Open-source alternatives to Xmtf

Youtu Agent is an open-source framework for building, running, and evaluating autonomous agents powered by large language models. It provides the core infrastructure for creating agents that follow reasoning loops, use toolkits, and coordinate with other agents to solve complex tasks, all managed through YAML-driven configuration files. The framework distinguishes itself through its support for multi-agent orchestration, where a planner agent decomposes tasks and coordinates specialized worker agents, and through its integration with the Model Context Protocol for connecting to external toolk

Dolly is an instruction-tuned large language model designed to follow complex natural language directions. It operates as a causal language model that predicts the next token in a sequence to generate coherent conversational responses and perform tasks such as brainstorming, classification, and question answering. The project focuses on the development of models using open datasets suitable for commercial application. It enables the creation of instruction-following models by utilizing curated collections of human-generated instruction-response pairs. The repository provides capabilities for

DeepSeek-V3 is a large language model that provides comprehensive resources for model utilization, including technical specifications, pre-trained weights, and evaluation benchmarks. The project details the core transformer architecture, including parameter counts and multi-token prediction modules, while supporting native 8-bit floating-point quantization. The repository offers extensive support for local and distributed inference through integration with multiple frameworks and engines. It includes documentation for deploying the model across various hardware configurations, such as GPUs an

ChatRWKV is an open-source frontend and GPU-accelerated inference engine designed for interacting with RWKV recurrent neural network language models. It provides a self-hosted web chat interface and a specialized client for generating human-like text using a linear-complexity architecture. The project utilizes a GPU-accelerated backend that employs custom CUDA kernels and dynamic model format conversion to increase processing speed and reduce memory overhead. It manages conversation history through state-based context management, updating a fixed-size hidden state to maintain a constant memor

Fairseq is a PyTorch toolkit for sequence-to-sequence modeling, specializing in neural machine translation, automatic speech recognition, and large-scale language model training. It provides a framework for processing and aligning diverse data sources, including text, audio, and video, to support tasks such as speech-to-text conversion and multimodal sequence learning. The project is distinguished by its distributed training capabilities, which utilize parameter sharding, mixed-precision training, and CPU offloading to handle models that exceed single-device memory. It also includes specializ

ChatYuan: Large Language Model for Dialogue in Chinese and English

Llama is a large language model runtime and inference engine designed to load and execute autoregressive transformer models. It enables the generation of natural language text completions from prompts using pretrained weights. The system features multi-GPU model parallelism, which distributes model weights and workloads across multiple graphics processors to support larger parameter counts. It also incorporates a content safety filter that uses classifiers to intercept and block unsafe inputs or outputs during the inference process. The project covers broad capabilities in distributed model

The Official Python Client for Lamini's API

Original Flan (2021) | The Flan Collection (2022) | Flan 2021 Citation | License

This repository serves as a comprehensive research platform and toolkit for advancing machine learning, quantum computing, and large-scale scientific data analysis. It provides foundational frameworks for developing complex algorithmic systems, offering the necessary infrastructure for distributed training, computational graph execution, and high-performance model development. The project distinguishes itself by integrating specialized research domains with robust, privacy-preserving methodologies. It supports diverse scientific discovery through tools for quantum simulation, physics-informed

Go to T5X ReadTheDocs Documentation Page.

OpenChat is a framework for the training, fine-tuning, and deployment of large language models optimized for conversational and mathematical reasoning tasks. It provides a comprehensive lifecycle for these models, ranging from training pipelines and deployment stacks to a web-based chat interface. The project focuses on enabling high-performance model execution on consumer-grade hardware without the need for enterprise-grade accelerators. It includes a production-ready inference server that implements the OpenAI chat completion protocol and utilizes dynamic request batching to optimize hardwa

A minimum example of aligning language models with RLHF similar to ChatGPT

🤗 HF Repo 📄 Paper 📚 6K Data 📚 10K Data

🎩 Models | 📚 Dataset | 🚀 Quick Start | 👀 Demo | 📝 Citation | 🙏 Acknowledgements

minGPT is a minimal implementation of the Transformer architecture designed for training and experimenting with language models. It functions as a neural network training framework and a text generation engine, providing the necessary tools to manage data loading, backpropagation, and parameter updates for custom deep learning models. The project is structured as an educational resource for understanding how transformer architectures function by building and training models from scratch. It utilizes a modular block architecture and transformer-based self-attention to process sequences, allowi

KoboldAI-Client is a web-based interface and toolkit for interacting with large language models. It functions as a local AI text generator for storytelling and conversational AI, providing a front end for models hosted either on local hardware or within cloud-provisioned environments. The system includes a persona manager that uses external modules and soft-prompting to guide AI responses toward specific characters and writing styles. It also provides an API wrapper that exposes a standardized, OpenAI-compatible REST API, allowing external applications to communicate with the hosted models.

Wenda is a self-hosted infrastructure and gateway platform for deploying language models within internal networks to ensure data privacy and security. It functions as a centralized hub and API gateway that unifies communication between various offline model runners and online service providers through a single interface. The platform includes a workflow orchestrator that uses custom scripts and API calls to automate complex conversation flows and model settings. It also incorporates a retrieval system that augments model responses with external knowledge retrieved from vector databases and se

Open-Assistant is a conversational assistant and a system for creating large language model training datasets. It utilizes a client-server architecture that separates the conversational user interface from language model processing through an API. The project features a retrieval-augmented generation system that fetches external data from search engines to provide real-time knowledge. It also includes a standardized plugin interface for connecting language models to third-party systems and external software tools. The system provides a pipeline for collecting and labeling human-annotated pro

This is a machine learning framework for treating diverse natural language processing tasks as a unified text-to-text problem. It provides a toolkit for pre-training and fine-tuning large-scale transformer models, utilizing a system where both inputs and outputs are formatted as raw text sequences. The framework is distinguished by its distributed training system, which uses mesh-based strategies to scale model weights and training batches across multiple TPU cores. It supports multi-task learning by combining diverse datasets into a single training stream using configurable mixture rates, al

BELLE is a specialized implementation of Chinese conversational large language models, encompassing a full instruction tuning framework. It provides a pipeline for training, evaluating, and deploying models optimized for natural language understanding and dialogue tasks in the Chinese language. The project is distinguished by its integrated approach to model refinement, combining the curation of multi-million entry instruction datasets with a distributed training pipeline. This pipeline supports both full fine-tuning and low-rank adaptation to optimize conversational performance. The system

Lit-llama is a PyTorch-based implementation framework for the LLaMA language model, providing a system for pre-training, fine-tuning, and high-performance inference. It includes a pre-training pipeline for creating foundational language models from scratch and tools for running pretrained weights to generate natural text and predict sequences. The project provides specialized toolkits for parameter-efficient fine-tuning using low-rank adaptation and lightweight adapters. It also includes a quantization library that reduces model memory footprints through four-bit and eight-bit precision to en

FastChat is a training and serving platform for large language models that provides an integrated toolkit for fine-tuning, hosting, and benchmarking chatbots. It functions as an inference server capable of hosting multiple models and exposing them via a standardized API for chat applications. The platform distinguishes itself through a distributed model controller that manages worker nodes and routes requests across a hardware-agnostic inference layer supporting various accelerators. It includes a dedicated evaluation framework for assessing model quality using automated judges, multi-turn di

This is a PyTorch implementation of reinforcement learning from human feedback designed to align large language models with human values and preferences. It provides a framework for the PaLM architecture and incorporates parameter-efficient fine-tuning to adapt models while minimizing the number of updated weights. The system enables the development of reward models that act as scoring mechanisms built from human preference data. These models evaluate generative outputs to guide the alignment process. The workflow covers policy optimization using a clipped objective, reward modeling based on

Towards Robust Grounded Language Modeling [DEMO](https://huggingface.co/spaces/luohy/SAIL-7B) | [WEB](https://openlsr.org/sail-7b)

Llama 3 is a collection of pretrained, autoregressive transformer-based models designed for natural language generation, reasoning, and complex instruction following. It functions as a generative AI framework that provides the infrastructure for managing model weights, executing neural network inference, and handling computational workloads across diverse knowledge domains. The project distinguishes itself through an integrated AI safety toolkit that employs secondary classification filtering to inspect inputs and outputs, ensuring adherence to usage compliance and safety standards. It suppor

This repository is for EleutherAI's project Pythia which combines interpretability analysis and scaling laws to understand how knowledge develops and evolves during training in autoregressive transformers. For detailed info on the models, their training, and their properties, please see our…