Addie Stevens's contributions
Article
Post-training methods for language models
Dive into LLM post-training methods, from supervised fine-tuning and continual learning to parameter-efficient and reinforcement learning approaches.
Article
vLLM with torch.compile: Efficient LLM inference on PyTorch
Learn how to optimize PyTorch code with minimal effort using torch.compile, a just-in-time compiler that generates optimized kernels automatically.
Article
Ollama or vLLM? How to choose the right LLM serving tool for your use case
Ollama makes it easy for developers to get started with local model experimentation, while vLLM provides a path to reliable, efficient, and scalable deployment.
Article
GuideLLM: Evaluate LLM deployments for real-world inference
Learn how to evaluate the performance of your LLM deployments with the open source GuideLLM toolkit to optimize cost, reliability, and user experience.
Article
Structured outputs in vLLM: Guiding AI responses
Learn how to control the output of vLLM's AI responses with structured outputs. Discover how to define choice lists, JSON schemas, regex, and more.
Article
LLM Compressor: Optimize LLMs for low-latency deployments
LLM Compressor bridges the gap between model training and efficient deployment via quantization and sparsity, enabling cost-effective, low-latency inference.
Article
vLLM V1: Accelerating multimodal inference for large language models
Explore how vLLM's new multimodal AI inference capabilities enhance performance, scalability, and flexibility across diverse hardware platforms.
Post-training methods for language models
Dive into LLM post-training methods, from supervised fine-tuning and continual learning to parameter-efficient and reinforcement learning approaches.
vLLM with torch.compile: Efficient LLM inference on PyTorch
Learn how to optimize PyTorch code with minimal effort using torch.compile, a just-in-time compiler that generates optimized kernels automatically.
Ollama or vLLM? How to choose the right LLM serving tool for your use case
Ollama makes it easy for developers to get started with local model experimentation, while vLLM provides a path to reliable, efficient, and scalable deployment.
GuideLLM: Evaluate LLM deployments for real-world inference
Learn how to evaluate the performance of your LLM deployments with the open source GuideLLM toolkit to optimize cost, reliability, and user experience.
Structured outputs in vLLM: Guiding AI responses
Learn how to control the output of vLLM's AI responses with structured outputs. Discover how to define choice lists, JSON schemas, regex, and more.
LLM Compressor: Optimize LLMs for low-latency deployments
LLM Compressor bridges the gap between model training and efficient deployment via quantization and sparsity, enabling cost-effective, low-latency inference.
vLLM V1: Accelerating multimodal inference for large language models
Explore how vLLM's new multimodal AI inference capabilities enhance performance, scalability, and flexibility across diverse hardware platforms.
