Baili Lu

black-forest-labs/FLUX.2-klein-9B is crazy fun! Under FLUX Non-Commercial License, it bring: - Real-time AI agents with visual reasoning. - Instant, iterative design workflows. - Low-latency production pipelines on consumer-grade hardware.  - Sub-second speed - Text-to-Image + Native Editing in one pipeline. - Fits in ~20GB VRAM (RTX 4090 ready). The 4-step distilled version is a game-changer for anyone looking to deploy high-quality vision models without the massive VRAM overhead. #GenerativeAI #MachineLearning #FLUX2 #OpenSource #AIEngineering

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We (w/ Shansan Gong, Ruixiang ZHANG, Huangjie Zheng, Jiatao Gu, Navdeep Jaitly, Lingpeng Kong) released a family of 7B diffusion language models, DiffuCoder, that specializes on code generation, with a focus on understanding and improving masked diffusion models. A core analysis of DiffuCoder is the autoregressiveness (AR-ness) score, a novel metric that quantifies the causal patterns in decoding, revealing how diffusion models break from strict left-to-right generation for more flexible, non-linear code planning. Recent advances in autoregressive (AR) models dominate code generation, but diffusion-based LLMs (dLLMs) like DiffuCoder offer a promising alternative, especially for complex programming tasks. DiffuCoder explores how these models decode differently—showing less global AR-ness in code tasks compared to math—and how temperature affects both token selection and generation order, unlike traditional AR models. We also introduce coupled-GRPO, a post-training RL method with a coupled-sampling scheme, to reduce performance drops during accelerated decoding, boosting parallelism and efficiency. We use a self-improvement pipeline that leverages AR-ness analysis, coupled-GRPO optimization, and evaluation on benchmarks like AceCode-89k to refine decoding strategies. This approach enables DiffuCoder to navigate diverse code generation pathways and enhance performance with modest computational overhead. Looking ahead, we aim to further leverage Reinforcement Learning to steer code generation through these decoding patterns, with the discrete nature of AR-ness scores providing a foundation for search-based strategies—ideal for the sparse rewards of optimizing complex code structures. Check out our full paper and code for a deeper dive! Paper: https://lnkd.in/gVWU3BDJ Code: https://lnkd.in/gmXTZ_6n Models: https://lnkd.in/gTcKCDr9 #MachineLearning #AI #CodeGeneration #DiffusionModels #NLP

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🎉 New paper from #NVIDIAResearch 🧩 Puzzle: Distillation-Based NAS for Inference-Optimized LLMs+ Puzzle is a distillation-based NAS approach, that transforms trained LLMs into far more efficient, inference-aware (heterogeneous) architectures.

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We developed an open-source AI tool to design concrete mixes that are stronger, more sustainable, and ready to build with faster—speeding up construction while reducing environmental impact. https://lnkd.in/gPCk8tCM But the impact of this AI tool is not just hypothetical! Amrize used Meta’s AI-based technologies to design a new low-carbon mix, and successfully deployed it in an at-scale slab-on-grade application at Meta's new data center in Rosemont, MN. Compared to the legacy mix, this new AI-designed mix is: 🦁 Stronger ⏱ Faster 🍃 Lower carbon ⏱ ️The ideal set time All this was achieved without needing any new materials, nor special equipment. Best of all, the AI is open-sourced. https://lnkd.in/g2KA7KZW This work was featured in a Meta engineering blog article published today! https://lnkd.in/gBU9HY8H

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NVIDIA invests $150M in AI inference platform Baseten at $5B valuation, validating CEO Jensen Huang's thesis that inference will dwarf model training markets. San Francisco startup serves Cursor and Notion, positioning as "AWS for inference" optimized for H100/B200 GPUs—critical as enterprises shift from AI pilots to production deployment. $585M total funding reflects inference infrastructure's premium positioning in maturing AI value chains. https://lnkd.in/d-6iu8YW #NVIDIA #AIInference #EnterpriseAI #GPUInfrastructure

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When you’re building with LLMs, every prompt change or model update is an experiment. 🧪 But without the right visibility, you're flying blind. That’s why we launched Experiment Runs: a powerful new way to track, compare, and understand how every change impacts your AI system’s performance. ✨ With side-by-side comparisons and evaluator scores in one view, you can: ✔️ Instantly see if a new prompt actually improved outcomes ✔️ Compare model responses without switching tabs ✔️ Spot regressions in system behavior at a glance Whether you’re testing new prompts, switching models, or refining your retrieval logic, this overview gives you fast, actionable insight so your AI systems get better with every iteration. Check out our documentation in the comments below for more information! 👇🏼 #GenerativeAI #LLMOps #AIEvaluation #PromptEngineering

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Document-centric tasks sit at the core of many enterprise, business, and government workflows. Search is important, but the real challenge is going beyond retrieval, enabling systems to reason across documents, verify facts, and handle multi-step information tasks. Great to see a new model from Databricks moving in this direction. I expect we’ll see many more models, including open-weight ones, designed specifically for document-driven workflows, an area with huge potential across enterprise and government use cases.

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In this episode, we're joined by Lin Qiao, CEO and co-founder of Fireworks AI. Drawing on key lessons from her time building PyTorch, Lin shares her perspective on the modern generative AI development lifecycle. She explains why aligning training and inference systems is essential for creating a seamless, fast-moving production pipeline, preventing the friction that often stalls deployment. We explore the strategic shift from treating models as commodities to viewing them as core product assets. Lin details how post-training methods, like reinforcement fine-tuning (RFT), allow teams to leverage their own proprietary data to continuously improve these assets. Lin also breaks down the complex challenge of what she calls "3D optimization"—balancing cost, latency, and quality—and emphasizes the role of clear evaluation criteria to guide this process, moving beyond unreliable methods like "vibe checking." Finally, we discuss the path toward the future of AI development: designing a closed-loop system for automated model improvement, a vision made more attainable by the exciting convergence of open and closed-source model capabilities. 🎧 / 🎥 Listen or watch the full episode on our page: https://twimlai.com/go/742.

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