RAGEN is the first open-source reproduction of DeepSeek-R1 on AGENT training.

[NeurIPS 2024] Direct3D: Scalable Image-to-3D Generation via 3D Latent Diffusion Transformer

An Easy-to-use, Scalable and High-performance RLHF Framework (70B+ PPO Full Tuning & Iterative DPO & LoRA & RingAttention & RFT)

This is a replicate of DeepSeek-R1-Zero and DeepSeek-R1 training on small models with limited data

Fully open reproduction of DeepSeek-R1

A high-throughput and memory-efficient inference and serving engine for LLMs

SGLang is a fast serving framework for large language models and vision language models.

User-friendly AI Interface (Supports Ollama, OpenAI API, ...)

Manual de utilização do Supercomputador Santos Dumont

Get up and running with Llama 3.3, DeepSeek-R1, Phi-4, Gemma 2, and other large language models.

Inference-Time Alignment in Protein Diffusion Models

Large Concept Models: Language modeling in a sentence representation space

SONAR, a new multilingual and multimodal fixed-size sentence embedding space, with a full suite of speech and text encoders and decoders.

This repository contains the code, data, and models of the paper titled "XL-Sum: Large-Scale Multilingual Abstractive Summarization for 44 Languages" published in Findings of the Association for Co…

Training Large Language Model to Reason in a Continuous Latent Space

Train transformer language models with reinforcement learning.

A curated list of Cheminformatics libraries and software.

ProtTrans is providing state of the art pretrained language models for proteins. ProtTrans was trained on thousands of GPUs from Summit and hundreds of Google TPUs using Transformers Models.

A easy, reliable, fluid template for python packages complete with docs, testing suites, readme's, github workflows, linting and much much more

A compilation of the best multi-agent papers

A curated list of resources for machine learning for small-molecule drug discovery

Plugin for folding sequences directly in PyMOL

Language models for drug discovery using torchrl

"Deep Generative Modeling": Introductory Examples

train and use graph-based ML models of potential energy surfaces

Joint embedding of protein sequence and structure with discrete and continuous compressions of protein folding model latent spaces. http://bit.ly/cheap-proteins