OPO-8B-GGUF

OPO-8B is a Qwen3-VL-8B-Instruct model trained with Observation Policy Optimization (OPO), introduced in the NeurIPS 2026 paper β€œLearning Where to Look: Observation Policy Optimization for Thinking with Images.” It learns to select image regions for closer inspection through uncertainty-guided branching, evidence-localized advantage attribution, and multi-turn zooming. On high-resolution perception benchmarks, it scores 93.19 on V* (93.04 attribute, 93.42 spatial), 82.25 on HR-Bench 4K (94.00 FSP, 70.50 FCP), and 82.25 on HR-Bench 8K (91.00 FSP, 73.50 FCP). Compared with the Qwen3-VL-8B zoom baseline, it improves by 5.2 points on V*, 0.9 on 4K, and 5.8 on 8K, outperforming several other 7B–8B methods. It remains competitive with larger models, though some top systems achieve higher scores. Evaluation uses the OPO repository with up to 10 turns; the model card does not specify a license.

Model Files

File Name Quant Type File Size File Link Description
OPO-8B.BF16.gguf BF16 16.4 GB Link Full BF16 weights. Highest quality, largest file size.
OPO-8B.Q3_K_L.gguf Q3_K_L 4.43 GB Link Lower quality but usable, good for low RAM availability.
OPO-8B.Q3_K_M.gguf Q3_K_M 4.12 GB Link Low quality.
OPO-8B.Q4_K_M.gguf Q4_K_M 5.03 GB Link Good quality, default size for most use cases, recommended.
OPO-8B.Q4_K_S.gguf Q4_K_S 4.8 GB Link Slightly lower quality with more space savings, recommended.
OPO-8B.Q5_K_M.gguf Q5_K_M 5.85 GB Link High quality, recommended.
OPO-8B.Q5_K_S.gguf Q5_K_S 5.72 GB Link High quality, recommended.
OPO-8B.Q6_K.gguf Q6_K 6.73 GB Link Very high quality, near perfect, recommended.
OPO-8B.Q8_0.gguf Q8_0 8.71 GB Link Extremely high quality, generally unneeded but max available quant.
OPO-8B.mmproj-bf16.gguf mmproj-bf16 1.16 GB Link Multimodal projection file in BF16 format. Used for vision/language models.

llama.cpp

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

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GGUF
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qwen3vl
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