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
- [Usage]: The GPU KV cache usage is still small (below 20% most of time) though I turned the enable_prefix_caching on as well as modified the block_size from 16 to 32. How could I numerally increase the KV cache for the Radix prefix caching?
- c4ai-command-r-plus on 16GPUs
- [Bug]: AsyncEngineDeadError: Task finished unexpectedly with qwen2 72b
- [RFC]: Deprecate stop_reason in OpenAI Entrypoint in favor of finish_reason; fix implementation of finish_reason
- [Usage]: How to increase the context length when start with vllm.entrypoints.openai.api_server
- [Usage]: BNB Gemma2 9b loading problems
- [Feature]: Precise model device placement
- [Usage]: How to use Multi-instance in Vllm? (Model replication on multiple GPUs)
- [Feature]: Return hidden states (in progress?)
- [Bug]: As V100 does not support FlashAttention, it is not possible to run the gemma model, hopefully it can support the xformers way to run it
- Docs
- Python not yet supported