Pixtral 12B Model Review: Great for Images, Not So Much for Multilingual
TLDRThis AI Anytime video reviews the Pixtral 12B model by Mistral AI, a French startup. The multimodal model excels at processing high-quality images and text simultaneously, with a context length of 128k tokens. It performs well in OCR and information extraction but struggles with multilingual support, particularly Hindi. The video demonstrates the model's capabilities through various prompts and image inputs, showing mixed results. It also touches on the installation process and the need for specific hardware and software requirements.
Takeaways
- 🌐 Pixtral is Mistral AI's first multimodal model designed for processing both text and images simultaneously.
- 🚀 It supports high-quality image processing, with no restrictions on image size up to 1024x1024 pixels.
- 🧠 The model boasts a substantial context length of 128k tokens, allowing for complex and detailed information processing.
- 🔍 Pixtral performs well in OCR and information extraction tasks, similar to other advanced multimodal models like IMP V3, eix9B, and Flamingo.
- 💾 To use Pixtral, a minimum of 50GB of disk space is recommended for model inference.
- 🔑 Access to the model requires obtaining an Hugging Face (HF) token and accepting the repository's agreement.
- 🛠️ VM (Vicuna) is the recommended library for inference due to its high throughput and memory efficiency.
- 🔗 The model can be tested by uploading an image and defining a prompt for the AI to respond to.
- 📈 Pixtral had mixed results in multilingual support; it failed to process a Hindi invoice accurately but performed well with other languages.
- 📝 For content creation, Pixtral was able to write a comprehensive article based on an architecture diagram, showing potential for blog writing and similar tasks.
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?
-The Pixtral model was developed by a French AI company called Mistral AI.
What is unique about the Pixtral model's capabilities with images?
-The Pixtral model can process high-quality images of up to 1024x1024 resolution.
What is the context length that the Pixtral model can handle?
-The Pixtral model has a context length of 128k tokens.
What are some potential use cases for the Pixtral model mentioned in the video?
-Some potential use cases for the Pixtral model include OCR (Optical Character Recognition) and information extraction.
What is the minimum GPU requirement to run the Pixtral model?
-The minimum GPU requirement to run the Pixtral model is an A100 GPU.
How can one access the Pixtral model repository?
-To access the Pixtral model repository, one needs to accept the agreement on the Hugging Face repository.
What is the size of the Pixtral model file?
-The Pixtral model file is approximately 25.4 GB in size.
What library is recommended for inference with the Pixtral model?
-The recommended library for inference with the Pixtral model is VM (Vicuna Model).
What is the performance of the Pixtral model with multilingual support?
-The performance of the Pixtral model with multilingual support is mixed, as it did not perform well with Hindi language support during the test in the video.
What was the outcome when the Pixtral model was tested with an architecture diagram?
-When tested with an architecture diagram, the Pixtral model was able to write an article explaining the architecture, which was considered good in the video.
Outlines
🌐 Introduction to Mistral AI's Multimodal Model
The video introduces a new multimodal AI model called 'Pixal' by Mistral AI, a French startup that's well-backed in the open-source AI space. Pixal is Mistral AI's first multimodal model, capable of processing both text and images simultaneously. The presenter mentions the growing trend of multimodal models with visual grounding capabilities and compares Pixal to Alibaba Cloud's Q and 2 VL model. The video aims to test Pixal's performance, and the presenter shares that a minimum of an A100 GPU is required to run the 12b model. High-level information about Pixal is provided, including its ability to process high-quality images up to 1024x1024 pixels and its context length of 128k tokens, which is significant for natural language processing tasks. The presenter also discusses potential use cases, such as OCR and information extraction, and mentions other multimodal models like IMP V3, eix 9B, Flamingo, and Florange 2.
🔑 Accessing and Setting Up Pixal Model
The presenter guides viewers on how to access the Pixal model from the Hugging Face repository, which requires accepting an agreement to access the model file. The file size is mentioned to be 25.4 GB, and it's recommended to have at least 50 GB of space for safe inference. The video then covers the installation of necessary libraries, including 'mistal-common' and 'VM', which is a high-throughput, memory-efficient inference serving engine. The presenter also discusses the need for an Hugging Face (HF) token, which is obtained from the Hugging Face website, and demonstrates how to create and use this token for model access. The process involves defining the model and tokenizer, setting a maximum model length, and handling potential errors related to URL access.
📸 Testing Pixal with Image Description
The presenter demonstrates how to use the Pixal model to describe an image. He defines a system prompt message with a user role, creates a dictionary with content, and specifies the type of input as text or image URL. The video shows the process of defining the prompt, uploading an image, and running the model to generate a description. The presenter tests the model's ability to describe an invoice image in Hindi and discusses the model's performance, noting that it did not meet expectations for Hindi language support. He encourages viewers to test the model with different images and languages to evaluate its capabilities.
📊 Analyzing an Architecture Diagram
The presenter challenges the Pixal model to explain an architecture diagram step by step. He sets up the prompt and runs the model, noting that the output was generic but included key elements like the app server's connection to the UI and data sources. The presenter suggests that the model might be better suited for certain tasks, like writing articles, and shares a positive review of the model's ability to generate a high-level explanation of an architecture from a diagram. He also mentions the need for better context in the prompt for more accurate results.
📉 Evaluating Pixal's Performance on Financial Data
In the final test, the presenter asks the Pixal model to interpret an image of NVIDIA's stock summary and list all findings. The model provides a detailed and organized summary, including current stock price, market information, and dividend details. The presenter expresses satisfaction with the model's performance on this task, indicating that it could be useful for blog writers and those needing to interpret financial data visually. He concludes the video by inviting viewers to share their feedback and findings with the Pixal model and reminds them to like and subscribe for more content.
Mindmap
Keywords
💡multimodal model
💡Mistral AI
💡visual grounding
💡context length
💡OCR
💡Hugging Face
💡VM Library
💡inference
💡multilanguage support
💡GitHub
Highlights
Introduction to the Pixtral 12B Model by Mistral AI, a French AI company.
Pixtral is Mistral AI's first multimodal model, capable of processing text and images simultaneously.
The model supports high-quality image processing up to 1024x1024 pixels.
It has an impressive context length of 128k tokens.
Use cases include OCR and information extraction.
The model is available on Hugging Face with an Apache 2.0 license.
Requirements include at least a T4 GPU for inference.
Mistral AI recommends using the VM library for efficient inference.
Instructions on how to access the model repository and generate an HF token.
The model can process prompts and images to generate descriptions.
Disappointment with the model's performance on Hindi language tasks.
The model's potential for generating articles based on architecture diagrams.
Positive feedback on the model's ability to interpret and summarize stock information from images.
Mixed reactions to the model's multilingual capabilities.
Recommendation to test the model with different images and languages.
The video will provide a notebook on GitHub for further exploration.
Encouragement for viewers to share their findings and thoughts on the Pixtral 12B Model.
