* deprecate the prev fix * reword warning and update docs * reword warning * tests * dont bloat `get_text_config()`
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Raushan Turganbay
committed by
Arthur Zucker
Arthur Zucker
parent
e6ab93e702
commit
d9ccb9adbb
@@ -105,59 +105,75 @@ inputs = processor.apply_chat_template(
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add_generation_prompt=True,
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).to(model.device)
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output = model.generate(**inputs, max_new_tokens=50)
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print(processor.decode(output[0], skip_special_tokens=True)[inputs.input_ids.shape[1]: ])
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output = model.generate(**inputs, max_new_tokens=50, cache_implementation="static")
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print(processor.decode(output[0], skip_special_tokens=True))
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```
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### Multi-image Inference
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Use the [AttentionMaskVisualizer](https://github.com/huggingface/transformers/blob/beb9b5b02246b9b7ee81ddf938f93f44cfeaad19/src/transformers/utils/attention_visualizer.py#L139) to better understand what tokens the model can and cannot attend to.
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```python
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model_id = "google/gemma-3-4b-it"
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model = Gemma3ForConditionalGeneration.from_pretrained(model_id, device_map="auto")
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processor = AutoProcessor.from_pretrained(model_id, padding_side="left")
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url_cow = "https://media.istockphoto.com/id/1192867753/photo/cow-in-berchida-beach-siniscola.jpg?s=612x612&w=0&k=20&c=v0hjjniwsMNfJSuKWZuIn8pssmD5h5bSN1peBd1CmH4="
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url_stop = "https://www.ilankelman.org/stopsigns/australia.jpg"
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messages = [
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{
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"role": "system",
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"content": [
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{"type": "text", "text": "You are a helpful assistant."}
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]
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},
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{
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"role": "user", "content": [
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{"type": "image", "url": url_cow},
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{"type": "image", "url": url_stop},
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{"type": "text", "text": "Are these two images identical?"},
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]
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},
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]
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inputs = processor.apply_chat_template(
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messages,
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tokenize=True,
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return_dict=True,
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return_tensors="pt",
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add_generation_prompt=True,
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).to(model.device)
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output = model.generate(**inputs, max_new_tokens=50)
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print(processor.decode(output[0], skip_special_tokens=True)[inputs.input_ids.shape[1]: ])
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```py
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from transformers.utils.attention_visualizer import AttentionMaskVisualizer
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visualizer = AttentionMaskVisualizer("google/gemma-3-4b-it")
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visualizer("<img>What is shown in this image?")
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```
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### Text-only inference
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## Notes
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You can use the VLMs for text-only generation by omitting images in your input. However, you can also load the models in text-only mode as shown below. This will skip loading the vision tower and will save resources when you just need the LLM capabilities.
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```python
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from transformers import AutoTokenizer, Gemma3ForCausalLM
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- Use [`Gemma3ForConditionalGeneration`] for image-and-text and image-only inputs.
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- Gemma 3 supports multiple input images, but make sure the images are correctly batched before passing them to the processor. Each batch should be a list of one or more images.
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model_id = "google/gemma-3-1b-it"
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```py
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url_cow = "https://media.istockphoto.com/id/1192867753/photo/cow-in-berchida-beach-siniscola.jpg?s=612x612&w=0&k=20&c=v0hjjniwsMNfJSuKWZuIn8pssmD5h5bSN1peBd1CmH4="
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url_cat = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = Gemma3ForCausalLM.from_pretrained(model_id, device_map="auto")
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messages =[
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{
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"role": "system",
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"content": [
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{"type": "text", "text": "You are a helpful assistant."}
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]
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},
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{
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"role": "user",
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"content": [
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{"type": "image", "url": url_cow},
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{"type": "image", "url": url_cat},
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{"type": "text", "text": "Which image is cuter?"},
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]
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},
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]
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```
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- Text passed to the processor should have a `<start_of_image>` token wherever an image should be inserted.
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- The processor has its own [`~ProcessorMixin.apply_chat_template`] method to convert chat messages to model inputs.
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- By default, images aren't cropped and only the base image is forwarded to the model. In high resolution images or images with non-square aspect ratios, artifacts can result because the vision encoder uses a fixed resolution of 896x896. To prevent these artifacts and improve performance during inference, set `do_pan_and_scan=True` to crop the image into multiple smaller patches and concatenate them with the base image embedding. You can disable pan and scan for faster inference.
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input_ids = tokenizer("Write me a poem about Machine Learning.", return_tensors="pt").to(model.device)
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```diff
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inputs = processor.apply_chat_template(
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messages,
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tokenize=True,
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return_dict=True,
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return_tensors="pt",
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add_generation_prompt=True,
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+ do_pan_and_scan=True,
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).to("cuda")
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```
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- For Gemma-3 1B checkpoint trained in text-only mode, use [`AutoModelForCausalLM`] instead.
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```py
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer
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tokenizer = AutoTokenizer.from_pretrained(
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"google/gemma-3-1b-pt",
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)
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model = AutoModelForCausalLM.from_pretrained(
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"google/gemma-3-1b-pt",
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torch_dtype=torch.bfloat16,
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device_map="auto",
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attn_implementation="sdpa"
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)
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input_ids = tokenizer("Plants create energy through a process known as", return_tensors="pt").to("cuda")
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outputs = model.generate(**input_ids, max_new_tokens=100)
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text = tokenizer.batch_decode(outputs, skip_special_tokens=True)
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