Source URL: https://simonwillison.net/2024/Nov/18/pixtral-large/
Source: Simon Willison’s Weblog
Title: Pixtral Large
Feedly Summary: Pixtral Large
New today from Mistral:
Today we announce Pixtral Large, a 124B open-weights multimodal model built on top of Mistral Large 2. Pixtral Large is the second model in our multimodal family and demonstrates frontier-level image understanding.
The weights are out on Hugging Face (over 200GB to download, and you’ll need a hefty GPU rig to run them). The license is free for academic research but you’ll need to pay for commercial usage.
The new Pixtral Large model is available through their API, as models called pixtral-large-2411 and pixtral-large-latest.
Here’s how to run it using LLM and the llm-mistral plugin:
llm install -U llm-mistral
llm keys set mistral
# paste in API key
llm mistral refresh
llm -m mistral/pixtral-large-latest describe -a https://static.simonwillison.net/static/2024/pelicans.jpg
The image shows a large group of birds, specifically pelicans, congregated together on a rocky area near a body of water. These pelicans are densely packed together, some looking directly at the camera while others are engaging in various activities such as preening or resting. Pelicans are known for their large bills with a distinctive pouch, which they use for catching fish. The rocky terrain and the proximity to water suggest this could be a coastal area or an island where pelicans commonly gather in large numbers. The scene reflects a common natural behavior of these birds, often seen in their nesting or feeding grounds.
Via @dchaplot
Tags: vision-llms, mistral, llm, generative-ai, ai, llms
AI Summary and Description: Yes
Summary: The text announces the release of Pixtral Large, a multimodal AI model capable of advanced image understanding. It highlights significant aspects including the open weights available for academic research, API access, and the detailed procedure to utilize the model, which appeals to professionals in AI and cloud computing sectors.
Detailed Description:
The release of Pixtral Large from Mistral marks an important advancement in multimodal artificial intelligence, particularly in the realm of image processing. This model, built on Mistral Large 2, represents a significant leap in capabilities, offering users advanced tools for understanding images.
Key points of significance:
– **Model Specifications**:
– Pixtral Large is a 124 billion parameter model, showcasing frontier-level image understanding.
– The model is intended for multimodal applications, which suggests it can process text and visual inputs concurrently.
– **Accessibility**:
– The weights of Pixtral Large are available for download on Hugging Face, which is a widely used platform for sharing ML models.
– The size of the model (over 200GB) necessitates comprehensive hardware for deployment, highlighting the infrastructural considerations for users.
– **Licensing**:
– The model is free for academic research, providing an excellent opportunity for educational and experimental use.
– Commercial usage requires a paid license, indicating the commercial interest and potential applications in various industries.
– **API Integration**:
– The model can be accessed through Mistral’s API, with specific commands provided to facilitate users in installing and running the model effectively.
– Instructions on how to utilize the model via the LLM and llm-mistral plugin are detailed, promoting ease of use for developers.
– **Practical Implications**:
– The ability to describe and process images, with an example using pelican imagery, illustrates the model’s practical utility in image analysis and potentially impacts various sectors, including environmental monitoring, wildlife research, and more.
– The mention of image data showcases the model’s ability to assist professionals in achieving operational insights through visual data.
In summary, Pixtral Large is pertinent for professionals in AI and cloud computing as it represents groundbreaking capabilities in multimodal models with practical applications across various fields. The approach towards ease of integration ensures that this model can be swiftly incorporated into existing workflows, enhancing both educational and commercial avenues in AI-driven solutions.