Tag

LeRobot

A PyTorch-native, hardware-agnostic stack for robot learning: data collection, training, and deployment across 11+ robots, from SO100 to Unitree G1. Includes imitation, RL, and vision-language-action policies (ACT, Diffusion, Pi0, SmolVLA).

WeClone

Creates personalized digital avatars (AI twins) by fine-tuning LLMs on users' chat history and binding them to chatbots. Provides an end-to-end pipeline — chat export, preprocessing with privacy filters, SFT/LoRA training, and deployment (Telegram/Discord/Slack). Best with larger models and substantial chat data.

Gemma

JAX-native library for loading, sampling, and fine-tuning DeepMind's open-weight Gemma LLM family, with multi-modal sampling and checkpoint utilities — aimed at researchers and engineers running experiments on CPU/GPU/TPU.

Brush

Produces real-time 3D reconstructions from multi-view images using Gaussian splatting, with on-device training and interactive viewing across native desktops, Android, and the browser. Uses WebGPU and the Burn ML framework to ship dependency-free binaries, a CLI, live training visualization, and streaming .ply support.

bigcode/the-stack-v2

Provides a multilingual, deduplicated corpus of public source code in Parquet for large-scale model training and evaluation. Includes license metadata, language splits, and streaming-friendly packaging for use with Hugging Face Datasets — suited to training code-focused foundation models but requires careful license/provenance review.

Open-Sora

Generates short videos from text, images, or videos and ships a full training/inference pipeline with checkpoints and demos. Key features include multi-stage training (VAE / 3D-VAE), rectified-flow training, video compression modules, and support for 2s–16s clips at up to 720p. Best for researchers and engineers who can provide substantial GPU resources.

Marin

End-to-end framework for running and reproducing foundation-model research workflows — from data curation and tokenization to training and evaluation. Emphasizes reproducibility by recording every step (including failed runs) and expressing experiments as dependency-ordered steps.

Dive into LLMs (《动手学大模型》系列编程实践教程)

Hands-on coding tutorial series for large language models with slides and runnable notebooks covering fine-tuning, prompting, RLHF, safety, steganography, watermarking, multimodal models, GUI agents, and deployment. Community-maintained, free course materials for students and researchers.

FineWeb

Provides a cleaned, deduplicated English web corpus optimized for LLM pretraining—over 15T tokens aggregated from CommonCrawl with per-dump snapshots and smaller sampled configs (10B/100B/350B). Includes the datatrove processing pipeline, MinHash deduplication, and an ODC-By v1.0 license; suited for large-scale model training and ablation studies but not specialized for code.

FastVideo

Accelerates video generation with a unified framework for inference, finetuning, LoRA, distillation, sparse attention, and distributed execution for research and demos.

TimesFM

Pretrained foundation model for zero-shot point and quantile forecasting on unseen time series, no per-dataset training. Decoder-only, trained on 100B real-world time-points; v2.5 (200M params) handles up to 16k context and 1k-step horizons.

Oumi

Streamlines the full lifecycle of foundation models — data prep, fine-tuning (SFT/LoRA/QLoRA/GRPO), evaluation, and deployment — with ready-to-run recipes, multi-engine inference support, and cloud/CLI workflows for both laptop experiments and large-scale runs.