YarnLake-Swap-7B
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View details ↗DISCOVER & COMPARE
Explore AI models 85003
Deformable DEtection TRansformer (DETR), trained on DocLayNet (including 80k annotated pages in 11 classes). The DETR model is an encoder-decoder transformer with a convolutional backbone. Two heads are added on top of the decoder outputs in order to perform object detection: a linear layer for the class labels and a MLP (multi-layer perceptron) for the bounding boxes. The model uses so-called object queries to detect objects in an image. Each object query looks for a particular object in the image. For COCO, the number of object queries is set to 100. The model is trained using a "bipartite matching loss": one compares the predicted classes + bounding boxes of each of the N = 100 object queries to the ground truth annotations, padded up to the same length N (so if an image only contains 4 objects, 96 annotations will just have a "no object" as class and "no bounding box" as bounding box). The Hungarian matching algorithm is used to create an optimal one-to-one mapping between each of the N queries and each of the N annotations. Next, standard cross-entropy (for the classes) and a linear combination of the L1 and generalized IoU loss (for the bounding boxes) are used to optimize the parameters of the model.
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View details ↗phi-4-mini-uncensored
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View details ↗qwen1.5-0.5b-uncensored
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View details ↗qwen2.5-0.5b-uncensored
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View details ↗qwen2.5-1.5b-uncensored
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View details ↗qwen2.5-3b-uncensored
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View details ↗qwen3.5-0.8b-uncensored
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View details ↗qwen3.5-2b-uncensored
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View details ↗qwen3.5-4b-uncensored
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View details ↗smollm-360m-uncensored
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View details ↗smolm2-1.7b-uncensored
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View details ↗smolm3-3b-uncensored
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View details ↗stablelm-2-1.6b-uncensored
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View details ↗stablelm-2-zephyr-1.6b-uncensored
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View details ↗segformer-v1-breastcancer
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View details ↗Llama-3.2-1B-Instruct-GGUF
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View details ↗bert-base-arabic
asafayaYou can use this model by installing `torch` or `tensorflow` and Huggingface library `transformers`. And you can use it directly by initializing it like this: Thanks to Google for providing free TPU for the training process and for Huggingface for hosting this model on their servers 😊
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View details ↗bert-large-arabic
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View details ↗bert-medium-arabic
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View details ↗bert-mini-arabic
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View details ↗kanarya-2b
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