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  • Fine-tune Meta Llama 3. 1 models using torchtune on Amazon SageMaker
    In this post, AWS collaborates with Meta’s PyTorch team to showcase how you can use PyTorch’s torchtune library to fine-tune Meta Llama-like architectures while using a fully-managed environment provided by Amazon SageMaker Training
  • Fine-tune Llama 3 with PyTorch FSDP and Q-Lora on Amazon SageMaker
    This blog post walks you thorugh how to fine-tune a Llama 3 using PyTorch FSDP and Q-Lora with the help of Hugging Face TRL, Transformers, peft datasets on Amazon SageMAker In addition to FSDP we will use Flash Attention v2 implementation
  • Fine-tune Meta Llama 3. 1 models using torchtune on Amazon SageMaker
    In this post, AWS collaborates with Meta’s PyTorch team to showcase how you can use Meta’s torchtune library to fine-tune Meta Llama-like architectures while using a fully-managed environment provided by Amazon SageMaker Training
  • Llama 3. 1 models are now available in Amazon SageMaker JumpStart
    Today, we are excited to announce that the state-of-the-art Llama 3 1 collection of multilingual large language models (LLMs), which includes pre-trained and instruction tuned generative AI models in 8B, 70B, and 405B sizes, is available through Amazon SageMaker JumpStart to deploy for inference
  • yuhuiaws finetuning-and-deploying-llama-on-Sagemaker
    Fine tune llama by SMP on SageMaker multiple nodes: We use SMP+HF trainer API to fine tune the llama, which is code zero intrusion S5cmd should be used to download model and upload model in the training procedure, which will save much time
  • Announcing Llama 3. 1 405B, 70B, and 8B models from Meta in Amazon . . .
    When you build fine-tuned models in SageMaker JumpStart, you will also be able to import your custom models into Amazon Bedrock To learn more, visit Meta Llama 3 1 models are now available in Amazon SageMaker JumpStart on the AWS Machine Learning Blog
  • Deploy Llama 3 on Amazon SageMaker - Philschmid
    In addition to the 4 models, a new version of Llama Guard was fine-tuned on Llama 3 8B and is released as Llama Guard 2 (safety fine-tune) In this blog you will learn how to deploy meta-llama Meta-Llama-3-70B-Instruct model to Amazon SageMaker
  • Fine-tune Meta Llama 3. 1 models for generative AI inference using . . .
    The following screenshot shows the fine-tuning page for the Meta Llama 3 1 405B model; however, you can fine-tune the 8B and 70B Llama 3 1 text generation models using their respective model pages similarly To fine-tune these models, you need to provide the following: Amazon Simple Storage Service (Amazon S3) URI for the training dataset location
  • Llama 3. 1 models are now available in Amazon SageMaker JumpStart . . .
    In this post, we walk through how to discover and deploy Llama 3 1 models using SageMaker JumpStart The Llama 3 1 multilingual LLMs are a collection of pre-trained and instruction tuned generative models in 8B, 70B, and 405B sizes (text in text and code out)
  • Deploy Meta Llama 3. 1 models cost-effectively in Amazon SageMaker . . .
    Meta Llama 3 1 multilingual large language models (LLMs) are a collection of pre-trained and instruction tuned generative models Trainium and Inferentia, enabled by the AWS Neuron software development kit (SDK), offer high performance and lower the cost of deploying Meta Llama 3 1 by up to 50%





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