From 649818995f60b410a57059445ec1d66f8db347b5 Mon Sep 17 00:00:00 2001 From: Michael Goin Date: Thu, 24 Apr 2025 22:20:04 -0600 Subject: [PATCH] [Docs] Fix True->true in supported_models.md (#17141) --- docs/source/models/supported_models.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/docs/source/models/supported_models.md b/docs/source/models/supported_models.md index 01427f4f2ba4..6b101662fc14 100644 --- a/docs/source/models/supported_models.md +++ b/docs/source/models/supported_models.md @@ -1082,7 +1082,7 @@ See [this page](#generative-models) for more information on how to use generativ :::{important} Pan-and-scan image pre-processing is currently supported on V0 (but not V1). -You can enable it by passing `--mm-processor-kwargs '{"do_pan_and_scan": True}'`. +You can enable it by passing `--mm-processor-kwargs '{"do_pan_and_scan": true}'`. ::: :::{warning} @@ -1097,7 +1097,7 @@ V0 correctly implements the model's attention pattern: V1 currently uses a simplified attention pattern: - Uses causal attention for all tokens, including image tokens -- Generates reasonable outputs but does not match the original model's attention for text + image inputs, especially when `{"do_pan_and_scan": True}` +- Generates reasonable outputs but does not match the original model's attention for text + image inputs, especially when `{"do_pan_and_scan": true}` - Will be updated in the future to support the correct behavior This limitation exists because the model's mixed attention pattern (bidirectional for images, causal otherwise) is not yet supported by vLLM's attention backends. @@ -1129,7 +1129,7 @@ To use Qwen2.5-Omni, you have to install Hugging Face Transformers library from `pip install git+https://github.com/huggingface/transformers.git`. Read audio from video pre-processing is currently supported on V0 (but not V1), because overlapping modalities is not yet supported in V1. -`--mm-processor-kwargs '{"use_audio_in_video": True}'`. +`--mm-processor-kwargs '{"use_audio_in_video": true}'`. ::: ### Pooling Models