diff --git a/docs/source/en/model_doc/gemma3.md b/docs/source/en/model_doc/gemma3.md index 4c7d978d3b..72c0c5d76a 100644 --- a/docs/source/en/model_doc/gemma3.md +++ b/docs/source/en/model_doc/gemma3.md @@ -164,6 +164,10 @@ visualizer = AttentionMaskVisualizer("google/gemma-3-4b-it") visualizer("What is shown in this image?") ``` +
+ +
+ ## Notes - Use [`Gemma3ForConditionalGeneration`] for image-and-text and image-only inputs. diff --git a/docs/source/en/model_doc/llama.md b/docs/source/en/model_doc/llama.md index 8869d8d4e8..7dc0660896 100644 --- a/docs/source/en/model_doc/llama.md +++ b/docs/source/en/model_doc/llama.md @@ -116,6 +116,10 @@ visualizer = AttentionMaskVisualizer("huggyllama/llama-7b") visualizer("Plants create energy through a process known as") ``` +
+ +
+ ## Notes - The tokenizer is a byte-pair encoding model based on [SentencePiece](https://github.com/google/sentencepiece). During decoding, if the first token is the start of the word (for example, "Banana"), the tokenizer doesn't prepend the prefix space to the string. diff --git a/docs/source/en/model_doc/llama2.md b/docs/source/en/model_doc/llama2.md index 4df0375f99..ec981890b2 100644 --- a/docs/source/en/model_doc/llama2.md +++ b/docs/source/en/model_doc/llama2.md @@ -116,6 +116,10 @@ visualizer = AttentionMaskVisualizer("meta-llama/Llama-2-7b-hf") visualizer("Plants create energy through a process known as") ``` +
+ +
+ ## Notes - Setting `config.pretraining_tp` to a value besides `1` activates a more accurate but slower computation of the linear layers. This matches the original logits better. diff --git a/docs/source/en/model_doc/paligemma.md b/docs/source/en/model_doc/paligemma.md index a1b4b6e1d4..fa119a5f83 100644 --- a/docs/source/en/model_doc/paligemma.md +++ b/docs/source/en/model_doc/paligemma.md @@ -125,6 +125,10 @@ visualizer = AttentionMaskVisualizer("google/paligemma2-3b-mix-224") visualizer(" What is in this image?") ``` +
+ +
+ ## Notes - PaliGemma is not a conversational model and works best when fine-tuned for specific downstream tasks such as image captioning, visual question answering (VQA), object detection, and document understanding.