---
library_name: transformers
license: apache-2.0
language:
- en
---

# LeroyDyer/Mixtral_AI_Cyber_Q_Vision

<!-- Provide a quick summary of what the model is/does. -->



## Model Details

### Model Description

<!-- Provide a longer summary of what this model is. -->

This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.

- **Developed by:** [LeroyDyer]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [English]





### Recommendations

<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

## How to Get Started with the Model

```python
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

```python


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

``` python

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 ViTBert 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)


```