Image-to-Text
Transformers
Safetensors
English
mistral
text-generation
vision
VISION-ENCODER-DECODER-MODEL
text-generation-inference
Instructions to use LeroyDyer/SpydazWebAI_VisionEncoderDecoderModel_Mini548m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LeroyDyer/SpydazWebAI_VisionEncoderDecoderModel_Mini548m with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "image-to-text" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # 'pip install "transformers<5.0.0' from transformers import pipeline pipe = pipeline("image-to-text", model="LeroyDyer/SpydazWebAI_VisionEncoderDecoderModel_Mini548m")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("LeroyDyer/SpydazWebAI_VisionEncoderDecoderModel_Mini548m") model = AutoModelForCausalLM.from_pretrained("LeroyDyer/SpydazWebAI_VisionEncoderDecoderModel_Mini548m") - Notebooks
- Google Colab
- Kaggle
metadata
license: mit
language:
- en
library_name: transformers
tags:
- vision
- VISION-ENCODER-DECODER-MODEL
pipeline_tag: image-to-text
ADD HEAD
Mistral
VISION-ENCODER-DECODER-MODEL
print('Add Vision...')
# ADD HEAD
# Combine pre-trained encoder and pre-trained decoder to form a Seq2Seq model
Vmodel = VisionEncoderDecoderModel.from_encoder_decoder_pretrained(
"google/vit-base-patch16-224-in21k", "LeroyDyer/Mixtral_AI_Tiny"
)
_Encoder_ImageProcessor = Vmodel.encoder
_Decoder_ImageTokenizer = Vmodel.decoder
_VisionEncoderDecoderModel = Vmodel
# Add Pad tokems
LM_MODEL.VisionEncoderDecoder = _VisionEncoderDecoderModel
# Add Sub Components
LM_MODEL.Encoder_ImageProcessor = _Encoder_ImageProcessor
LM_MODEL.Decoder_ImageTokenizer = _Decoder_ImageTokenizer
LM_MODEL