Philschmid/flan-t5-base-samsum
WebbWhen running the script: python ./scripts/convert.py --model_id philschmid/flan-t5-base-samsum --from_hub --quantize --task seq2seq-lm I get the following error: TypeError: … Webb来自:Hugging Face进NLP群—>加入NLP交流群在本文中,我们将展示如何使用 大语言模型低秩适配 (Low-Rank Adaptation of Large Language Models,LoRA) 技术在单 GPU 上微调 110 亿参数的 FLAN-T5 XXL 模型。在此过程中,我们会使用到 Hugging Face 的 Transformers、Accelerate ...
Philschmid/flan-t5-base-samsum
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Webb22 feb. 2024 · 1. Process dataset and upload to S3. Similar to the “Fine-tune FLAN-T5 XL/XXL using DeepSpeed & Hugging Face Transformers” we need to prepare a dataset to fine-tune our model. As mentioned in the beginning, we will fine-tune FLAN-T5-XXL on the CNN Dailymail Dataset.The blog post is not going into detail about the dataset generation. Webb20 mars 2024 · Philschmid/flan-t5-base-samsum is a pre-trained language model developed by Phil Schmid and hosted on Hugging Face’s model hub. It is based on the …
Webb12 apr. 2024 · 库。 通过本文,你会学到: 如何搭建开发环境; 如何加载并准备数据集; 如何使用 LoRA 和 bnb (即 bitsandbytes) int-8 微调 T5 Webb5 feb. 2024 · Workflows can be created in either Python or YAML. For this article, we’ll create YAML configuration. summary: path: philschmid/flan-t5-base-samsum translation: workflow: summary: tasks ...
Webb我们可以看到 bf16 与 fp32 相比具有显著优势。 FLAN-T5-XXL 能放进 4 张 A10G (24GB),但放不进 8 张 V100 16GB。 我们的实验还表明,如果模型可以无需卸载同时以 batch size 大于 4 的配置跑在 GPU 上,其速度将比卸载模型和减小 batch size 的配置快约 2 倍且更具成本效益。 Webbför 2 dagar sedan · 在本文中,我们将展示如何使用 大语言模型低秩适配 (Low-Rank Adaptation of Large Language Models,LoRA) 技术在单 GPU 上微调 110 亿参数的 FLAN-T5 XXL 模型。 在此过程中,我们会使用到 Hugging Face 的 Transformers、Accelerate 和 PEFT 库。. 通过本文,你会学到: 如何搭建开发环境
Webb23 mars 2024 · In this blog, we are going to show you how to apply Low-Rank Adaptation of Large Language Models (LoRA) to fine-tune FLAN-T5 XXL (11 billion parameters) on a single GPU. We are going to leverage Hugging Face Transformers, Accelerate, and PEFT.. You will learn how to: Setup Development Environment
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