vllm
https://github.com/vllm-project/vllm
Python
A high-throughput and memory-efficient inference and serving engine for LLMs
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- Issues
- [Bug]: Gemma4 tool parser (non-streaming): finish_reason=tool_calls with empty tool_calls array — model's move silently lost under concurrent load
- [Feature]: Populate completion_tokens_details in streaming and non-streaming usage responses
- [Bugfix] Transformers backend: tokenize multimodal prompts once
- [rl] Stateful Trainer Send: NCCL + Sparse NCCL [3/N]
- [ROCm] Remove stale SDPA and skinny GEMM workarounds
- [Bugfix] Include disabled multimodal modalities in the model config hash (#50891)
- [Bugfix] Validate Kimi attention layer configuration
- [Bugfix] Enhance reasoning extraction to handle prefixes and missing …
- [Bug]: stack overflow causing system crash: Rust unbounded recursion in the Gemma4 unified parser
- [Bugfix][Model] Kimi K3: pad the MoE intermediate by the effective shard count, on both backends
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