vllm and Automodel
These are complementary tools: vLLM provides optimized inference serving for already-trained models, while NeMo's Automodel handles distributed training and preparation of those models before deployment.
About vllm
vllm-project/vllm
A high-throughput and memory-efficient inference and serving engine for LLMs
This project helps machine learning engineers and developers efficiently deploy and serve large language models (LLMs) in production environments. You provide your chosen LLM and receive a high-throughput, memory-optimized inference service ready for use. It's designed for ML engineers, MLOps specialists, and developers who need to integrate LLM capabilities into applications without sacrificing speed or cost efficiency.
About Automodel
NVIDIA-NeMo/Automodel
Pytorch Distributed native training library for LLMs/VLMs with OOTB Hugging Face support
This tool helps machine learning engineers and researchers adapt large language models (LLMs) and vision-language models (VLMs) from Hugging Face for specific tasks. You input an existing Hugging Face model and your specialized dataset, and it outputs a fine-tuned, more accurate model optimized for your particular use case. It's designed for individuals developing custom AI solutions that require state-of-the-art foundation models.
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