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@@ -31,8 +31,8 @@ The additional details of the Aquila model will be presented in the official tec
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  | Aquila-33B | Base model, 33 billion parameters | Same as above | Coming soon | Nvidia-A100 |
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  | AquilaChat-7B | SFT model, fine-tuned and RL based on Aquila-7B | **AquilaChat Dialog Model** supports fluent text dialogue and multiple language generation tasks, and realizes the call of AquilaChat to other models and tools by defining an expandable special instruction specification, which is easy to extend. For example, calling the open source **[AltDiffusion](https://github.com/FlagAI-Open/FlagAI/tree/master/examples/AltDiffusion-m18) multimodal language image generation model** of Flagship Intelligence achieved smooth image generation capability. Together with Flagship Intelligence's **InstructFace multi-step controllable text-picture model**, it is easy to achieve multi-step controllable editing of human face images. | Released | Nvidia-A100 |
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  | AquilaChat-33B | SFT model, fine-tuned and RL based on Aquila-33B | Same as above | Coming soon | Nvidia-A100 |
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- | AquilaCode-7B-NV | Base model, "text-code" generation model, further pre-trained based on Aquila-7B, trained on Nvidia | AquilaCode-7B achieves high performance with small data sets and parameters, and is currently the best open source code model that supports both Chinese and English, trained using training code data with compliant open source licenses after high-quality filtering. AquilaCode-7B has been trained on both Nvidia and domestic chips for code models. | Released on GitHub | Nvidia-A100 |
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- | AquilaCode-7B-TS | Base model, "text-code" generation model, further pre-trained based on Aquila-7B, trained on Horizon Robotics chips | Same as above | Released on GitHub | Tianshu-BI-V100 |
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  | Aquila-33B | Base model, 33 billion parameters | Same as above | Coming soon | Nvidia-A100 |
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  | AquilaChat-7B | SFT model, fine-tuned and RL based on Aquila-7B | **AquilaChat Dialog Model** supports fluent text dialogue and multiple language generation tasks, and realizes the call of AquilaChat to other models and tools by defining an expandable special instruction specification, which is easy to extend. For example, calling the open source **[AltDiffusion](https://github.com/FlagAI-Open/FlagAI/tree/master/examples/AltDiffusion-m18) multimodal language image generation model** of Flagship Intelligence achieved smooth image generation capability. Together with Flagship Intelligence's **InstructFace multi-step controllable text-picture model**, it is easy to achieve multi-step controllable editing of human face images. | Released | Nvidia-A100 |
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  | AquilaChat-33B | SFT model, fine-tuned and RL based on Aquila-33B | Same as above | Coming soon | Nvidia-A100 |
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+ | AquilaCode-multi | Base model, "text-code" generation model, continue-pre-trained based on Aquila-7B. | AquilaCode utilizes high-quality, filtered, and compliant open-source code data for training, with a dataset size of approximately 10-40% compared to other open-source code generation models. By following the provided official guidelines, developers can harness the power of the AquilaCode model to customize their own code assistant. | Released | Nvidia-A100 |
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+ | AquilaCode-py | Base model, "text-code" generation model, continue-pre-trained based on Aquila-7B, trained on Horizon Robotics chips | Same as above | Released | Nvidia-A100 |
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