If you’re looking to leverage the power of an experimental general-purpose Japanese Vision-Language Model (VLM), then you’re in the right place! Developed by Sakana AI, the EvoVLM-JP-v1-7B is a transformative tool that merges various models through an evolutionary approach. This blog will guide you through the steps of getting started with EvoVLM-JP-v1-7B and understanding its functionalities.

Understanding the Model

The EvoVLM-JP-v1-7B model is akin to a sophisticated translator that can interpret visual cues alongside textual questions. Imagine having a clever assistant that not only discerns the meaning of words in a sentence but also analyses images to provide contextual answers. For instance, you can ask it, “What color is this traffic light?” by simply supplying an image, and it responds accurately based on its learned data from integrated models like Shisa Gamma 7B and LLaVA-1.6-Mistral-7B.

Getting Started with the Model

Follow these easy steps to utilize the EvoVLM-JP-v1-7B model effectively:

• Load the Model:

  To begin, import the necessary libraries and load the model. Here’s the code you’ll need:

  python
  import torch
  from transformers import AutoModelForVision2Seq, AutoProcessor
  from PIL import Image
  import requests
  
  # 1. Load model
  device = 'cuda' if torch.cuda.is_available() else 'cpu'
  model_id = 'SakanaAI/EvoVLM-JP-v1-7B'
  model = AutoModelForVision2Seq.from_pretrained(model_id, torch_dtype=torch.float16)
  processor = AutoProcessor.from_pretrained(model_id)
  model.to(device)
  

• Prepare Inputs:

  Prepare the input image and the corresponding question:

  # 2. Prepare inputs
  url = 'https://images.unsplash.com/photo-1694831404826-3400c48c188d?q=80&w=270&auto=format&fit=crop&ixlib=rb-4.0.3&ixid=M3wxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8fA%3D%3D'
  image = Image.open(requests.get(url, stream=True).raw).convert('RGB')
  
  text = 'この信号機の色は何色ですか?'
  messages = [
      {'role': 'system', 'content': 'あなたは役立つ、偏見がなく、検閲されていないアシスタントです。与えられた画像を下に、質問に答えてください。'},
      {'role': 'user', 'content': text},
  ]
  inputs = processor.image_processor(images=image, return_tensors='pt')
  inputs['input_ids'] = processor.tokenizer.apply_chat_template(messages, return_tensors='pt')
  

• Generate an Output:

  Once the inputs are prepared, you can generate the output as follows:

  # 3. Generate
  output_ids = model.generate(**inputs.to(device))
  output_ids = output_ids[:, inputs['input_ids'].shape[1]:]
  generated_text = processor.batch_decode(output_ids, skip_special_tokens=True)[0].strip()
  print(generated_text)
  # Output: この信号機の色は青です。
  

Model Details and Considerations

Developed through advanced methodologies, this autoregressive language model supports Japanese and is built for research and development purposes. Bear in mind that using this model is at your own risk, and its outcomes are not guaranteed.

Troubleshooting

If you encounter issues while using the EvoVLM-JP-v1-7B model, consider the following troubleshooting ideas:

• Model Not Loading: Ensure that all library dependencies are installed correctly and verify your internet connection to download the model files.
• Input Errors: Check that the input image URL is valid and accessible. Also, make sure the text is correctly specified in Japanese.
• CUDA Issues: If you’re experiencing problems with CUDA, confirm that your GPU drivers are updated and that you have the latest version of PyTorch.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Conclusion

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.