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ArmorOCR-GGUF

ArmorOCR is a two-stage framework for grounded adversarial OCR perception from Ant Group's inclusionAI, built on Qwen3-VL-8B-Instruct and introduced in the paper "ArmorOCR: Grounded Adversarial Visual Perception via Observation-Transferred Self-Distillation." Its key design goal is robustness to adversarial or degraded visual conditions while enabling single-pass inference directly on the original image, without requiring any inference-time visual transformations (such as cropping, upscaling, or denoising) or external tool assistance. The model follows a structured reasoning-then-answer output format, wrapping its analysis in <analyze></analyze> tags and the final recognized text in <answer></answer> tags, and is loaded via the standard Qwen3VLForConditionalGeneration class in Transformers. Training details, the accompanying AdvSpot adversarial OCR benchmark, and evaluation scripts are hosted separately on the project's GitHub repository, and the model is released under Apache License 2.0, with usage additionally subject to the base Qwen3-VL-8B-Instruct model's license and acceptable-use policy.

Model Files

File Name Quant Type File Size File Link
ArmorOCR.BF16.gguf BF16 16.4 GB Download
ArmorOCR.F16.gguf F16 16.4 GB Download
ArmorOCR.Q2_K.gguf Q2_K 3.28 GB Download
ArmorOCR.Q3_K_L.gguf Q3_K_L 4.43 GB Download
ArmorOCR.Q3_K_M.gguf Q3_K_M 4.12 GB Download
ArmorOCR.Q3_K_S.gguf Q3_K_S 3.77 GB Download
ArmorOCR.Q4_0.gguf Q4_0 4.77 GB Download
ArmorOCR.Q4_K_M.gguf Q4_K_M 5.03 GB Download
ArmorOCR.Q4_K_S.gguf Q4_K_S 4.8 GB Download
ArmorOCR.Q5_0.gguf Q5_0 5.72 GB Download
ArmorOCR.Q5_K_M.gguf Q5_K_M 5.85 GB Download
ArmorOCR.Q5_K_S.gguf Q5_K_S 5.72 GB Download
ArmorOCR.Q6_K.gguf Q6_K 6.73 GB Download
ArmorOCR.Q8_0.gguf Q8_0 8.71 GB Download
ArmorOCR.mmproj-bf16.gguf mmproj-bf16 1.16 GB Download
ArmorOCR.mmproj-f16.gguf mmproj-f16 1.16 GB Download
ArmorOCR.mmproj-q8_0.gguf mmproj-q8_0 752 MB Download

llama.cpp

LLM inference in C/C++ — https://github.com/ggml-org/llama.cpp

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GGUF
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qwen3vl
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