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cstr/deepseek-ocr2-crispembed-GGUF overview

DeepSeek OCR2 — CrispEmbed GGUF GGUF conversion of deepseek ai/DeepSeek OCR 2 https://huggingface.co/deepseek ai/DeepSeek OCR 2 for use with CrispEmbed https:/…

ggufocrdocument-ocrcrispembedmoelicense:apache-2.0region:us

Runs locally from ~2.15 GB disk (4 GB VRAM class GPUs with llama.cpp / guIDE).

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Repository Files & Downloads

5 GGUF files detected
Direct downloads for local inference
FileTypeQuantizationSizeLink
deepseek-ocr2-f16-stacked.ggufGGUFF166.32 GBDownload
deepseek-ocr2-f16.ggufGGUFF166.32 GBDownload
deepseek-ocr2-q4_k-stacked.ggufGGUFQ4_K2.15 GBDownload
deepseek-ocr2-q4_k.ggufGGUFQ4_K2.15 GBDownload
deepseek-ocr2-q8_0.ggufGGUFQ8_03.36 GBDownload

Model Details

Model IDcstr/deepseek-ocr2-crispembed-GGUF
Authorcstr
Pipeline
Licenseapache-2.0
Base model
Last modified2026-08-02T15:21:34.000Z

Model README

---

license: apache-2.0

tags:

- gguf

- ocr

- document-ocr

- crispembed

- moe

---

DeepSeek-OCR2 — CrispEmbed GGUF

GGUF conversion of deepseek-ai/DeepSeek-OCR-2 for use with CrispEmbed.

Architecture

SAM-ViT-B (12L, 768d) → Qwen2 encoder (24L, 896d, bidirectional) → Linear projector (896→1280) → DeepSeek-V2 MoE decoder (12L, 1280d, 64 experts top-6 + 2 shared, layer 0 dense) → lm_head

Models

| File | Quant | Size | Description |

|------|-------|------|-------------|

| deepseek-ocr2-f16.gguf | F16 | 6.4 GB | Full precision |

| deepseek-ocr2-q8_0.gguf | Q8_0 | ~3.4 GB | Best quality/size balance |

| deepseek-ocr2-q4_k.gguf | Q4_K | ~2.0 GB | Smallest, good quality |

Performance features

  • Per-row embedding dequant (saves ~655 MB peak RSS vs full table expansion)
  • MoE decoder on Metal via ggml_mul_mat_id
  • SAM patch-embed + neck on Metal via ggml_conv_2d
  • Qwen2 encoder on Metal graph

Converted with models/convert-deepseek-ocr2-to-gguf.py from CrispEmbed.

Provenance and EU AI Act Art. 53 note

  • Upstream model: deepseek-ai/DeepSeek-OCR-2 — published by deepseek-ai.
  • Upstream licence: apache-2.0. This repository redistributes under the same terms; it grants no rights the upstream licence does not.
  • What was done here: format conversion and/or quantisation only (GGUF). No training, no fine-tuning, no merging, no distillation, no change to architecture, vocabulary or capability. Only the numeric representation of the upstream weights differs.
  • Training data: documented — where it is documented at all — by the upstream provider; see the upstream model card. No training data was used, added or selected by this repository. No training-content summary was found on the upstream model card at the time of writing; that documentation gap is upstream's and is not filled here.
  • Provider status: under Regulation (EU) 2024/1689 the upstream authors remain the provider of this model. Converting the serialisation format does not make this repository the provider of a new general-purpose AI model, and no such claim is made. Questions about training content, copyright policy or model capability belong upstream.

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