LLaVA 1.6 is here...but is it any good? (via Ollama)

Learn Data with Mark
4 Feb 202405:40

TLDRThe video script discusses the release of version 1.6 of the LAVA model, highlighting its improvements over version 1.5, such as handling higher resolution images, enhanced visual reasoning and OCR capabilities, and better management of conversational scenarios. The host compares the performance of both versions on various tasks, including image description, caption creation, text extraction from images, and code extraction, noting that while 1.6 shows some improvements, there is still room for advancement as seen in the comparison with Chat GPT's results.

Takeaways

  • 🚀 Lava, a multimodal model, has released version 1.6 with several improvements over version 1.5.
  • 📸 Version 1.6 can handle images of greater resolution and has enhanced visual reasoning and OCR capabilities.
  • 💬 The new version also supports more conversational scenarios, providing a more interactive experience.
  • 💻 Lava 1.6 is available on AI and can be used locally with the appropriate setup.
  • 🔄 A comparison between Lava 1.5 and 1.6 shows that the latter provides more detailed descriptions of images.
  • 🎨 When tasked with creating captions for images, Lava 1.6 seems to offer slightly more creative responses.
  • 📝 Lava 1.6 demonstrates better performance in extracting text from images compared to version 1.5.
  • 🔍 Both versions struggle with extracting code from images, but 1.6 shows a marginal improvement.
  • 📊 Lava 1.6 has a better understanding of diagrams and data structures, although it doesn't perfectly describe the differences between relational and graph databases.
  • 🤖 The comparison also includes results from Chat GPT, which provides a more accurate extraction of text from an image.
  • 📚 For those interested in the Alama Python library used in the video, there is a dedicated video for more in-depth information.

Q & A

  • What is the main topic of the video transcript?

    -The main topic of the video transcript is the comparison and review of LAVA version 1.6, a multimodal model, and its improvements over version 1.5.

  • What are the key improvements in LAVA version 1.6 compared to version 1.5?

    -LAVA version 1.6 has several improvements over version 1.5, including the ability to handle images of greater resolution, better visual reasoning and OCR capability, and the capacity to manage more conversational scenarios.

  • How can one access and use LAVA version 1.6?

    -LAVA version 1.6 is available on AI and can be used locally by downloading and launching the model. For Mac users, it will start automatically, while others may need to call a specific command to run it.

  • What was the result of testing LAVA version 1.6 on an image of the presenter looking at a magnifying glass?

    -LAVA version 1.6 provided a more detailed description of the image compared to version 1.5, identifying the man wearing glasses and holding an old magnifying glass.

  • How did LAVA version 1.6 perform in creating a caption for an image of an arrow on bricks?

    -LAVA version 1.6 provided a caption that suggested guidance or direction with a white arrow pointing to the left on a blue brick wall, which was considered slightly more creative than the caption generated by version 1.5.

  • What was the performance of LAVA version 1.6 in extracting text from an image?

    -LAVA version 1.6 demonstrated an improved ability to extract text from an image, accurately identifying the text 'hugging face running a large language model locally on my laptop' from the image.

  • How did LAVA version 1.6 handle extracting code from an image containing Python window functions?

    -LAVA version 1.6 showed some improvement in identifying elements of the code but did not accurately extract the specific code from the image. It mentioned 'C' and 'statistics' but failed to provide the correct code snippet.

  • What was the ability of LAVA version 1.6 to describe a diagram of a relational database versus a graph database?

    -LAVA version 1.6 was able to identify that the image was a diagram of relationships between objects and suggested it was a data structure, likely a graph structure. However, it did not clearly articulate the difference between the two types of databases.

  • How does the Alama Python Library relate to the testing of LAVA models in the video?

    -The Alama Python Library was used in the video to facilitate the testing of LAVA models. It allows for the calling of the LAVA generate function, passing in the model, prompt, and image to receive and display the model's response.

  • What was the overall conclusion from the testing of LAVA version 1.6 against version 1.5?

    -The overall conclusion from the testing was that LAVA version 1.6 showed improvements in various areas such as image resolution handling, visual reasoning, OCR capability, and conversational scenario management. However, there is still room for further enhancement, especially in tasks like code extraction and detailed diagram interpretation.

Outlines

00:00

🚀 Lava Model Version 1.6 Updates and Capabilities

The script discusses the release of Lava model version 1.6, highlighting its improvements over version 1.5. The new version is capable of handling images with higher resolution and has enhanced visual reasoning and OCR capabilities. It also supports more complex conversational scenarios. The script mentions that version 1.6 is available on AI and provides instructions for users to try it out locally. The author compares the performance of Lava 1.5 and 1.6 by testing them with various images and scenarios, including image description, caption generation, text extraction, and code recognition. The detailed comparison showcases the advancements and effectiveness of the updated Lava model in understanding and processing visual and textual data.

05:00

📈 Comparing Lava 1.5 and 1.6 in Image and Text Analysis

This paragraph continues the discussion on Lava model's capabilities by focusing on the practical application and comparison of Lava 1.5 and 1.6. The author runs experiments using images with both models, evaluating their performance in creating captions and extracting text and code from the images. The comparison reveals that while both models perform well, version 1.6 demonstrates a clearer understanding and more accurate extraction of information from the visual data. The paragraph also mentions the use of the Alama Python library and the rich console for displaying results. The author concludes by noting the differences between the models and suggests that further exploration and fine-tuning of prompts can yield better results, as demonstrated by the chat GPT's accurate interpretation of a complex database diagram image.

Mindmap

Keywords

💡multimodal model

A multimodal model refers to an artificial intelligence system capable of processing and understanding multiple types of data inputs, such as text, images, and audio. In the context of the video, the Lava model is described as a large multimodal model, indicating its ability to handle various data formats, which is crucial for its improved performance in tasks like image resolution handling and visual reasoning.

💡image resolution

Image resolution is the measure of the amount of detail an image contains, typically expressed in pixels. A higher resolution means a more detailed and clearer image. In the video, it is mentioned that the Lava model's new version can handle images of greater resolution, implying an improvement in the model's ability to process and analyze more detailed visual data.

💡visual reasoning

Visual reasoning involves the ability to interpret and draw conclusions from visual information, such as images or diagrams. In the context of AI, it refers to the model's capability to understand and make inferences from visual content. The video emphasizes that Lava 1.6 has better visual reasoning, indicating that the model can now more effectively analyze and interpret visual data.

💡OCR capability

OCR, or Optical Character Recognition, is the technology that enables the conversion of different types of documents, such as scanned paper documents, PDF files or images captured by a camera into editable and searchable data. In the video, the Lava model's OCR capability is mentioned as having been improved in version 1.6, suggesting that the model can now more accurately recognize and extract text from images.

💡conversational scenarios

Conversational scenarios refer to the contexts or situations in which a conversational AI model operates, including the ability to understand and participate in human-like dialogues. In the video, the Lava model's update is said to handle more conversational scenarios, indicating an improvement in its natural language processing and understanding, allowing it to engage in a wider range of interactions and discussions.

💡Al LL

Al LL, or AI Language Library, is presumably a library or platform used for developing and interacting with AI models like Lava. The video suggests that Al LL is a place where the updated Lava model is available for use, indicating that it is a resource for accessing and experimenting with the latest AI capabilities.

💡Llama run

In the context of the video, 'Llama run' seems to be a command or action used to execute the Lava model's functions. It is implied that this is a method to activate the model and process inputs such as images or text, showcasing the model's capabilities in response to specific tasks.

💡Python Library

A Python Library is a collection of modules, functions, and classes that can be imported into a Python program to perform specific tasks or operations. In the video, the 'Alama python Library' is mentioned, which is likely a tool or framework designed to facilitate the use of AI models like Lava within Python environments.

💡caption

A caption, in the context of images, is a text description that provides an explanation or commentary on the visual content. The video script mentions creating captions for images as a task for the Lava model, demonstrating its application in generating textual content based on visual input.

💡text extraction

Text extraction is the process of identifying and pulling out textual information from various sources, such as images or documents. In the video, the Lava model's ability to extract text from images is tested, highlighting the model's OCR capabilities and its application in converting visual text into editable text formats.

💡code extraction

Code extraction refers to the process of identifying and extracting source code from images or other visual formats. This is particularly useful for tasks like reproducing software code displayed in images or understanding programming examples from screenshots. In the video, the Lava model's attempt to extract code from an image illustrates its ability to recognize and interpret programming languages within visual contexts.

💡data modeling

Data modeling is the process of creating a conceptual or logical representation of data and the relationships between data elements. It is a critical aspect of database design and management. In the video, a diagram showing the difference between relational and graph databases is used to test the Lava model's ability to understand and describe data modeling concepts.

Highlights

Lava, a large multimodal model, has released version 1.6 with several improvements over version 1.5.

Version 1.6 can handle images of greater resolution compared to its previous version.

The new version boasts better visual reasoning and OCR capabilities.

Lava 1.6 is capable of handling more conversational scenarios.

Lava 1.6 is available on AI platforms for users to try out.

The user demonstrates running Lava 1.5 and 1.6 side by side to compare their performance.

Lava 1.6 provides more detailed descriptions of images compared to version 1.5.

The user tests the models' ability to create captions for images and notes a slight improvement in creativity with 1.6.

Lava 1.6 shows better performance in extracting text from images compared to 1.5.

There was an issue with Lava 1.5 when extracting text from an image, but 1.6 performed similarly with minor improvements.

The user attempts to extract code from an image using Lava 1.5 and 1.6, with limited success.

Chat GPT is shown to extract code from an image more accurately than Lava 1.5 and 1.6.

Lava 1.6 identifies a database diagram's structure better than 1.5, but neither can succinctly explain the difference between relational and graph databases.

Chat GPT is able to accurately describe the difference between tabular data representation and graph models in a database diagram.

The Alama Python Library is used to facilitate the interaction with Lava models.

The video provides a detailed comparison of Lava 1.5 and 1.6's capabilities.

The user's experience shows that Lava 1.6 has made strides in image recognition and text extraction.

Despite improvements, Lava 1.6 still has room for enhancement when compared to other AI products like Chat GPT.

The video serves as a practical guide for users interested in trying out Lava 1.6.

The user's testing methodology involves comparing the outputs of Lava 1.5 and 1.6 using various image inputs.

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