Tag

LightRAG

A graph-based RAG framework pairing a knowledge graph with vector retrieval and a dual-level (low/high) query mode. New documents merge into the graph via set operations instead of triggering a rebuild, cutting the cost of keeping the index current.

AI Toolkit

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.

IMDB

Provides labeled movie-review data for binary sentiment classification: 25,000 training and 25,000 test examples, plus 50,000 unlabeled reviews for unsupervised or semi-supervised use. Labels reflect strong polarity (positive ≥7, negative ≤4) and the set is a widely used NLP benchmark.

SQuAD

Provides about 100,000 crowd‑written question–answer pairs from Wikipedia where each answer is a text span in the passage, used to train and evaluate extractive question‑answering models. Includes train/validation splits, span offsets, Parquet format, CC BY‑SA 4.0.

Weights & Biases

Tracks every ML run — hyperparameters, metrics, checkpoints, dataset versions — into one dashboard you share as a live report, with Sweeps for tuning and a model registry. Weave extends it to LLM apps: tracing, evals, and production monitoring.

GLUE (General Language Understanding Evaluation benchmark)

A multi-task English NLU benchmark for evaluating models across nine tasks (acceptability, sentiment, paraphrase, similarity, and various NLI setups), with a diagnostic evaluation set and an online leaderboard to compare generalization and transfer learning.

OpenVINO

Converts, quantizes, and runs deep learning models from PyTorch, TensorFlow, ONNX, and PaddlePaddle across Intel CPUs, GPUs, and NPUs without the training framework. Adds a GenAI pipeline for LLMs plus Hugging Face, vLLM, and LangChain integrations.

Transformers

Provides unified model definitions and a single API for pretrained text, vision, audio, and multimodal models for both training and inference. Emphasizes cross-framework compatibility (PyTorch/TF/JAX), pipeline-based inference, and direct access to 1M+ Hub checkpoints.

Hugging Face — Transformers

Turns model definitions into a shared layer across training and inference stacks, covering text, vision, audio, video, and multimodal models. Pipelines, Trainer, and generation APIs make pretrained models usable without locking teams to one framework.

PyTorch Image Models (timm)

Bundles hundreds of pretrained image backbones — ResNet, EfficientNet, ViT, ConvNeXt, Swin and more — behind one consistent API for classification and feature extraction, with training and inference scripts that reproduce published ImageNet results.

NVIDIA/Megatron-LM

Train and experiment with multi-billion to trillion-parameter transformer models on large GPU clusters using GPU-optimized building blocks and reference training scripts; offers advanced parallelism and mixed-precision support for research teams and ML engineers.

CleanRL (Clean Implementation of RL Algorithms)

Implements deep RL algorithms (PPO, DQN, SAC, TD3, DDPG, C51, PPG) as standalone single-file scripts — the PPO Atari variant is ~340 readable lines. Built for research debugging and reproducibility, with W&B and TensorBoard tracking.