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- VL Model Training: Support SFT for [PaddleOCR-VL-0.9B]((https://huggingface.co/PaddlePaddle/PaddleOCR-VL/tree/main/PaddleOCR-VL-0.9B)) model. More details in [PaddleOCR-VL-0.9B SFT](./docs/paddleocr_vl_sft.md).
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- VL Model Training: Support SFT for [PaddleOCR-VL-0.9B]((https://huggingface.co/PaddlePaddle/PaddleOCR-VL/tree/main/PaddleOCR-VL-0.9B)) model. More details in [PaddleOCR-VL-0.9B SFT](./docs/source/paddleocr_vl_sft.md).
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- Dataflow : Support padding-free startegy.
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- Packing data within a batch into a sequence to avoid padding, thereby reducing GPU memory usage and accelerating training.
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@@ -178,7 +178,7 @@ In the non-thinking mode, ERNIE-4.5-VL exhibits outstanding proficiency in visua
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## Model Development
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ERNIE 4.5 models are trained and deployed for inference using the [PaddlePaddle]((https://github.com/PaddlePaddle/Paddle)) framework. The full workflow of training, compression, and inference for ERNIE 4.5 is supported through the [ERNIEKit](./docs/erniekit.md) and [FastDeploy](https://github.com/PaddlePaddle/FastDeploy) toolkit. The table below details the feature matrix of the ERNIE 4.5 model family for training and inference.
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ERNIE 4.5 models are trained and deployed for inference using the [PaddlePaddle]((https://github.com/PaddlePaddle/Paddle)) framework. The full workflow of training, compression, and inference for ERNIE 4.5 is supported through the [ERNIEKit](./docs/source/erniekit.md) and [FastDeploy](https://github.com/PaddlePaddle/FastDeploy) toolkit. The table below details the feature matrix of the ERNIE 4.5 model family for training and inference.
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<divalign="center">
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| Model | Training | Inference |
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* Quantization-Aware Training (QAT)
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* Post-Training Quantization (PTQ) [WIP]
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Minimum hardware requirements for training each model are documented [here](./docs/erniekit.md).
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Minimum hardware requirements for training each model are documented [here](./docs/source/erniekit.md).
For detailed guides on installation, CLI usage, WebUI, multi-node training, and advanced features, please refer to [ERNIEKit Training Document](./docs/erniekit.md).
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For detailed guides on installation, CLI usage, WebUI, multi-node training, and advanced features, please refer to [ERNIEKit Training Document](./docs/source/erniekit.md).
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For detailed guides on High-performance pre-training, please refer to [Pre-Training Document](./examples/pre-training/README.md).
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