Last Updated on 2024-11-06 by Clay
In auto-regressive model decoding, if we need to decode K tokens, we must go through the process K times, which is the current bottleneck in the inference time of large language models.
Read More »[Paper Reading] Fast Inference from Transformers via Speculative Decoding
Last Updated on 2024-11-02 by Clay
I have recently set up numerous backend API servers for Chatbots. Initially, I received user messages and returned the entire LLM-generated reply in one go to the frontend interface. However, this approach did not provide a good user experience. I then switched to HTTP streaming, sending each generated token to the frontend as it was produced. Later, I found that some users' devices experienced packet sticking, so I finally switched to using WebSocket.
Read More »[Python] FastAPI Using Server-Sent Events (SSE) for Streaming Responses
Last Updated on 2024-11-01 by Clay
During the decoding process of large language models, especially in Auto-regressive models, decoding must be performed step-by-step until the entire sequence is generated. Within this process, there are caching techniques that can help reduce computation and improve decoding speed; one such technique is known as the KV Cache.
Read More »KV Cache: A Caching Mechanism To Accelerate Transformer Generation
