Open-Weight Models

π₀.₅ (Pi05) is a Vision-Language-Action model from Physical Intelligence designed for open-world generalization: it evolves π₀ to generalize to entirely new environments and situations that were never seen during training. The LeRobot implementation is adapted from their open-source OpenPI repository. This policy has been trained and pushed to the Hub using LeRobot. Learn how to train and run it in the LeRobot pi05 guide, or browse the full documentation. The policy consumes these observation features and produces these action features. Inputs Outputs New to LeRobot? These guides cover the full workflow: - Install LeRobot — set up the lerobot package. - Hardware setup — assemble, wire, and…

π₀.₅ (Pi05) is a Vision-Language-Action model from Physical Intelligence designed for open-world generalization: it evolves π₀ to generalize to entirely new environments and situations that were never seen during training. The LeRobot implementation is adapted from their open-source OpenPI repository. This policy has been trained and pushed to the Hub using LeRobot. Learn how to train and run it in the LeRobot pi05 guide, or browse the full documentation. The policy consumes these observation features and produces these action features. Inputs Outputs New to LeRobot? These guides cover the full workflow: - Install LeRobot — set up the lerobot package. - Hardware setup — assemble, wire, and…

π₀.₅ (Pi05) is a Vision-Language-Action model from Physical Intelligence designed for open-world generalization: it evolves π₀ to generalize to entirely new environments and situations that were never seen during training. The LeRobot implementation is adapted from their open-source OpenPI repository. This policy has been trained and pushed to the Hub using LeRobot. Learn how to train and run it in the LeRobot pi05 guide, or browse the full documentation. The policy consumes these observation features and produces these action features. Inputs Outputs New to LeRobot? These guides cover the full workflow: - Install LeRobot — set up the lerobot package. - Hardware setup — assemble, wire, and…

π₀ (Pi0) is a general-purpose robot foundation model from Physical Intelligence: a generalist Vision-Language-Action policy that understands visual inputs, interprets natural language instructions, and controls a variety of different robots across diverse tasks. The LeRobot implementation is adapted from their open-source OpenPI repository. This policy has been trained and pushed to the Hub using LeRobot. Learn how to train and run it in the LeRobot pi0 guide, or browse the full documentation. The policy consumes these observation features and produces these action features. Inputs Outputs New to LeRobot? These guides cover the full workflow: - Install LeRobot — set up the lerobot package.…

Model

train_chatterbox

Bibek

A modular, highly efficient infrastructure for fine-tuning both Chatterbox TTS (Standart) and Chatterbox Turbo models with your own dataset and generating high-quality speech synthesis. This kit is specially designed to support new languages and voices by intelligently extending the model's vocabulary. With the newly added LoRA (Low-Rank Adaptation) support, you can now train high-quality voices faster and with significantly less VRAM. This repository allows you to choose your training strategy using the islora flag in src/config.py. What is it? LoRA (Low-Rank Adaptation) freezes the massive base model and only trains tiny adapter layers alongside the new language embeddings. What is it?…

Model

train_progress

SupraLabs

This repository is meant to be containing checkpoints and training progress of a new 13M parameter model, Supra2.5-Pro. It is part of our new Supra3 research program and will be share publicly soon. Please do not request access to this model before it's being released - we will not approve any requests. October 10-12 th, 2026

My trained LoRa repository. + Trigger words are found in the.md file Disclaimer: The LoRAs are provided for entertainment purposes only. You are solely responsible for ensuring that any images you generate and distribute comply with all applicable laws, including copyright, trademark, and personality rights. Do not use these LoRAs to create illegal, harmful, or non-consensual content.

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. Use the code below to get started with the model. Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - PEFT 0.18.1

Versioned, data-only runtime packages for local speech recognition in Trans. These are conversions of the credited upstream models, not models trained by AladdinChen. The original authors retain their rights. No endorsement is implied. Each package is downloaded separately. Recognition runs on the user's device; this repository contains no hosted inference service, recordings, training data, app credentials, or application code. See LICENSES.md for source authors, licenses and modification notices. Full license texts and preserved upstream notices are in licenses/ and provenance/. Redistributors must retain the notices and meet the applicable per-model license conditions. This is not a…

Gradient-boosted classifier. You compute the 15 features yourself and pass them in; the model returns P(fraud) and a risk level 0-3. Trained on a synthetic dataset - validate on real data. predict accepts one dict, a list of dicts, or a pandas DataFrame with these columns (extra columns are ignored, order doesn't matter, missing ones raise an error). "History" = the sender's transfers strictly before the one being scored, within the last 180 days. "rocauctest": 0.9924, "prauctest": 0.8382, "fraudratetest": 0.0169, "alertprecisionlevel2plus": 0.64, "alertrecalllevel2plus": 0.8287, "recalllevel1plus": 0.9655

基于标准 Transformer（Post-LN + 固定 sin-cos 位置编码）的英译中模型， 由本工程用 HuggingFace Seq2SeqTrainer 训练得到（checkpoint：trained with Seq2SeqTrainer (standard HF)）。 本仓库是标准 HF 仓库：包含 config.json + automap，代码随仓库分发， 因此任何人在 HF 上 trustremotecode=True 即可直接加载使用，无需本工程的其它文件。 逐步自回归推理，未实现 KV Cache（usecache=false），长句偏慢。 生成质量以 beam search（numbeams=3）优于贪心。 请按自身场景补充 LICENSE。