mradermacher/Qwen3-32B-Uncensored-i1-GGUF
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bartowski/huihui-ai_Huihui-gpt-oss-20b-BF16-abliterated-GGUF:This won't run in Ollama
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gabriellarson/Huihui-gpt-oss-20b-BF16-abliterated-GGUF:Compatible with ollama?
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mradermacher/Qwen3-32B-Uncensored-i1-GGUF
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This won't run in Ollama
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#1 opened 8 days ago
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tebal91901
Compatible with ollama?
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#2 opened 8 days ago
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Marioher
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OpenThinker2-32 Abliterated?
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#837 opened 4 months ago
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tebal91901
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New foundation model on image and video captioning just dropped by NVIDIA AI 🔥
Describe Anything Model (DAM) is a 3B vision language model to generate detailed captions with localized references 😮
The team released the models, the dataset, a new benchmark and a demo 🤩 nvidia/describe-anything-680825bb8f5e41ff0785834c
Most of the vision LMs focus on image as a whole, lacking localized references in captions, and not taking in visual prompts (points, boxes, drawings around objects)
DAM addresses this on two levels: new vision backbone that takes in focal crops and the image itself, and a large scale dataset 👀
They generate a dataset by extending existing segmentation and referring expression generation datasets like REFCOCO, by passing in the images and classes to VLMs and generating captions.
Lastly, they also release a new benchmark again with self-supervision, they use an LLM to evaluate the detailed captions focusing on localization 👏
Describe Anything Model (DAM) is a 3B vision language model to generate detailed captions with localized references 😮
The team released the models, the dataset, a new benchmark and a demo 🤩 nvidia/describe-anything-680825bb8f5e41ff0785834c
Most of the vision LMs focus on image as a whole, lacking localized references in captions, and not taking in visual prompts (points, boxes, drawings around objects)
DAM addresses this on two levels: new vision backbone that takes in focal crops and the image itself, and a large scale dataset 👀
They generate a dataset by extending existing segmentation and referring expression generation datasets like REFCOCO, by passing in the images and classes to VLMs and generating captions.
Lastly, they also release a new benchmark again with self-supervision, they use an LLM to evaluate the detailed captions focusing on localization 👏
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