metadata
library_name: transformers
license: apache-2.0
language:
- en
tags:
- image-text-to-text
- text-to-text
- image-text-to-image-text
pipeline_tag: image-text-to-text
LeroyDyer/Mixtral_AI_Cyber_Q_Vision
VisionEncoderDecoderModel is a generic model class that will be instantiated as a transformer architecture with one of the base vision model classes of the library as encoder and another one as decoder when created with the :
transformers.AutoModel.from_pretrained class method for the encoder and :
transformers.AutoModelForCausalLM.from_pretrained class method for the decoder.
Model Description
This is the model card of a 🤗 transformers model that has been pushed on the Hub.
This is an experiment in vision - the model has been created as a mistral/VisionEncoder/Decoder
Customized from:
- Mixtral_AI_Cyber_Matrix_2.0(7b)
- TinyMistral (248)
- ikim-uk-essen/BiomedCLIP_ViT_patch16_224
- Developed by: [LeroyDyer]
- Model type: [image-text-to-image-text]
- Language(s) (NLP): [English]
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]
[More Information Needed]
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
Model Architecture and Objective
from transformers import MistralConfig, ViTConfig, VisionEncoderDecoderConfig, VisionEncoderDecoderModel
# Initializing a ViT & Mistral style configuration
config_encoder = ViTConfig()
config_decoder = MistralConfig()
config = VisionEncoderDecoderConfig.from_encoder_decoder_configs(config_encoder, config_decoder)
# Initializing a ViTMistral model (with random weights) from a ViT & Mistral style configurations
model = VisionEncoderDecoderModel(config=config)
# Accessing the model configuration
config_encoder = model.config.encoder
config_decoder = model.config.decoder
# set decoder config to causal lm
config_decoder.is_decoder = True
config_decoder.add_cross_attention = True
# Saving the model, including its configuration
model.save_pretrained("my-model")
# loading model and config from pretrained folder
encoder_decoder_config = VisionEncoderDecoderConfig.from_pretrained("my-model")
model = VisionEncoderDecoderModel.from_pretrained("my-model", config=encoder_decoder_config)