MBZUAI

This 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 google/flan-t5-large 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. Flan-T5 LaMini-Flan-T5-77M ✩ LaMini-Flan-T5-248M ✩ LaMini-Flan-T5-783M ✩ Cerebras-GPT LaMini-Cerebras-111M LaMini-Cerebras-256M LaMini-Cerebras-590M LaMini-Cerebras-1.3B GPT-2 LaMini-GPT-124M ✩ LaMini-GPT-774M ✩ LaMini-GPT-1.5B ✩ Intended use We recommend using the model to response to human instructions written in natural language. We now show you how to load and use our model using HuggingFace `pipeline()`. We initialize with google/flan-t5-large and fine-tune it on our LaMini-instruction dataset. Its total number of parameters is 783M. The following hyperparameters were used during training: - learningrate: 0.0005 - trainbatchsize: 128 - evalbatchsize: 64 - seed: 42 - gradientaccumulationsteps: 4 - totaltrainbatchsize: 512 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lrschedulertype: linear - numepochs: 5 Evaluation We conducted two sets of evaluations: automatic evaluation on downstream NLP tasks and human evaluation on user-oriented instructions. For more detail, please refer to our [paper]().

📝 Description GeoPixel-7B-RES is the model specific to the Referring Remote Sensing Image Segmentation (RRSIS) task. It is finetuned on RRSIS-D dataset. 📚 Additional Resources - Paper: ArXiv. - GitHub Repository: For training and updates: GitHub - GeoPixel. - Project Page: For a detailed overview, visit our Project Page - GeoPixel.

BiMediX2 : Bio-Medical EXpert LMM for Diverse Medical Modalities Mohamed Bin Zayed University of Artificial Intelligence (MBZUAI), UAE [](https://github.com/mbzuai-oryx/BiMediX2) [](https://arxiv.org/abs/2412.07769) [](https://github.com/mbzuai-oryx/BiMediX/blob/main/LICENSE.txt) BiMediX2 is released under the CC-BY-NC-SA 4.0 License. For more details, please refer to the LICENSE file included in our BiMediX repository. ⚠️ Warning! This release, intended for research, is not ready for clinical or commercial use. Users are urged to employ BiMediX2 responsibly, especially when applying its outputs in real-world medical scenarios. It is imperative to verify the model's advice with qualified healthcare professionals and not rely on it for medical diagnoses or treatment decisions. Despite the overall advancements BiMediX2 shares common challenges with other language models, including hallucinations, toxicity, and stereotypes. BiMediX2's medical diagnoses and recommendations are not infallible. If you use BiMediX2 in your research, please cite our work as follows: