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]: [LMCache] `update_state_after_alloc` passes wrong `cache_salt` to `free_lookup_locks`, leaking server read locks in multi-tenant deployments
- [LMCache] fix missing cache_salt in free_lookup_locks call
- [Bug]: GLM tool-call streaming final chunks repeat metadata and combine arguments with finish_reason
- [Bugfix] Fix GLM tool-call final streaming chunks
- [Bug]: [LMCache] `_cleanup_request_tracker` leaks lookup state and server read locks when request is aborted before allocation
- [LMCache] fix lookup lock leak when request is aborted before alloc
- [Bug]: Chat completions emits empty tool_calls arrays after tool results
- [Frontend][ToolParser] emit tool name blocks in advance in MinimaxM2
- routed_experts all-zero with FlashInfer (TRTLLM/CUTLASS) MoE — wire FlashInfer routing_replay_out into the capturer
- Add env var for FlashInfer autotune cache
- Docs
- Python not yet supported