Revolutionizing Data Extraction: Alama's Structured Outputs and Vision Models

- Authors
- Published on
- Published on
In this riveting episode from the channel Sam Witteveen, the team delves into the thrilling world of structured outputs in Alama. This groundbreaking addition allows for a structured passing of text and data extraction from images, revolutionizing the way tasks are handled. With the introduction of structured outputs, Python users can now set up classes with pantic to finely tune how outputs are structured, providing a level of control like never before. The team showcases code examples, illustrating how this feature can be utilized for simple tasks and even building apps using a vision model to extract valuable information. This is the kind of innovation that gets the adrenaline pumping, offering a glimpse into the future of AI technology.
The video emphasizes the beauty of simplicity, highlighting the fact that complex agent frameworks are not always necessary. By directly writing Python or JavaScript code, users can tailor their applications to perform specific tasks efficiently. Moreover, the ability to leverage large language models locally without relying on external APIs opens up a world of possibilities. The demonstration of extracting entities using classes and validating structured outputs showcases the power and precision of this new feature. It's like witnessing a high-speed race where every move is calculated and executed flawlessly.
Furthermore, the comparison between different versions of Alama models sheds light on the iterative process of fine-tuning for optimal results. The team's exploration of analyzing images of bookshelves and extracting book details using custom prompts and the Alama 3.2 Vision model adds a thrilling dimension to the discussion. The potential of extracting track listings from album covers without the need for an agent framework is a testament to the versatility and ingenuity of this technology. By structuring outputs with descriptions and nesting objects, the team demonstrates how to extract valuable information efficiently. This is the kind of cutting-edge technology that leaves you on the edge of your seat, eager to see what's next in the world of AI.

Image copyright Youtube

Image copyright Youtube

Image copyright Youtube

Image copyright Youtube
Watch Building a Vision App with Ollama Structured Outputs on Youtube
Viewer Reactions for Building a Vision App with Ollama Structured Outputs
User learning AI opensource with Python and Ollama
Request for a simple example of fine-tuning a vision model with Ollama
Appreciation for the channel's practical development how-to content
Difficulty in keeping up with new releases
Interest in using NER with LLMs and comparison to SpaCy
Curiosity about using Miles and IA
Request for video on required specs for using LLMs locally
Limitation of Llama vision model to only pictures and structured output
Interest in extracting information from invoices and saving into Excel using structured output
Request for in-depth tutorial on finetuning models for improved accuracy
Question on the possibility of intelligent document processing and classification with open source vision models
Inquiry about using the model for getting coordinates of objects in images
Request for a video on using vision-based models for reading and describing images in a document
Curiosity about the system prompt response and the significance of 2025
Experience with model performance depending on the model itself
Comment on the hacking required for results not being production quality
Request for Hindi audio track availability
Appreciation for the useful content
Request for support of regular expressions with pydantic pattern field
Related Articles

Quen's qwq 32b Model: Local Reasoning Powerhouse Outshines Deep seek R1
Quen introduces the powerful qwq 32b local reasoning model, outperforming the Deep seek R1 in benchmarks. Available on Hugging Face for testing, this model offers top-tier performance and accessibility for users interested in cutting-edge reasoning models.

Microsoft's F4 and 54 Models: Revolutionizing AI with Multimodal Capabilities
Microsoft's latest F4 and 54 models offer groundbreaking features like function calling and multimodal capabilities. With billions of parameters, these models excel in tasks like OCR and translation, setting a new standard in AI technology.

Unveiling OpenAI's GPT 4.5: Underwhelming Performance and High Costs
Sam Witteveen critiques OpenAI's GPT 4.5 model, highlighting its underwhelming performance, high cost, and lack of innovation compared to previous versions and industry benchmarks.

Unleashing Ln AI's M OCR: Revolutionizing PDF Data Extraction
Discover Ln AI's groundbreaking M OCR model, fine-tuned for high-quality data extraction from PDFs. Unleash its power for seamless text conversion, including handwriting and equations. Experience the future of OCR technology with Ln AI's transparent and efficient solution.