)** | [🤗](https://huggingface.co/openbmb/MiniCPM-OCR-1.0-gguf) | [ModelScope](https://modelscope.cn/models/OpenBMB/MiniCPM-OCR-1.0-gguf) | | AWQ W4A16 | [🤗](https://huggingface.co/openbmb/MiniCPM-OCR-1.0-AWQ) | [ModelScope](https://modelscope.cn/models/OpenBMB/MiniCPM-OCR-1.0-AWQ) | | GPTQ W4A16 | [🤗](https://huggingface.co/openbmb/MiniCPM-OCR-1.0-GPTQ) | [ModelScope](https://modelscope.cn/models/OpenBMB/MiniCPM-OCR-1.0-GPTQ) | | DSpark draft (BF16) | [🤗](https://huggingface.co/openbmb/MiniCPM-OCR-1.0-DSpark) | [ModelScope](https://modelscope.cn/models/OpenBMB/MiniCPM-OCR-1.0-DSpark) | | DSpark draft (GGUF) | [🤗](https://huggingface.co/openbmb/MiniCPM-OCR-1.0-DSpark-gguf) | [ModelScope](https://modelscope.cn/models/OpenBMB/MiniCPM-OCR-1.0-DSpark-gguf) | Speculative decoding works in llama.cpp with the [DSpark draft GGUF](https://huggingface.co/openbmb/MiniCPM-OCR-1.0-DSpark-gguf) via `--spec-type draft-dspark`. ## FAQ Installation, model downloads, GPU memory, input formats, and unexpected outputs: [FAQ](https://github.com/OpenBMB/MiniCPM-OCR/blob/main/docs/faq_en.md). ## License Model weights, code, and documentation are released under the [Apache License 2.0](https://github.com/OpenBMB/MiniCPM-OCR/blob/main/LICENSE).