fine-tuning
fine-tuning on Beyond Market Intelligence: a running collection of 16 stories we have gathered and hand-picked because they are worth your time. Every post here touches on fine-tuning in some way — the news, the analysis, the deep dives, and the occasional surprise find. Acme AI is the next-generation, AI-powered spreadsheet platform built to replace Excel and redefine how analysts, data scientists, and enterprise teams work with data. New stories are added to this page as we find them, so check back if you want to keep up with what is happening around fine-tuning, or subscribe to the RSS feed to get them as soon as they are published. Browse the collection below, or head back to the homepage to see everything Beyond Market Intelligence is covering right now.
![Continual Learning of Frontier Models for SovereignAI. Tech Report + Open Weights Model [R]](https://preview.redd.it/3fzb6dga0ilh1.png?width=640&crop=smart&auto=webp&s=28aa5b3250dc5aab05341f6874be2181cbd67ce4)
Continual Learning of Frontier Models for SovereignAI. Tech Report + Open Weights Model [R]
Frontier AI model development is often perceived as the domain of large, well-funded organizations, creating an imbalance in access and power. This report challenges that notion, arguing that continual learning on readily available open-weight models empowers a wider range of institutions to achieve frontier performance and build SovereignAI capabilities. Introducing Thomson, a new model demonstrating competitive results across diverse domains—including agentic tasks and multilingualism—with significantly reduced compute costs. As highlighted in our recent article, "Prompt injection ranks No.
Hyperparameters fine tuning for MARL comparative study [D]
Evaluating multi-agent reinforcement learning (MARL) architectures demands rigorous methodology. A common challenge arises when optimal hyperparameters—learning rates, entropy coefficients, and batch sizes—vary across different model configurations. While unifying hyperparameters can appear advantageous for fair comparison, it risks hindering convergence. This study investigates the robustness of PPO variants (Independent PPO, Graph PPO, etc.) under adversarial attack, necessitating careful consideration of hyperparameter tuning. See "Continual Learning of Frontier Models" for related insights into model development.
Nvidia just showed that the harness, not the AI model, is now the real hero
Recent Nvidia research demonstrates a pivotal shift in AI development: the harness, or the system surrounding the AI model, is now paramount to performance and stability. Findings show that careful fine-tuning of these systems can enable robust AI agent behavior, even with less sophisticated underlying models. This signals a move away from solely focusing on model size and towards optimizing the environment in which AI operates. Explore this concept further in our related article, "Epistemic Intelligence in Machine Learning Neurips Workshop page limit?
How to Fine-Tune an LLM: An End-to-End Guide
Ready to move beyond pre-trained LLMs and unlock their full potential? Our comprehensive guide, "How to Fine-Tune an LLM: An End-to-End Guide," provides a practical, hands-on approach to tailoring these powerful models for real-world applications. Explore the process, from data preparation to evaluation, and discover how fine-tuning can dramatically improve performance on specific tasks. For a deeper dive into the complexities of LLM evaluation, see our article, "The LLM Judge That Kept Agreeing With Itself," and empower your data journey.
Why Capital One built its multi-agent AI platform around open-weight models
At VB Transform 2026, Capital One’s Kel Vanee detailed the bank’s strategic shift toward building AI, not just using it. Capital One constructed a scalable, multi-agent AI platform centered around deeply customized open-weight models, leveraging proprietary data for enhanced accuracy and extensibility. This approach, underpinned by prior investments in data transformation and cloud adoption, enables the bank to optimize workflows, from fraud detection to customer service, and even automate internal infrastructure tuning.
Stanford is running 37,000 AI agents as a virtual biotech — and one of its drug designs got independently confirmed by Merck
Stanford University’s pioneering research demonstrates a transformative shift in AI development: scaling to tens of thousands of specialized agents. James Zou's team has built a "Virtual Biotech" – emulating a corporate structure with 37,000 agents – that autonomously designs drug candidates. Notably, one such design was independently validated by Merck, receiving FDA breakthrough designation. The key? Orchestration via a novel platform, Paperclip, which digitizes data and creates an AI-native virtual file system.
5 Free Courses to Learn Modern AI and LLMs
Unlock the potential of generative AI with our five free courses, designed to empower you with modern skills. Explore building Retrieval-Augmented Generation (RAG) and agentic applications, fine-tuning models, and navigating the Hugging Face ecosystem. These hands-on resources equip you to prototype AI products and seamlessly integrate AI into your workflows. Ready to transform your data journey? For deeper insights into AI governance, consider our article on "Azure API Management Adds Dedicated AI Gateway Tier."
Ex-Spotify employees raise $10M to bring the AI behind its recommendations to e-commerce
A team of former Spotify engineers is pioneering a new era in e-commerce with $10 million in funding. Their startup’s platform leverages AI, mirroring the recommendation engine behind Spotify's success, to predict shopper behavior. It anticipates the next product a customer desires, learns their preferences, and continuously refines its predictions in real-time. This innovative approach promises to transform online shopping experiences. For further insights into automating complex processes, explore our article on "Naïve raises $28.5M" and its approach to infrastructure automation.
Beyond Bots: Rethinking AI Support with a Hybrid AI Architecture
Traditional AI support often falls short, leaving users frustrated. Beyond Bots explores a transformative approach: a hybrid AI architecture blending Retrieval-Augmented Generation (RAG) and fine-tuning. This combination delivers more effective and nuanced support experiences, moving beyond simple chatbot interactions. Discover how this innovative blend empowers seamless problem-solving and boosts user satisfaction. For deeper insights into the evolving AI landscape, explore our coverage of the recent venture by Jeff Dean and other top AI researchers.
Thinking Machines debuts Inkling Small open source AI model nearing performance of predecessor at about 1/4 size
Thinking Machines has unveiled Inkling-Small, a groundbreaking open-source AI model demonstrating remarkable efficiency. Nearing the performance of its predecessor, Inkling, this new model achieves this at roughly one-quarter the size, surpassing it on several key benchmarks. Released under a permissive Apache 2.0 license, Inkling-Small offers enterprises a compelling blend of power and practicality, reducing compute requirements and deployment complexities. Explore this transformative solution and discover how it can empower your data journey—a clear signal that enterprise AI is rapidly evolving.
5 Must-Read Resources for Mastering Small Language Models
## 5 Must-Read Resources for Mastering Small Language Models Data professionals seeking to leverage Small Language Models (SLMs) require a focused skillset. To that end, we’ve curated five essential resources covering critical areas: SLM architecture, effective fine-tuning strategies, practical agentic workflows, and secure local deployment. These resources offer a clear path to mastery, empowering you to integrate SLMs into your data strategies. For deeper insights into securing AI deployments, explore our article, "Securing MCP in Production: Defense-in-Depth Beyond the Gateway."
Multi-Tenant SaaS: Which Architecture Would You Choose? [D]
Navigating multi-tenant SaaS architectures for sensitive data, particularly with RAG and LLMs, demands careful consideration. For your Sri Lankan document platform, a global RAG layer alongside user-specific RAG (Option 1) presents a compelling starting point. It avoids the complexities and costs of fine-tuning while enabling access to a curated knowledge base for accurate, general responses, supplemented by private document search. Scalability to thousands of users is readily achievable with this design.
How to pick an AI model in 2026
Navigating the AI model landscape in 2026 will demand a strategic approach. Choosing the right model requires prioritizing specific task performance, cost-effectiveness, and integration capabilities. Expect a market saturated with specialized models, making broad, general-purpose options less appealing. Focus on evaluating models based on rigorous benchmarks and real-world application testing. Consider scalability and ongoing maintenance costs as critical factors. For deeper insights into optimizing infrastructure alongside AI investment, explore our article, "Uber’s Zero Growth Stack."
Kimi K3's full weights are here, but they're 'open' with a caveat: What enterprises should know
Moonshot AI has released the full weights for Kimi K3, its powerful new AI model, marking a significant step for open-weight AI. While broadly accessible, enterprises should carefully review the custom Kimi K3 usage license. Larger organizations operating a "Model as a Service" exceeding $20 million in revenue, or those with products impacting over 100 million users, face specific commercial obligations, including potential licensing agreements and prominent attribution.
Presentation: Engineering AI for Creativity and Curiosity on Mobile
Join us for a compelling presentation by Bhavuk Jain, exploring the engineering behind bringing powerful AI to mobile devices. Jain details the challenges and solutions in translating foundational AI into scalable products like AI Wallpapers and Circle to Search, focusing on runtime guardrails, fine-tuning, and OS integration. This session offers critical insights for engineering leaders navigating the balance between user experience, model latency, and infrastructure costs—essential for delivering safe and reliable AI experiences.
The qlora 2e-4 default is wrong under 10k samples and nobody talks about it [D]
Fine-tuning QLoRA models on smaller datasets—less than 10,000 samples—often leads to unexpected results. The pervasive default learning rate of 2e-4, widely promoted across tutorials and documentation, can actually trigger overfitting. Extensive experimentation reveals that a starting learning rate of 1e-4 or lower, combined with increased epochs, consistently yields significantly improved evaluation metrics. This adjustment, easily implemented, can save practitioners considerable time and frustration, as detailed in a recent discussion about ECCV expenses.