Welcome to the world of artificial intelligence where the combination of vision and language is taking us to new heights! In this blog, we will dive into how to install and implement the DeepSeek-VL model, an open-source Vision-Language model designed for a variety of real-world applications. Buckle up as we journey through the process step-by-step!

1. Introduction

DeepSeek-VL represents a significant leap in multimodal understanding, enabling systems to interpret logical diagrams, web pages, formula recognition, scientific literature, and natural images. This model is like a Swiss Army knife for AI, equipped to handle a plethora of tasks that involve both vision and language processing.

2. Model Summary

The standout feature of DeepSeek-VL-7b-base is its hybrid vision encoder, which utilizes the SigLIP-L and SAM-B. It supports large images (1024 x 1024 pixels) and is built upon the DeepSeek-LLM-7b-base, trained on an impressive corpus of 2 trillion text tokens, culminating in a model trained on approximately 400 billion vision-language tokens!

3. Quick Start

Installation

To get started, ensure you have a Python 3.8 environment. Then, install the necessary dependencies by executing the following commands:

git clone https://github.com/deepseek-ai/DeepSeek-VL.git
cd DeepSeek-VL
pip install -e .

Simple Inference Example

This is where the magic happens! Think of this process as setting up a conversation between a user and the model, where each user’s message is like a piece of a puzzle, and the model connects and provides a final, coherent picture.

Here’s a breakdown:

• First, prepare your environment by importing the necessary libraries.
• Next, specify the model path where DeepSeek-VL is stored, just like marking the spot on a treasure map.
• Then, load your images and the model. Picture this as gathering all your tools before starting a project.
• Finally, run the model to interpret the inputs and get responses, similar to presenting your assembled puzzle and revealing the beautiful image it forms.

python
import torch
from transformers import AutoModelForCausalLM
from deepseek_vl.models import VLChatProcessor, MultiModalityCausalLM
from deepseek_vl.utils.io import load_pil_images

# specify the path to the model
model_path = 'deepseek-ai/deepseek-vl-7b-base'
vl_chat_processor = VLChatProcessor.from_pretrained(model_path)
tokenizer = vl_chat_processor.tokenizer
vl_gpt = AutoModelForCausalLM.from_pretrained(model_path, trust_remote_code=True)
vl_gpt = vl_gpt.to(torch.bfloat16).cuda().eval()

conversation = [
    {
        "role": "User",
        "content": "Describe each stage of this image.",
        "images": ["./images/training_pipelines.png"]
    },
    {
        "role": "Assistant",
        "content": ""
    }
]

# load images and prepare for input
pil_images = load_pil_images(conversation)
prepare_inputs = vl_chat_processor(
    conversations=conversation,
    images=pil_images,
    force_batchify=True
).to(vl_gpt.device)

# run image encoder to get the image embeddings
inputs_embeds = vl_gpt.prepare_inputs_embeds(**prepare_inputs)

# run the model to get the response
outputs = vl_gpt.language_model.generate(
    inputs_embeds=inputs_embeds,
    attention_mask=prepare_inputs.attention_mask,
    pad_token_id=tokenizer.eos_token_id,
    bos_token_id=tokenizer.bos_token_id,
    eos_token_id=tokenizer.eos_token_id,
    max_new_tokens=512,
    do_sample=False,
    use_cache=True
)
answer = tokenizer.decode(outputs[0].cpu().tolist(), skip_special_tokens=True)
print(f"{prepare_inputs['sft_format'][0]}: {answer}") 

CLI Chat

If you prefer to engage directly via the command line interface, you can run:

bash
python cli_chat.py --model_path deepseek-ai/deepseek-vl-7b-base
# or local path
python cli_chat.py --model_path local_model_path

4. License

The code repository is licensed under the MIT License. Additionally, the usage of DeepSeek-VL BaseChat models is governed by the DeepSeek Model License, promoting commercial use of these models.

5. Troubleshooting

If you encounter issues during installation or running the model, consider these troubleshooting tips:

• Ensure you have the correct version of Python installed (Python = 3.8).
• Check your internet connection in case dependencies fail to download.
• Verify that the model path you provided is correct.
• Look for specific error messages in the console – they are usually your best friends in debugging!

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

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.

6. Conclusion

Now you’re equipped with everything you need to start leveraging DeepSeek-VL for your vision-language tasks! Embrace the power of multimodal understanding and enhance your projects with cutting-edge AI technology. Happy coding!