library_name: transformers
tags: []
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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How to Get Started with the Model
from transformers import AutoProcessor, VisionEncoderDecoderModel
import requests
from PIL import Image
import torch
processor = AutoProcessor.from_pretrained("LeroyDyer/Mixtral_AI_Cyber_Q_Vision")
model = VisionEncoderDecoderModel.from_pretrained("LeroyDyer/Mixtral_AI_Cyber_Q_Vision")
# load image from the IAM dataset
url = "https://fki.tic.heia-fr.ch/static/img/a01-122-02.jpg"
image = Image.open(requests.get(url, stream=True).raw).convert("RGB")
# training
model.config.decoder_start_token_id = processor.tokenizer.eos_token_id
model.config.pad_token_id = processor.tokenizer.pad_token_id
model.config.vocab_size = model.config.decoder.vocab_size
pixel_values = processor(image, return_tensors="pt").pixel_values
text = "hello world"
labels = processor.tokenizer(text, return_tensors="pt").input_ids
outputs = model(pixel_values=pixel_values, labels=labels)
loss = outputs.loss
# inference (generation)
generated_ids = model.generate(pixel_values)
generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
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Training Details
from transformers import ViTImageProcessor, AutoTokenizer, VisionEncoderDecoderModel
from datasets import load_dataset
image_processor = ViTImageProcessor.from_pretrained("LeroyDyer/Mixtral_AI_Cyber_Q_Vision")
tokenizer = AutoTokenizer.from_pretrained("LeroyDyer/Mixtral_AI_Cyber_Q_Vision")
model = VisionEncoderDecoderModel.from_encoder_decoder_pretrained(
"LeroyDyer/Mixtral_AI_Cyber_Q_Vision", "LeroyDyer/Mixtral_AI_Cyber_Q_Vision"
)
model.config.decoder_start_token_id = tokenizer.cls_token_id
model.config.pad_token_id = tokenizer.pad_token_id
dataset = load_dataset("huggingface/cats-image")
image = dataset["test"]["image"][0]
pixel_values = image_processor(image, return_tensors="pt").pixel_values
labels = tokenizer(
"an image of two cats chilling on a couch",
return_tensors="pt",
).input_ids
# the forward function automatically creates the correct decoder_input_ids
loss = model(pixel_values=pixel_values, labels=labels).loss
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Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
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