Machine Learning Articles

Discover how AirLLM enables running 70B parameter LLMs on 4GB GPUs and 405B models on 8GB VRAM through innovative layer-wise inference streaming. Complete setup guide, real code examples, and performance optimization tips included.

Anemll unlocks Apple Neural Engine for local LLM inference, enabling private, efficient running of LLaMA, Qwen, Gemma models on Mac and iOS. Complete setup guide with real code examples from the open-source project.

CatVTON is an ICLR 2025 virtual try-on diffusion model achieving 1024×768 inference under 8GB VRAM through elegant concatenation-based design. With only 49.57M trainable parameters, it democratizes fashion AI for developers and researchers.

Discover how docext eliminates OCR pain with vision-language models for on-premises document extraction. Convert PDFs to structured markdown, extract fields with confidence scoring, and benchmark models transparently.

GLM-OCR ranks #1 on OmniDocBench V1.5 with 94.62 score. This 0.9B parameter open-source multimodal OCR model delivers state-of-the-art formula recognition, table extraction, and layout understanding via vLLM, SGLang, and cloud deployment.

Discover Gemma, Google DeepMind's open-weights LLM library built on JAX. Learn installation, multi-modal usage, fine-tuning with LoRA, and why developers are choosing open models over proprietary APIs.

My-Jogyo is a multi-agent research automation system that transforms OpenCode into a scientific laboratory. With Professor (Gyoshu) planning, TA (Jogyo) executing, and PhD reviewer (Baksa) verifying, it produces reproducible notebooks with statistical rigor—automatically.

Master GPU optimization with the Infatoshi CUDA course. Learn kernel programming, Triton, and PyTorch extensions through 11 structured modules—from first kernels to production-ready extensions. Free, comprehensive, and deep learning-native.

OpenCompass is the universal LLM evaluation platform endorsed by Meta AI, supporting 100+ datasets and models from GPT-4 to Llama3. Learn installation, real code examples, and advanced benchmarking strategies in this complete guide.

TurboQuant-GPU achieves 5.02x KV cache compression for LLM inference with 0.98 cosine similarity. Learn how this open-source tool outperforms NVIDIA's FP4 formats, works on any GPU, and installs in seconds.

NVIDIA's DreamZero achieves #1 robot benchmark rankings with zero-shot generalization using world action models. Learn how to deploy, fine-tune, and extend this open-source robotics breakthrough.

Discover NVIDIA NeMo DataDesigner, the open-source framework generating 350B+ tokens of production-grade synthetic data with LLMs, statistical samplers, and dependency-aware pipelines. Complete guide with real code examples.

PointNet revolutionized 3D deep learning by processing raw point clouds directly, eliminating inefficient voxel grids. Learn installation, implementation, and why researchers abandoned old approaches.

Tired of broken JSON from LLMs? Discover Instructor, the open-source library that delivers reliable structured outputs with Pydantic validation. Used by 100K+ developers at OpenAI, Google, and Microsoft.

llmfit is a Rust-powered terminal tool that automatically matches LLM models to your hardware. Detects GPU/CPU/RAM, scores hundreds of models across quality, speed, fit and context dimensions, and tells you what actually runs on your machine.

Graph-CAD is an ICLR 2026 graph-mediated Text-to-CAD framework that achieves 90%+ geometric constraint satisfaction by using hierarchical decomposition graphs as intermediate representations, dramatically outperforming GPT-5 and Claude on manufacturable 3D generation.

Discover how AgentGym-RL trains 7B parameter LLM agents to surpass GPT-4o on real-world multi-turn tasks using progressive horizon scaling reinforcement learning. Complete setup guide, code examples, and performance benchmarks included.

Stop wasting money on bad trading bots. Discover Awesome-Quant-Machine-Learning-Trading, the ruthlessly curated resource collection that elite quantitative developers use to build profitable ML-driven trading systems. From de Prado's foundational techniques to production-ready RL environments.