Pixtral 12B Model Review: Great for Images, Not So Much for Multilingual

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.

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.

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.

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.

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.