ML Frameworks

It turns Claude, Codex, or OpenCode into hyperparameter optimizers that actually read your parameter docs before proposing the next trial.

Pits PatchTST, TFT, and N-HiTS against CatBoost to help energy markets pick a forecasting model.

TabFM exists so you can run classification and regression on messy, mixed-type tables without retraining a model on your data.

It split off from `verl` to give diffusion, video, and omni-modality models an RL post-training framework that doesn't treat them like chatbots.

KTransformers makes CPU-GPU heterogeneous inference and fine-tuning for massive MoE models almost practical on consumer hardware.

Curated technical deep-dives covering everything from NVLink signal integrity to Kubernetes GPU scheduling and Huawei NPU porting.

This engine decouples RL training and inference into independent GPU services, letting text, vision, and audio models post-train asynchronously at scale.

Google's edge ML runtime grows up, adds async NPU support, and finally admits PyTorch exists.

This book teaches modern GPU kernel programming as a hardware-first progression, using the Blackwell architecture and a Python IR DSL called TIRx to move from concepts to production-grade kernels.

A Chinese-language notebook curriculum that teaches LLM inference engineering by rebuilding vLLM and SGLang internals in Python.

Chinese open-source community Datawhale built a from-zero embodied-AI course that gets you from `print('hello')` to fine-tuning SmolVLA and Pi0.

One framework to run RL post-training on diffusion, autoregressive, and hybrid models without rewriting the orchestration layer each time.

MiniMind-O packs listen-see-speak intelligence into a 0.1B-parameter model you can retrain from the first line of code on a single desktop GPU.

A PyTorch implementation of "Attention Is All You Need" that scales from 13M to multi-billion parameter models.

A tiny deep-learning model that guesses what a file actually contains, not just what its extension claims.

It corrals the latest subquadratic sequence-model research into hardware-efficient, training-ready PyTorch layers verified across NVIDIA, AMD, and Intel GPUs.

A library of 98 structured skill packs that turns coding agents into end-to-end AI researchers, from distributed training to LaTeX submission.

RLinf exists because fine-tuning policies for robots and agents still requires rewriting your training stack for every new simulator, world model, or hardware rig.

MLX-VLM crams speculative decoding, continuous batching, and KV cache quantization into a Mac-native toolkit for running multimodal models locally.

OpenMed packages clinical entity extraction and HIPAA-grade de-identification into models small enough for Apple Silicon and impatient DevOps teams.