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
- [ROCm] Add defensive group-size validation for W4A16 kernels
- [Bug][CPU] apply_top_k_top_p_triton drops tokens that top-p must keep (kernel logic; reproduces end to end on V1 and V2)
- [Frontend][Responses] Support client-executed tool_search
- [Bug]: LoRA adapters with PEFT rank_pattern / alpha_pattern are served with the wrong scaling
- [Bug]: Engine fails to start with `--enable-expert-parallel` and `--load-format ipc_cache`; the disk fallback fails too
- [Bugfix][Sampler] Keep tokens top-p must keep in Triton top-k/top-p search
- [Bug]: `--custom-histogram-buckets` rejects a `0` bound, so `vllm:request_num_preemptions` cannot separate never-preempted requests
- [Bugfix][Metrics] Allow zero-valued histogram bucket boundaries for count-based metrics
- [Bugfix][Metrics] Allow a 0 bound in --custom-histogram-buckets
- [ROCm][AMD] GLM5.3 Flash Performance Optimization on gfx950 / MI355X
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