Gahow Wang
445e491123
third_party/vllm/ now tracked in git for direct patch management.
Based on vLLM v0.18.1 release with one patch applied:
vllm/v1/core/sched/scheduler.py:
Replace fatal assert with graceful skip when KV transfer callback
arrives for an already-aborted request during PD disaggregated serving.
Future vLLM modifications should be made directly in third_party/vllm/
and committed normally. The patches/ directory is kept as documentation
of what changed from upstream.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
24 lines
1.2 KiB
Markdown
24 lines
1.2 KiB
Markdown
# Summary
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!!! important
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Many decoder language models can now be automatically loaded using the [Transformers modeling backend](../../models/supported_models.md#transformers) without having to implement them in vLLM. See if `vllm serve <model>` works first!
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vLLM models are specialized [PyTorch](https://pytorch.org/) models that take advantage of various [features](../../features/README.md#compatibility-matrix) to optimize their performance.
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The complexity of integrating a model into vLLM depends heavily on the model's architecture.
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The process is considerably straightforward if the model shares a similar architecture with an existing model in vLLM.
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However, this can be more complex for models that include new operators (e.g., a new attention mechanism).
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Read through these pages for a step-by-step guide:
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- [Basic Model](basic.md)
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- [Registering a Model](registration.md)
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- [Unit Testing](tests.md)
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- [Multi-Modal Support](multimodal.md)
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- [Speech-to-Text Support](transcription.md)
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!!! tip
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If you are encountering issues while integrating your model into vLLM, feel free to open a [GitHub issue](https://github.com/vllm-project/vllm/issues)
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or ask on our [developer slack](https://slack.vllm.ai).
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We will be happy to help you out!
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