Pixtral 12B Model Review: Great for Images, Not So Much for Multilingual
TLDRIn this AI Anytime video, the host reviews the Pixtral 12B model by Mistral AI, a French AI company. The model is praised for its multimodal capabilities, handling high-quality images and long context lengths. It shows promise in OCR and information extraction but falls short in multilingual support, particularly for Hindi. The video demonstrates the model's performance through various tests, including image description and article writing from an architecture diagram, with mixed results. The host encourages viewers to test the model themselves and share their findings.
Takeaways
- 🌐 Pixtral is a multimodal model by Mistral AI, a French AI startup.
- 🚀 It's capable of processing text and images simultaneously.
- 🖼️ Pixtral can handle high-quality images up to 1024x1024 pixels.
- 📄 The model boasts a context length of 128k tokens, suitable for complex tasks.
- 🔍 Pixtral shows promise in OCR and information extraction.
- 💾 Requires at least an A100 GPU for inference, with 50GB recommended for safety.
- 🔑 Access to the model involves accepting an agreement on the Hugging Face repository.
- 🤖 VM Library is recommended for high-throughput, memory-efficient inference.
- 🔗 An HF token is necessary for access, which can be obtained from Hugging Face.
- 📊 Mixed results in testing: Pixtral performed well with English but failed with Hindi language support.
- 📝 Good for generating articles from diagrams but needs better context for specificity.
Q & A
What is the name of the multimodal model discussed in the video?
-The multimodal model discussed in the video is called Pixtral.
Which company developed the Pixtral model?
-Pixtral was developed by a French AI company called Mistral AI.
What is special about the Pixtral model's ability to process images?
-The Pixtral model can process high-quality images of up to 1024x1024 pixels without any restrictions on image quality.
What is the context length that Pixtral can handle?
-Pixtral has a context length of 128k tokens, which is quite impressive for processing large amounts of text.
What are some use cases where Pixtral performs well?
-Pixtral performs well in OCR (Optical Character Recognition) and information extraction tasks.
What are the system requirements to run the Pixtral model?
-To run the Pixtral model, you need at least an A100 GPU or equivalent, with a minimum of 50 GB of disk space recommended for inference.
How can one access the Pixtral model repository?
-The Pixtral model repository can be accessed through a Hugging Face account, where one must agree to their terms and conditions to gain access.
What is the minimum GPU requirement for running the Pixtral model on Google Colab Pro?
-The minimum GPU requirement for running the Pixtral model on Google Colab Pro is an A00 GPU.
What library is recommended for inference with large AI models like Pixtral?
-The VM (Vicuna Model) library is recommended for inference with large AI models like Pixtral due to its high throughput and memory efficiency.
What was the reviewer's experience with Pixtral's multilingual capabilities?
-The reviewer found Pixtral to be disappointing for Hindi language support but performed well for other languages, suggesting it might be more European-focused.
What was the outcome of the Pixtral model's test with an architecture diagram?
-Pixtral was able to write an article explaining the architecture diagram, which was considered a good performance for blog writers and content creators.
Outlines
🌐 Introduction to Mistral AI's Multimodal Model
The speaker introduces a new multimodal model called 'pixal' by Mistral AI, a French AI company known for its open-source contributions. Mistral AI offers both open-source and commercial models through Mistral Cloud. The model is capable of processing text and images simultaneously and can handle high-quality images up to 1024x1024 pixels. It also supports a context length of 128k tokens, which is significant for natural language processing tasks. The video aims to explore the model's performance, particularly in OCR and information extraction, comparing it to other multimodal models like Alibaba Cloud's Q and 2 VL.
💾 Setting Up the Multimodal Model
The speaker outlines the steps to set up the pixal model, starting with accessing the model file from a Hugging Face repository under an Apache 2.0 license. The model requires a minimum of 50 GB of space for inference. The speaker also discusses the installation of Mistral common and the VM library, which is recommended for efficient inference of large AI models. The process includes obtaining an HF token for authentication and setting up the model and tokenizer with specified parameters such as the maximum model length.
📸 Testing the Model with an Image
The speaker proceeds to test the pixal model by uploading an image and defining a system prompt to describe the image. The process involves creating a user role, defining content with type and text, and setting up the image URL. The speaker runs the model and prints the messages to check the input. The model's response to extracting information from an invoice image in Hindi is tested, revealing a lack of Hindi support and highlighting the importance of accurate multimodal understanding.
📊 Analyzing the Model's Performance
The speaker evaluates the model's performance by asking it to explain an architecture diagram and write an article based on it. The model's response is mixed; it fails to support Hindi but performs well in explaining the architecture diagram and writing a generic article. The speaker suggests that the model might be more focused on European languages due to its French origin and encourages viewers to test the model with different images and languages.
📉 Final Thoughts and Encouragement to Explore
In conclusion, the speaker shares mixed reactions to the pixal model, noting its limitations with Hindi but appreciating its capabilities in other areas. The speaker encourages viewers to try the model with different images and provide feedback. They also remind viewers to like, subscribe, and comment on the video, and mention that the Notebook for the video will be available on GitHub.
Mindmap
Keywords
💡multimodal model
💡Mistral AI
💡visual grounding
💡context length
💡OCR
💡Hugging Face
💡VM Library
💡inference
💡high-level info
💡prompt
Highlights
Pixtral 12B Model is the first multimodal model by Mistral AI, a French AI startup.
Pixtral is capable of processing text and image simultaneously.
The model can process high-quality images up to 1024x1024 pixels.
It boasts a context length of 128k tokens, suitable for complex tasks.
Pixtral shows promise for OCR and information extraction.
To use Pixtral, a minimum of an A00 GPU is required.
The model is available on Hugging Face with an Apache 2.0 license.
VM Library is recommended for efficient inference.
An HF token is needed to access the model on Hugging Face.
The model can be tested by uploading an image and defining a prompt.
Pixtral failed to process a Hindi invoice image correctly.
The model performed well in explaining an architecture diagram.
Pixtral generated a good article from an architecture diagram.
The model showed potential in interpreting a stock image with numerical data.
Mixed reactions to Pixtral's performance on multilingual tasks.
The video provides a detailed tutorial on how to use Pixtral.
The Notebook for this review will be available on GitHub.