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Hi @changgeli ! With the new transformers release, fuyu-8b's processor has been updated and we've also got more information on prompt structure. You have to prompt it to answer in a VQA fashion.
here, try this:

from PIL import Image
import requests
import io
from transformers import FuyuForCausalLM, FuyuProcessor


pretrained_path = "adept/fuyu-8b"
processor = FuyuProcessor.from_pretrained(pretrained_path)
model = FuyuForCausalLM.from_pretrained(pretrained_path, device_map='auto')


text_prompt = "Answer the following VQAv2 question based on the image: What type of foods are in the image?"
fish_image_url = "https://huggingface.co/datasets/hf-internal-testing/fixtures-captioning/resolve/main/fish_carrots.png"
fish_image_pil = Image.open(io.BytesIO(requests.get(fish_image_url).content))
model_inputs = processor(text=text_prompt, images=fish_image_pil).to('cuda')


model_outputs = processor.batch_decode(model.generate( **model_inputs, max_new_tokens=10)[:, -10:], skip_special_tokens=True)[0]
prediction = model_outputs.split('\x04 ', 1)[1] if '\x04' in model_outputs else ''

You should see "fish, carrots, lemon" as a prediction.

Thank you for the comprehensive reply! Are there any recommended prompts to use for other datasets or tasks?

You're welcome :) that I know of, to best match the blog post examples, you can try "Answer the following DocVQA question based on the image." or the one I shared above, seems to match well!

from PIL import Image
import requests
import io
from transformers import FuyuForCausalLM, FuyuProcessor

pretrained_path = "adept/fuyu-8b"
processor = FuyuProcessor.from_pretrained(pretrained_path)
model = FuyuForCausalLM.from_pretrained(pretrained_path, device_map='auto')


text_prompt = "Answer the following DocVQA question based on the image. \n Which is the metro in California that has a good job Outlook?"
jobs_image_url = "https://huggingface.co/datasets/hf-internal-testing/fixtures-captioning/resolve/main/jobs.png"
jobs_image_pil = Image.open(io.BytesIO(requests.get(jobs_image_url).content))

second_text_prompt = "Answer the following DocVQA question based on the image. \n What if the maximum male life expectancy?"
chart_image_url = "https://huggingface.co/datasets/hf-internal-testing/fixtures-captioning/resolve/main/chart.png"
chart_image_pil = Image.open(io.BytesIO(requests.get(chart_image_url).content))

third_text_prompt = "Answer the following DocVQA question based on the image. \n What sport is that?"
skate_image_url = "https://huggingface.co/datasets/hf-internal-testing/fixtures-captioning/resolve/main/skateboard.png"
skate_image_pil = Image.open(io.BytesIO(requests.get(skate_image_url).content))

fourth_text_prompt = "Answer the following DocVQA question based on the image. \n What was the fair amount of paid vacation days in the United Kingdom?"
vacations_image_url = "https://huggingface.co/datasets/hf-internal-testing/fixtures-captioning/resolve/main/vacation_days_hr.png"
vacations_image_pil = Image.open(io.BytesIO(requests.get(vacations_image_url).content)).convert('RGB')

fifth_text_prompt = "Answer the following VQAv2 question based on the image: What type of foods are in the image?"
fish_image_url = "https://huggingface.co/datasets/hf-internal-testing/fixtures-captioning/resolve/main/fish_carrots.png"
fish_image_pil = Image.open(io.BytesIO(requests.get(fish_image_url).content))


texts = [text_prompt, second_text_prompt, third_text_prompt, fourth_text_prompt, fifth_text_prompt]
images = [jobs_image_pil, chart_image_pil, skate_image_pil, vacations_image_pil, fish_image_pil]

model_inputs = processor(text=texts, images=images).to('cuda')


model_outputs = processor.batch_decode(model.generate(
    **model_inputs, max_new_tokens=10)[:, -10:], skip_special_tokens=True)

ground_truths = ['Los Angeles', '80.7', 'skateboarding', '28', 'fish, carrots, lemon']


for ground_truth, model_output in zip(ground_truths, model_outputs):
    prediction = model_output.split('\x04 ', 1)[1] if '\x04' in model_output else ''
    assert (ground_truth == prediction)

@changgeli And I forgot to include bounding box localisation and guided OCR: for that, you can do

# bbox contents prediction (OCR)

pretrained_path = "adept/fuyu-8b"
processor = FuyuProcessor.from_pretrained(pretrained_path)
model = FuyuForCausalLM.from_pretrained(pretrained_path, device_map='auto')


bbox_prompt = "When presented with a box, perform OCR to extract text contained within it. If provided with text, generate the corresponding bounding box.\\n<box>388, 428, 404, 488</box>"
bbox_image_url = "https://huggingface.co/datasets/hf-internal-testing/fixtures-captioning/resolve/main/bbox_sample_image.jpeg"
bbox_image_pil = Image.open(io.BytesIO(requests.get(bbox_image_url).content))
model_inputs = processor(text=bbox_prompt, images=bbox_image_pil).to('cuda')


model_outputs = processor.batch_decode(model.generate(
    **model_inputs, max_new_tokens=10)[:, -10:], skip_special_tokens=True)[0]
prediction = model_outputs.split('\x04', 1)[1] if '\x04' in model_outputs else ''

# bbox localisation from text

pretrained_path = "adept/fuyu-8b"
processor = FuyuProcessor.from_pretrained(pretrained_path)
model = FuyuForCausalLM.from_pretrained(pretrained_path, device_map='auto')


bbox_prompt = "When presented with a box, perform OCR to extract text contained within it. If provided with text, generate the corresponding bounding box.\\n Williams"
bbox_image_url = "https://huggingface.co/datasets/hf-internal-testing/fixtures-captioning/resolve/main/bbox_sample_image.jpeg"
bbox_image_pil = Image.open(io.BytesIO(requests.get(bbox_image_url).content))
model_inputs = processor(text=bbox_prompt, images=bbox_image_pil).to('cuda')

outputs = model.generate(**model_inputs, max_new_tokens=10)
post_processed_bbox_tokens = processor.post_process_box_coordinates(outputs)[0]
model_outputs = processor.decode(post_processed_bbox_tokens, skip_special_tokens=True)
prediction = model_outputs.split('\x04', 1)[1] if '\x04' in model_outputs else ''

The last example "bbox localisation from text" seems to not output the '\x04' symbol, and cannot be parsed.

Hello @changgeli ! Are you using the same input image and prompt? Can you verify what's the decoded result before thesplit?

Thank you for reply! The symbol '\x04' can be output. I used '\x04 ' instead of '\x04' as a delimiter, so the parsing failed.
My question is, why do some predictions start with a space, while others do not?

the decoded result of bbox localisation from text:
...||SPEAKER||SPEAKER||SPEAKER||NEWLINE|<s> When presented with a box, perform OCR to extract text contained within it. If provided with text, generate the corresponding bounding box.\\n Williams\x04<box>388, 428, 404, 900</box>'

the decoded result of bbox contents prediction (OCR):
...||SPEAKER||SPEAKER||SPEAKER||NEWLINE|<s> When presented with a box, perform OCR to extract text contained within it. If provided with text, generate the corresponding bounding box.\\n<box>388, 428, 404, 488</box>\x04 Williams'

Did you try tweaking the generation parameters? I think we mostly tried greedy decoding, sampling might change what you encounter.