Cyrus Leung 27d7638b94
[Bugfix] Merge MM embeddings by index instead of token IDs (#16229)
Signed-off-by: DarkLight1337 <tlleungac@connect.ust.hk>
Signed-off-by: NickLucche <nlucches@redhat.com>
Signed-off-by: Roger Wang <hey@rogerw.io>
Co-authored-by: NickLucche <nlucches@redhat.com>
Co-authored-by: Roger Wang <hey@rogerw.io>
2025-09-27 08:15:12 +00:00
..
2025-09-16 20:53:23 -07:00

Summary

!!! important Many decoder language models can now be automatically loaded using the [Transformers backend][transformers-backend] without having to implement them in vLLM. See if vllm serve <model> works first!

vLLM models are specialized PyTorch models that take advantage of various features to optimize their performance.

The complexity of integrating a model into vLLM depends heavily on the model's architecture. The process is considerably straightforward if the model shares a similar architecture with an existing model in vLLM. However, this can be more complex for models that include new operators (e.g., a new attention mechanism).

Read through these pages for a step-by-step guide:

!!! tip If you are encountering issues while integrating your model into vLLM, feel free to open a GitHub issue or ask on our developer slack. We will be happy to help you out!