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4 items

tutorialsJan 30

How to deploy and fine-tune DeepSeek models on AWS

DeepSeek models can be deployed and fine-tuned on AWS using Hugging Face's Transformers library and the SageMaker platform. This integration enables users to leverage the scalability and flexibility of AWS for their AI workloads. You can use pre-trained models or create custom models through fine-tuning. The solution provides a streamlined process for deploying and managing AI models in the cloud.

Key takeaways
  • DeepSeek models deployable on AWS via Hugging Face and SageMaker
  • Fine-tuning supported for custom model creation
  • Scalability and flexibility of AWS leveraged for AI workloads
HHugging Face Blog#cloud-deployment#fine-tuning#aws
modelsApr 17

Accelerating Hugging Face Transformers with AWS Inferentia2

Hugging Face has partnered with AWS to optimize Transformers for Inferentia2, a custom chip designed for machine learning inference. This collaboration aims to accelerate Transformers on AWS, reducing costs and improving performance. You can now deploy optimized Transformers on Inferentia2-based instances for faster and more cost-effective inference. The optimization enables faster inference speeds and lower costs for Transformers on AWS.

Key takeaways
  • Hugging Face Transformers optimized for AWS Inferentia2
  • Faster inference speeds and lower costs on Inferentia2-based instances
  • Partnership aims to improve performance and reduce costs for Transformers on AWS
HHugging Face Blog#hugging-face#aws#transformers#inference
otherFeb 23

Fetch Consolidates AI Tools and Saves 30% Development Time with Hugging Face on AWS

Fetch uses Hugging Face on AWS to consolidate AI tools and cut development time by 30%. The company integrated multiple models and APIs into a single platform, streamlining workflows. Builders can apply this approach to reduce complexity and costs. Consolidation also enables better scalability and maintenance.

Key takeaways
  • 30% reduction in development time via consolidation.
  • Hugging Face on AWS used for AI tool integration.
  • Scalability and maintenance improved.
HHugging Face Blog#hugging-face#aws#ai-tools
modelsMar 16

Accelerate BERT inference with Hugging Face Transformers and AWS Inferentia

Hugging Face and AWS collaborated to optimize BERT inference on AWS Inferentia chips, enabling faster and more cost-effective deployments. The solution leverages Hugging Face Transformers and SageMaker, reducing inference latency and increasing throughput. You can deploy optimized BERT models using Hugging Face and AWS services. This integration helps you accelerate NLP workloads.

Key takeaways
  • Optimized BERT inference on AWS Inferentia reduces latency and cost.
  • Hugging Face Transformers integrates with SageMaker for deployment.
  • Faster NLP workloads enabled for builders.
HHugging Face Blog#transformers#aws#inference-optimization#nlp