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In this tutorial, we explore how to fine-tune NVIDIA’s NV-Embed-v1 model on the Amazon Polarity dataset using LoRA (Low-Rank Adaptation) with PEFT (Parameter-Efficient Fine-Tuning) from Hugging Face.
[model] type = 'sdxl' checkpoint_path = '/data2/imagegen_models/sdxl/sd_xl_base_1.0_0.9vae.safetensors' dtype = 'bfloat16' # You can train v-prediction models (e.g ...
To alleviate this problem, we apply Low Rank Adaptation (LoRA) to freeze most of the pre-trained model weights and inject the trainable rank decomposition matrices into each layer of the Transformer ...
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