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--- |
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library_name: transformers |
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license: apache-2.0 |
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language: |
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- en |
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--- |
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# LeroyDyer/Mixtral_AI_Cyber_Q_Vision |
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<!-- Provide a quick summary of what the model is/does. --> |
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## Model Details |
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### Model Description |
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<!-- Provide a longer summary of what this model is. --> |
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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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- **Developed by:** [LeroyDyer] |
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- **Model type:** [More Information Needed] |
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- **Language(s) (NLP):** [English] |
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### Recommendations |
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> |
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. |
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## How to Get Started with the Model |
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```python |
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from transformers import AutoProcessor, VisionEncoderDecoderModel |
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import requests |
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from PIL import Image |
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import torch |
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processor = AutoProcessor.from_pretrained("LeroyDyer/Mixtral_AI_Cyber_Q_Vision") |
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model = VisionEncoderDecoderModel.from_pretrained("LeroyDyer/Mixtral_AI_Cyber_Q_Vision") |
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# load image from the IAM dataset |
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url = "https://fki.tic.heia-fr.ch/static/img/a01-122-02.jpg" |
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image = Image.open(requests.get(url, stream=True).raw).convert("RGB") |
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# training |
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model.config.decoder_start_token_id = processor.tokenizer.eos_token_id |
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model.config.pad_token_id = processor.tokenizer.pad_token_id |
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model.config.vocab_size = model.config.decoder.vocab_size |
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pixel_values = processor(image, return_tensors="pt").pixel_values |
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text = "hello world" |
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labels = processor.tokenizer(text, return_tensors="pt").input_ids |
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outputs = model(pixel_values=pixel_values, labels=labels) |
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loss = outputs.loss |
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# inference (generation) |
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generated_ids = model.generate(pixel_values) |
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generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0] |
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``` |
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[More Information Needed] |
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## Training Details |
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```python |
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from transformers import ViTImageProcessor, AutoTokenizer, VisionEncoderDecoderModel |
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from datasets import load_dataset |
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image_processor = ViTImageProcessor.from_pretrained("LeroyDyer/Mixtral_AI_Cyber_Q_Vision") |
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tokenizer = AutoTokenizer.from_pretrained("LeroyDyer/Mixtral_AI_Cyber_Q_Vision") |
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model = VisionEncoderDecoderModel.from_encoder_decoder_pretrained( |
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"LeroyDyer/Mixtral_AI_Cyber_Q_Vision", "LeroyDyer/Mixtral_AI_Cyber_Q_Vision" |
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) |
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model.config.decoder_start_token_id = tokenizer.cls_token_id |
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model.config.pad_token_id = tokenizer.pad_token_id |
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dataset = load_dataset("huggingface/cats-image") |
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image = dataset["test"]["image"][0] |
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pixel_values = image_processor(image, return_tensors="pt").pixel_values |
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labels = tokenizer( |
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"an image of two cats chilling on a couch", |
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return_tensors="pt", |
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).input_ids |
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# the forward function automatically creates the correct decoder_input_ids |
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loss = model(pixel_values=pixel_values, labels=labels).loss |
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``` |
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### Model Architecture and Objective |
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``` python |
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from transformers import MistralConfig, ViTConfig, VisionEncoderDecoderConfig, VisionEncoderDecoderModel |
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# Initializing a ViT & Mistral style configuration |
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config_encoder = ViTConfig() |
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config_decoder = MistralConfig() |
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config = VisionEncoderDecoderConfig.from_encoder_decoder_configs(config_encoder, config_decoder) |
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# Initializing a ViTBert model (with random weights) from a ViT & Mistral style configurations |
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model = VisionEncoderDecoderModel(config=config) |
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# Accessing the model configuration |
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config_encoder = model.config.encoder |
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config_decoder = model.config.decoder |
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# set decoder config to causal lm |
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config_decoder.is_decoder = True |
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config_decoder.add_cross_attention = True |
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# Saving the model, including its configuration |
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model.save_pretrained("my-model") |
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# loading model and config from pretrained folder |
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encoder_decoder_config = VisionEncoderDecoderConfig.from_pretrained("my-model") |
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model = VisionEncoderDecoderModel.from_pretrained("my-model", config=encoder_decoder_config) |
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``` |
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