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Host concurrent LLMs with LoRAX
- Businesses are increasingly using domain-specific large language models (LLMs) to meet specialized needs across various domains such as NLP and content generation.
- Custom fine-tuned LLMs are tailored for specific tasks within domains like finance, marketing, and healthcare.
- To efficiently manage multiple fine-tuned models, Low-Rank Adaptation (LoRA) is utilized with LoRA eXchange (LoRAX) on Amazon EC2 GPU instances.
- LoRA involves introducing adapters within pre-trained language models for efficient adaptation with reduced trainable parameters.
- LoRAX allows hosting different model variants on a single EC2 instance, optimizing costs without compromising performance.
- LoRAX supports quantization methods like Activation-aware Weight Quantization and Half-Quadratic Quantization.
- By deploying LoRAX on AWS, businesses can benefit from cost-effective and efficient multi-tenant serving of fine-tuned models.
- Storing models and adapters in Amazon S3 offers reliability over third-party services, allowing for consistent deployments.
- Using LoRA adapters helps reduce hosting costs significantly by dynamically swapping multiple variants on a single instance.
- LoRAX enables efficient model serving and adaptation for various tasks, providing a cost-efficient solution for hosting multiple language models.
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