cstr/deepseek-ocr2-crispembed-GGUF

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

Performance features

• —Per-row embedding dequant (saves ~655 MB peak RSS vs full table expansion)
• —MoE decoder on Metal via ggmlmulmat_id
• —SAM patch-embed + neck on Metal via ggmlconv2d
• —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.