Projects sharing features with CodeGPT

Huatuo-Llama-Med-Chinese is a medical large language model specialized in processing and generating natural language text in Chinese. It is an instruction-tuned system designed to answer professional healthcare questions by leveraging a dedicated medical knowledge base. The model integrates structured medical literature and knowledge graphs to ensure clinical accuracy during response generation. It employs knowledge-graph augmented inference to combine structured entity relationships with neural network outputs. The system is developed through domain-specific weight adaptation, cross-lingual

UltraChat is a collection of large-scale conversational datasets and instruction-tuning data designed for training and evaluating generative AI models. It provides structured JSON data consisting of complex, multi-round dialogue sequences intended to refine the performance of large language models in chat tasks. The project focuses on improving reasoning and response quality through a diverse set of interactions across multiple sectors. These datasets are used for supervised fine-tuning and instruction tuning workflows to improve how models follow complex directions and maintain context acros

Starcoder is a large language model and associated framework designed to generate, complete, and evaluate source code across multiple programming languages. It functions as a source code model that can produce complete function implementations and predict subsequent characters in a line of code based on provided prompts. The project provides a specialized toolkit for adapting base models to specific coding tasks and instruction-following behaviors. This includes a conversational code assistant framework for training models to generate code via natural language chat, as well as a parameter-eff

IF is a text-to-image diffusion system that translates natural language descriptions into visual imagery. The project provides a generative pipeline for creating images, an inpainting tool for modifying specific image sections, and a super-resolution upscaler to increase pixel density and clarity. The system includes a concept fine-tuning framework that allows for the teaching of new visual concepts by updating a small set of parameters. It also supports image style transfer to apply the aesthetic characteristics of a reference image to a new output.

LangChain is an orchestration framework designed for building, managing, and deploying applications powered by large language models. It provides a unified integration layer that normalizes disparate model provider APIs into a consistent set of primitives, enabling developers to build complex, multi-step AI workflows that manage state, memory, and tool execution. The project distinguishes itself through a durable execution runtime that maintains persistent state across long-running processes by checkpointing progress to external storage. It models agent workflows as directed graphs, allowing

GPT4All is a cross-platform runtime environment designed to execute large language models directly on local consumer hardware. By leveraging an optimized C++ inference backend, it enables private, offline AI interactions without requiring an internet connection or external cloud services. The project provides a comprehensive ecosystem for managing the entire model lifecycle, including discovery, downloading, and configuration of local weights. What distinguishes the platform is its integrated retrieval-augmented generation engine, which allows users to index local documents into semantic vect

LaWGPT is an open-source large language model fine-tuned specifically for the Chinese legal domain. It is designed to understand and respond to Chinese legal queries, generate legally accurate text, and assist with judicial exam preparation by answering questions from the Chinese National Judicial Examination. The project distinguishes itself through a comprehensive adaptation pipeline that extends a base Chinese-language LLaMA model with legal-domain vocabulary and fine-tunes it on large-scale Chinese legal corpora and curated question-answer pairs. It employs parameter-efficient tuning tech

whisper-jax is a high-performance implementation of the Whisper automatic speech recognition model rewritten using the JAX framework. It is designed for accelerated inference and uses XLA compilation to optimize model execution on hardware accelerators. The project focuses on TPU optimized transcription to achieve high throughput and speed. It includes a weight translation pipeline that converts pre-trained model parameters from PyTorch into JAX-compatible arrays. The system supports transcribing audio to text, translating speech across multiple languages, and generating audio timestamps. It

🌟 ChatGenTitle：使用百万arXiv论文信息在LLaMA模型上进行微调的论文题目生成模型

transformers结构的中文OFA模型

This project is a quantized fine-tuning framework for large language models. It implements a low-rank adaptation library and a four-bit quantizer to reduce the GPU memory requirements needed to train large models. The framework utilizes four-bit quantization and low-rank adapters to enable model training on consumer-grade hardware. It further reduces the memory footprint through double quantization and a paged optimizer that offloads states to system RAM. The system supports distributed training across multiple GPUs to handle larger parameter scales and includes utilities for custom dataset

LiteLLM is a unified gateway and proxy server designed to centralize access to over one hundred language model providers. It provides a standardized API interface that abstracts vendor-specific schemas, allowing developers to interact with diverse models through a single, consistent format. By acting as a central traffic management layer, it enables organizations to route, secure, and govern model interactions across multiple deployments. The platform distinguishes itself through its policy-driven architecture, which uses configuration-based routing to manage traffic distribution, load balanc

ChatBot Injection and Exploit Examples: A Curated List of Prompt Engineer Commands - ChatGPT

Code and data associated with the AmbiEnt dataset in "We're Afraid Language Models Aren't Modeling Ambiguity" (Liu et al., 2023)

SalesGPT is an AI-powered sales agent platform that autonomously handles customer conversations, schedules meetings, and manages sales pipelines using language models. It is built on the LangChain framework and orchestrates multi-stage sales dialogues across voice, email, and messaging channels, grounding responses in product knowledge to reduce hallucinations and answer inquiries accurately. The agent guides conversations through predefined stages such as Introduction, Qualification, and Close using a state machine that tracks progress, while a reactive tool loop selects and executes externa

Transformers is a comprehensive library for machine learning that provides a unified interface for training, fine-tuning, and deploying transformer-based models. It supports a wide range of tasks, including text classification, language modeling, question answering, and sequence-to-sequence translation, while offering specialized architectures for both text and vision processing. The framework includes tools for managing the entire model lifecycle, from data preprocessing and tokenization to distributed training and inference. The library features extensive support for model optimization and

MultimodalC4 is a multimodal extension of c4 that interleaves millions of images with text.

This project is an AI coding assistant and code translator designed to convert source code from one programming language to another while preserving original logic and functionality. It functions as an intelligent interface for learning programming techniques and implementing code through guided AI support. The system includes a manager for creating and executing local autonomous AI agents to automate technical workflows and study machine learning behaviors. It also features a learning integration server that connects AI assistants with external educational content and technical context. The

AudioGPT is an LLM-driven audio framework and processing suite that uses large language models to orchestrate neural audio pipelines. It functions as a multimodal audio generator and processing system, integrating a collection of pretrained models to handle speech synthesis, sound generation, and audio manipulation. The system is distinguished by its ability to generate audio from diverse inputs, including text and images, and its capacity to produce synchronized talking head videos. It also operates as a neural speech translator, converting spoken language between different tongues while pre

⚡ Everything about ChatGPT

A dataset featuring diverse dialogues between two ChatGPT (gpt-3.5-turbo) instances with system messages written by GPT-4. Covering various contexts and tasks (task-oriented dialogue systems, abstract reasoning, brainstorming).

This repository serves as an educational framework for building large language models from the ground up. It provides a structured curriculum that guides learners through the end-to-end lifecycle of model development, including data processing, architecture design, and optimization. By focusing on low-level implementation, the project enables users to master the fundamental mechanics of artificial intelligence without relying on high-level abstraction frameworks. The project distinguishes itself by constructing neural network components and gradient-based optimization logic from first princip

lecture notes for probabilistic topic models using ipython notebook

Implementation of various topic models

Argilla is a collaborative AI feedback tool and data curation management system. It serves as a human-in-the-loop dataset platform designed to coordinate workforce annotators and domain experts in labeling, rating, and refining data samples for machine learning projects. The platform focuses on large language model dataset curation and reinforcement learning from human feedback workflows. It provides a shared workspace for integrating human expertise into AI development to validate model outputs and correct data errors. The system manages the end-to-end machine learning data pipeline, includ