下面的图表表明,当使用 ONNX Runtime 和 DeepSpeed ZeRO Stage 1进行训练时,用 Optimum 的 Hugging Face 模型的加速从 39% 提高到 130%。性能测试的基准运行是在选定的 Hugging Face PyTorch 模型上进行的,第二次运行是只用 ONNX Runtime 训练,最后一次运行是 ONNX Runtime + DeepSpeed ZeRO Stage 1,图中显...
I checked the code of optimum as per your suggestion. From this I implemented my callable functionality of the OnnxDecoder. I also checked the keys of the input_names of the onnx names and all this is correct. However, at the end I keep on getting an issue with size mismatches. The ...
I'm struggling with the sioze of the openai/clip-vit-large-patch14 model, thus I want to convert it to OPTIMUM onnx! Your contribution no ideas so far.. Hi@antje2233, which command are you running?optimum-cli export onnx --model openai/clip-vit-large-patch14 clip_onnx --task zero...
fix image link in inpaint doc by @yiyixuxu in #2693 [docs] Update ONNX doc to useoptimumby @sayakpaul in #2702 Enabling gradient checkpointing for VAE by @Pie31415 in #2536 [Tests] Correct PT2 by @patrickvonplaten in #2724 Update mps.mdx by @standardAI in #2749 Update torch2.0.md...
Following what was done by @chainyo in Transformers, in the ONNXConfig: Add a configuration for all available models issue, the idea is to add support for exporting new models in optimum.exporters.onnx. This issue is about the working gr...
Optimum + ONNX Runtime: Easier, Faster training for your Hugging Face models Introduction Transformer based models in language, vision and speech are getting larger to support complex multi-modal use cases for the end customer. Increasing model sizes directly impact the resources ...
Optimum + ONNX Runtime: Easier, Faster training for your Hugging Face models Introduction Transformer based models in language, vision and speech are getting larger to support complex multi-modal use cases for the end customer. Increasing model sizes directly impact the resources ...
After the pre-training or the fine-tuning is done, developers can either save the trained PyTorch model or convert it to the ONNX format with APIs that Optimum implemented for ONNX Runtime to ease the deployment for Inference. And just like Trainer, ORTTrainer has full i...
@SmartWashingMachine, yes output was logits indeed. Thanks for the code snipped, it works well, but it seems that the model should be converted in some other way. I used Huggingface's Optimum for MarianMT and it works perfect with conversion and inference. ...
Hugging Face 和微软的 ONNX Runtime 团队正在一起努力,在微调大型语言、语音和视觉模型方面取得进步。Hugging Face 的 [Optimum 库](https://huggingface.co/docs/optimum/index),通过和 ONNX Runtime 的集成进行训练,为许多流行的 Hugging Face 模型提供了一个开放的解决方案,**可以将训练时间缩短35%或更多**...