Advanced
vLLM Integration
Configure vLLM for high-throughput inference during RLHF training.
Installation
pip install -e ".[vllm]"Configuration
config = ModelConfig(
use_vllm=True,
vllm_tensor_parallel_size=2, # GPUs for tensor parallelism
vllm_gpu_memory_utilization=0.9, # GPU memory fraction
vllm_max_model_len=4096, # Max sequence length
vllm_enforce_eager=False, # Use CUDA graphs
)Performance Benefits
10x Faster Generation
PagedAttention enables 10x faster experience collection during RLHF training.
Continuous Batching
Dynamic batch scheduling maximizes GPU utilization.