Fine-tuning Programming Tutorials & Engineering Articles

How we compress production failures into model weights every day, beat frontier-model quality, and cut serving costs 96%.

Fine-tuning LLMs for financial natural language processing (NLP) is constrained by limited, imbalanced data. Real-world financial news overrepresents earnings…

Agentic systems often face a trade-off between accuracy and cost. The highest-performing proprietary frontier models and harnesses provide top accuracy but are…

Production training data only captures successful queries; it can't teach a model when to say no. We built an automated curation pipeline using LLM judge consensus to close that gap.

Developers building real-time AI—such as chat assistants, copilots, and agentic workflows—are often constrained by token-by-token generation speed.

DiffusionGemma is an experimental text-generation model built on the Gemma 4 architecture that uses diffusion-based parallel generation instead of token-by-token autoregression, enabling much faster inference, bidirectional context awareness, and real-time self-correction while remaining deployable on consumer GPUs. Its architecture generates and refines 256-token blocks in parallel through iterative denoising, allowing it to handle complex constraint-based tasks such as Sudoku more effectively than traditional language models and demonstrating strong gains from fine-tuning. The model integrates with vLLM and other popular inference frameworks, giving developers access to a new non-autoregressive approach that combines high performance, efficient long-context scaling, and straightforward customization and deployment.

Training a speech AI model to correctly recognize or synthesize clinical terminology is surprisingly difficult. Drug names like Acetaminophen, Amlodipine…

Google has announced the Google Colab Command-Line Interface (CLI), a new tool that allows developers and AI agents to connect local terminals to remote Colab runtimes for frictionless execution. The lightweight CLI enables users to easily request high-powered GPUs, run local Python scripts remotely, and seamlessly retrieve artifact logs or models like fine-tuned Gemma 3 adapters. By integrating directly into standard terminal environments, the tool is highly programmable and ready to be used by AI agents such as Antigravity or Claude Code to manage complex machine learning pipelines.

The Google Tunix Hackathon on Kaggle challenged developers to transform small, non-reasoning base models into general reasoning engines using Kaggle TPUs and a limited compute budget. The winning teams achieved this by implementing multi-stage post-training pipelines that combined Supervised Fine-Tuning (SFT) with advanced alignment techniques like GRPO and SimPO. Ultimately, the competition democratized AI development by proving that highly capable, structured reasoning models can be successfully trained by the community using accessible, open-source resources.

For decades, computational biology has operated under a reductionist compromise. To fit complex biological systems into the limited memory of a single GPU…

Agentic systems often reason across screens, documents, audio, video, and text within a single perception‑to‑action loop. However, they still rely on fragmented…

DeepSeek just launched its fourth generation of flagship models with DeepSeek-V4-Pro and DeepSeek-V4-Flash, both targeted at enabling highly efficient million…

We fine-tuned Qwen3-32B into a tool-calling agent that generates Flow automations from natural language—faster, cheaper, and more accurate than the frontier model it replaced, with a weekly retraining flywheel built on real merchant data.

NVIDIA Ising is the world’s first family of open AI models for building quantum processors, launching with two model domains: Ising Calibration and Ising…

Autonomous AI agents are driving the next wave of AI innovation. These agents must often manage long-running tasks that use multiple communication channels and…

Agentic AI systems need models with the specialized depth to solve dense technical problems autonomously. They must excel at reasoning, coding…

Autonomous networks are quickly becoming one of the top priorities in telecommunications. According to the latest NVIDIA State of AI in Telecommunications…

How multimodal post-training was engineered for production at Microsoft, system-level interventions that reshaped policy gradient stability, and Copilot agent training at scale.

The article discusses how Painkiller RTX utilizes generative AI to enhance game assets by transforming legacy textures into high-quality Physically Based Rendering (PBR) materials.

Kimi K2. 5 is an advanced multimodal vision language model (VLM) developed by Kimi, optimized for various AI tasks.

This article presents a blueprint for building trustable AI systems, demonstrated through a real-world field test at Thunderhill Raceway where Google Developer Experts built a real-time AI racing c...

This article demonstrates how to fine-tune FunctionGemma, a specialized 270M parameter Gemma 3 model designed for function calling in agentic AI systems.

PinLanding is a multimodal AI pipeline developed by Pinterest to generate shopping collections from billions of products.

The article discusses the optimization of semiconductor defect classification using generative AI and vision foundation models (VFMs).

The article discusses the use of AI Model Distillation to create efficient financial data workflows, focusing on the optimization of large language models (LLMs) for applications in quantitative fi...

The article discusses Spotify's approach to enhancing the Shuffle feature by balancing statistical randomness with user perception.

The article discusses how NVIDIA's CorrDiff model leverages generative AI for downscaling weather predictions, significantly improving efficiency and reducing computational costs.

The article introduces CodonFM, a new state-of-the-art RNA foundation model developed by NVIDIA as part of the Clara open model family.

Netflix introduces Advantage-Weighted Supervised Fine-Tuning (A-SFT), a novel post-training algorithm for generative recommender systems that addresses the unique challenges of applying reinforceme...

The article discusses how the NVIDIA DGX Spark supercomputer enhances performance for intensive AI tasks, providing a local alternative to cloud computing.

The article discusses how to fine-tune and scale large language models (LLMs) using the open-source Unsloth framework on NVIDIA Blackwell GPUs.

The article discusses the development of an AI-powered log analysis solution using NVIDIA's Generative AI reference workflows.

The article discusses how to fine-tune the Gemma 3 270M model for on-device applications, enabling developers to create custom AI models without the need for expensive hardware.

The article discusses the implementation of NVIDIA NV-Tesseract and NVIDIA NIM for smarter anomaly detection in semiconductor manufacturing.

The article discusses Neural Robot Dynamics (NeRD), a neural simulation framework designed to enhance robotics development by accurately predicting the dynamics of articulated robots.

The article provides an in-depth exploration of the EmbeddingGemma architecture, detailing its origins, embedding generation process, and the comprehensive training methodology.

The article discusses three neural innovations from NVIDIA Research that are enhancing robot learning capabilities, specifically focusing on bridging the gap between controlled simulations and real...

The article discusses the integration of AI-powered simulations in computer-aided engineering (CAE) to accelerate design processes.

The article discusses the significance of small language models (SLMs) in the development of scalable agentic AI, emphasizing their efficiency and cost-effectiveness compared to large language mode...

NVIDIA Cosmos Reason is an open and customizable vision language model designed for robotics and physical AI, enabling robots to reason using prior knowledge and common sense.

The article discusses how Netflix has developed an automated quality control method for video content that detects pixel-level artifacts, significantly reducing the need for manual reviews.

This article provides a comprehensive guide on how to train a reasoning-capable language model using NVIDIA NeMo in just 48 hours on a single GPU.

The article discusses Shopify's Global Catalogue, which utilizes multimodal Large Language Models (LLMs) to standardize and enrich product data across its platform.

The article discusses the general availability of Google DeepMind's Gemma 3n on NVIDIA RTX and Jetson platforms, highlighting its capabilities in multi-modal on-device deployment, including audio, ...

The article discusses the contributions of the community to the Unlock Global Communication with Gemma competition on Kaggle, focusing on adapting large language models (LLMs) for diverse linguisti...

The article discusses the operational challenges of deploying large language models (LLMs) and introduces LLMOps as a framework for managing their lifecycle.

The article discusses the NVIDIA AI Blueprint for building efficient AI agents through model distillation, focusing on the challenges of scaling intelligent applications and managing inference cost...

The article discusses Eigenspace Low-Rank Approximation (EoRA), a fine-tuning-free method developed by NVIDIA for compensating compression errors in large language models (LLMs).

The article discusses NVIDIA Cosmos Reason, a world foundation model designed to enhance physical AI by curating synthetic datasets for training robots and autonomous vehicles.