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Builds and trains deep learning models from one Python API across JAX, TensorFlow, PyTorch, and OpenVINO inference. Its real value is portability: model code, custom layers, and data pipelines can move across backends instead of locking into one stack.
Patches Hugging Face Transformers and TRL with hand-written Triton kernels to fine-tune LLMs on a single consumer GPU up to 30x faster with about 90% less memory. Does LoRA, QLoRA, and full fine-tuning across 500+ models, exporting to GGUF and Safetensors.
Trains and fine-tunes diffusion models on consumer GPUs: LoRA and LoKr for image families like FLUX.1/2, SDXL and Qwen-Image, plus video models such as Wan 2.x and LTX. Layer-specific targeting, configurable VRAM, and a browser dashboard for runs.
Trains gradient-boosted tree models across local and distributed environments, with bindings for Python, R, JVM, Julia, and C++. Its sparsity-aware split finding and quantile sketch made it a default baseline for tabular ML competitions.
End-to-end quantitative trading framework that covers feature engineering, model training, signal generation and live execution; includes vnpy.alpha for ML-driven strategy research and workflow management, aimed at production trading and research pipelines.
Builds and deploys machine learning models across research, production, web, mobile, and edge environments. Its ecosystem spans Keras, TFX, LiteRT, TensorFlow.js, datasets, model hubs, and visualization tools.
Trains gradient-boosted decision trees for classification, ranking, and large-scale tabular ML with lower memory use and faster training. GOSS and EFB help it handle high-dimensional sparse data on CPU, GPU, and distributed setups.
Trains gradient-boosted decision trees with native categorical-feature handling, GPU acceleration, and production-ready prediction APIs. A strong fit for tabular ML when preprocessing categories into numeric features would add noise or leakage.
Sequence modeling toolkit for training custom models for translation, summarization, and language modeling. Reference implementation behind RoBERTa, BART, mBART, XLM-R, and wav2vec 2.0, with multi-GPU and mixed-precision training.
Scales any Python or ML workload across CPUs and GPUs with a few decorators, instead of rewriting code for Spark or MPI. Bundles libraries for distributed training, hyperparameter tuning, RL, batch inference, and online model serving on one cluster.
Rust-native, event-driven trading platform for backtesting and live execution across crypto, forex, equities, and futures on 27+ venues. The same strategy code runs in nanosecond backtests and in production, giving true research-to-live parity.