Ollama has introduced a new inference engine specifically designed for multimodal models. This engine allows models to seamlessly process and generate both text and images within a single context window. Unlike previous methods that relied on separate models or complex pipelines, Ollama's new engine natively supports multimodal data, enabling developers to create more sophisticated and interactive applications. This unified approach simplifies the process of building and deploying multimodal models, offering improved performance and a more streamlined workflow. The engine is compatible with the GGML format and supports various model architectures, furthering Ollama's goal of making powerful language models more accessible.
Vert.sh is an open-source, self-hostable file conversion service. It leverages LibreOffice in the backend to handle a wide array of document, image, and presentation formats. Users can easily deploy Vert.sh using Docker and configure it to their specific needs, maintaining complete control over their data privacy. The project aims to provide a robust and versatile alternative to cloud-based conversion tools for individuals and organizations concerned about data security and vendor lock-in.
Hacker News users generally expressed enthusiasm for the open-source, self-hostable file converter Vert.sh, praising its simplicity and potential usefulness. Several commenters highlighted the benefit of avoiding uploads to third-party services for privacy and security reasons, with some mentioning specific use cases like converting ebooks. A few users questioned the project's long-term viability and maintainability given the potential complexity of handling numerous file formats and dependencies. Some also suggested alternative self-hosted solutions like Pandoc and Soffice/LibreOffice. The discussion also touched on the challenges of sandboxing potentially malicious files uploaded for conversion, with some proposing using Docker or virtual machines for enhanced security.
Summary of Comments ( 60 )
https://news.ycombinator.com/item?id=44001087
Hacker News users discussed Ollama's potential, praising its open-source nature and ease of use compared to setting up one's own multimodal models. Several commenters expressed excitement about running these models locally, eliminating privacy concerns associated with cloud services. Some highlighted the impressive speed and low resource requirements, making it accessible even on less powerful hardware. A few questioned the licensing of the models available through Ollama, and some pointed out the limited context window compared to commercial offerings. There was also interest in the possibility of fine-tuning these models and integrating them with other tools. Overall, the sentiment was positive, with many seeing Ollama as a significant step forward for open-source multimodal models.
The Hacker News post titled "Ollama's new engine for multimodal models" (linking to https://ollama.com/blog/multimodal-models) sparked a discussion with several interesting comments.
Several users discussed the potential impact of Ollama's local approach to running multimodal models. One user expressed excitement about the possibility of running these models locally, highlighting the privacy benefits compared to cloud-based solutions and the potential to incorporate personalized data without sharing it with external services. Another user echoed this sentiment, emphasizing the significance of local processing for sensitive data and the potential for more customized and personalized experiences. They also speculated on the possibility of federated learning with locally trained models being aggregated into more robust versions.
The practicality of running these models on resource-constrained devices was also a topic of discussion. One commenter questioned the feasibility of running large models on devices like phones or Raspberry Pis, given the substantial hardware requirements. This prompted another user to elaborate on the challenges of mobile deployment, pointing out the need for quantization and other optimization techniques. They also suggested that certain tasks, like image captioning, might still be viable even with limited resources.
The conversation also touched on the competitive landscape of multimodal models. One commenter compared Ollama to other models like GPT-4V and Gemini, suggesting that Ollama offers greater transparency due to its open-source nature. They also mentioned the rapid pace of development in the field and the potential for disruption.
Another user pointed out the potential of this technology for assistive devices, envisioning applications like real-time descriptions for visually impaired users.
Finally, there was a technical discussion about the specific optimizations used by Ollama, including quantization and the use of GGML (a machine learning library). One user speculated on the future potential of hardware acceleration for tasks like matrix multiplication.
Overall, the commenters expressed a mix of enthusiasm and pragmatism regarding the potential of Ollama's new engine. While acknowledging the practical challenges, they recognized the significant benefits of local, privacy-preserving multimodal models and the potential for a wider range of applications.