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Explore AI models 19
geochat-7B
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View details ↗GeoPixel-7B
MBZUAIGeoPixel-7B is the model specific to the Remote Sensing Grounded Conversation Generation (RS-GCG) task. It is finetuned on GeoPixelD dataset. - **Paper:** ArXiv. - **GitHub Repository:** For training and updates: GitHub - GeoPixel. - **Project Page:** For a detailed overview, visit our Project Page - GeoPixel.
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View details ↗LaMini-Flan-T5-248M
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View details ↗LaMini-Flan-T5-77M
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View details ↗LaMini-Flan-T5-783M
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View details ↗LaMini-GPT-124M
MBZUAIThis model is one of our LaMini-LM model series in paper "LaMini-LM: A Diverse Herd of Distilled Models from Large-Scale Instructions". This model is a fine-tuned version of gpt2 on LaMini-instruction dataset that contains 2.58M samples for instruction fine-tuning. For more information about our dataset, please refer to our project repository. You can view other models of LaMini-LM series as follows. Models with ✩ are those with the best overall performance given their size/architecture, hence we recommend using them. More details can be seen in our paper. We recommend using the model to respond to human instructions written in natural language. Since this decoder-only model is fine-tuned with wrapper text, we suggest using the same wrapper text to achieve the best performance. See the example on the right or the code below. We initialize with gpt2 and fine-tune it on our LaMini-instruction dataset. Its total number of parameters is 124M.
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View details ↗LaMini-T5-61M
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View details ↗LaMini-T5-738M
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View details ↗MediX-R1-2B-GGUF
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View details ↗MediX-R1-30B-GGUF
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View details ↗MediX-R1-8B-GGUF
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View details ↗MedMO-4B
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View details ↗MedMO-4B-Next
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View details ↗MedMO-8B-Next
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View details ↗MobiLlama-05B
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View details ↗OpenEarthAgent
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View details ↗swiftformer-s
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View details ↗swiftformer-xs
MBZUAIThe SwiftFormer model was proposed in SwiftFormer: Efficient Additive Attention for Transformer-based Real-time Mobile Vision Applications by Abdelrahman Shaker, Muhammad Maaz, Hanoona Rasheed, Salman Khan, Ming-Hsuan Yang, Fahad Shahbaz Khan. SwiftFormer paper introduces a novel efficient additive attention mechanism that effectively replaces the quadratic matrix multiplication operations in the self-attention computation with linear element-wise multiplications. A series of models called 'SwiftFormer' is built based on this, which achieves state-of-the-art performance in terms of both accuracy and mobile inference speed. Even their small variant achieves 78.5% top-1 ImageNet1K accuracy with only 0.8 ms latency on iPhone 14, which is more accurate and 2× faster compared to MobileViT-v2. url = 'http://images.cocodataset.org/val2017/000000039769.jpg' image = Image.open(requests.get(url, stream=True).raw)
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