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LLM Deep-dive: Solar 10.7B

SOLAR 10.7B's introduction showcases a transformative step in LLMs, blending Llama 2's architecture with Mistral 7B's weights for unparalleled performance. Notably, its success in single-turn conversations, as reflected by its impressive Model H6 score, marks a new industry benchmark. This breakthrough underscores South Korea's rising prominence in AI, promising innovative applications of LLMs across diverse fields.

By
Matouš Eibich, Stefan Wendin & Marcus Zethraeus
February 23, 2024

Working with Sensitive Data and LLMs

The synergy between sensitive data and LLMs marks a significant step forward in sectors like healthcare and finance. The insights derived from data can revolutionize services and outcomes. However, this journey must be underpinned by a strong commitment to ethical data use, robust protection strategies, and respect for privacy. In this article we explore three approaches to combine LLMs with sensitive data, while protecting data integrety.

By
Sarthak Arora & Marcus Zethraeus
January 12, 2024

LLM Deep-dive: Gemini

The introduction of Google's Gemini model to the competitive landscape of Large Language Models, with its advanced multimodal capabilities, is a noteworthy event. Its impressive performance could be a game-changer if further evaluations uphold Google's claims. However, the model's true standing, particularly in comparison to GPT-4, will hinge on unbiased, independent validation in the times ahead. 

By
Matouš Eibich & Stefan Wendin
January 9, 2024

LLM Deep-dive: Phi-2

Microsoft's Phi-2 model represents a significant shift in the landscape of Large Language Models, challenging the notion that bigger models always equate to better performance. With its 2.7 billion parameters, Phi-2 rivals the performance of much larger models like Llama and Mistral, underscoring the power of its meticulously curated 'textbook-quality' training dataset.

By
Matouš Eibich
January 9, 2024

LLM Deep-dive: Mixtral 8x7b

This model, with its unique Mixture of Experts (MoE) architecture, marks a significant advancement, offering a blend of efficiency and capability that challenges even the best open-source LLMs. Key Highlights of Mixtral 8x7b: Unparalleled performance in the open-source domain. The MoE architecture enhances efficiency and processing speed. High VRAM requirements orient it towards research and enterprise applications. While Mixtral 8x7b may not rival the giants like GPT-4 in all aspects, it represents a significant step for open-source AI, hinting at a future where powerful AI tools are more accessible. Discover the depths of Mixtral 8x7b in our latest deep dive

By
Matous Eibich, Marcus Zethraeus & Stefan Wendin
December 15, 2023

How to choose the right LLM for your use-case

Choosing between convenient proprietary or customizable open source large language models involves balancing rapid prototyping against long-term costs and data security. The optimal approach depends on use case breadth and security needs.

By
Predli
December 11, 2023

Supercharge how you interact with proprietary documents with LiQA

LiQA leverages AI to transform enterprise document search. Proprietary files are ingested, converted to vectors, and indexed for personalized QA. Questions retrieve relevant excerpts to contextualize answers. Ongoing improvements will enhance accuracy, efficiency, and knowledge sources. LiQA unlocks the potential of your organization's documents.

By
Predli
September 25, 2023

Using the Language Powerhouse for Effective Content Generation

Our team explored using LLMs like GPT3.5 for controlled content generation from seed data. We designed prompts and evaluation methods to quantify quality. LLMs possess great potential but need guidance. Exciting times ahead!

By
Predli
September 25, 2023

Predli and the AI for Good Foundation Partner to Advance UN’s 2030 Agenda

Stockholm, May 28, 2021 — Predli announced today a collaboration with the AI for Good Foundation to accelerate work on the UN Sustainable Development Goals (UNSDGs) and address the most pressing challenges faced by our communities.

By
Predli
April 11, 2022

Future of Manufacturing

While we see manufacturers fiddling with AI and machine learning, Industry 4.0 is still a moonshot for many, including top Fortune 500 companies. The reasoning is simple, too many companies are stuck in the “pilot purgatory” phase. This is the state where companies have an idea that has moved to the proof of concept (PoC) phase, but instead of reaching customers, it ends up at the infamous PoC graveyard.

By
Yash Agrawal and Alexander Fred-Ojala
April 11, 2022