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
- [Doc] Fill gaps in the DeepSeek-V4.1-Flash B300 Dynamo disaggregated guide
- [Bugfix][MRV2] Fix GDN states and prefill metadata in AV profiling
- [Bugfix][Frontend] Pass Cohere v2 response_format to the prompt renderer
- [Feature]: Token-aware control-token sanitization for DeepSeek renderers
- [Bugfix] Keep prompt logprobs when merging n>1 DELTA outputs
- [Perf][Reasoning] Cut decoding overhead in Muse Spark Open's reasoning parser
- [Bugfix][Frontend] Report max_model_len for LoRA adapters in /v1/models
- [Bugfix][Multimodal] Transfer pruning-adjusted M-RoPE state in disaggregated P/D
- [Feature]: Measure the Dynamic SD table instead of writing it by hand
- [Bug]: K2 Horizon reasoning parser only accepts the end tag of the requested effort, but the model closes medium/low thinking with </ifm|think>, so reasoning leaks into content or the answer ends up in reasoning
- Docs
- Python not yet supported