DB-GPT-Hub is an experimental project leveraging Large Language Models (LLMs) for Text-to-SQL parsing. It includes stages like data collection, preprocessing, model selection, construction, and fine-tuning of model weights. The project aims to enhance Text-to-SQL capabilities, reduce model training costs, and enable developers to contribute to improving Text-to-SQL accuracy. The ultimate goal is to achieve automated question-answering based on databases, allowing users to execute complex database queries using natural language descriptions. The project has successfully integrated multiple large models and established a comprehensive workflow for data processing, SFT model training, prediction output, and evaluation.

AgentGym is a framework designed to help the AI community evaluate and develop generally-capable Large Language Model-based agents. It features diverse interactive environments and tasks with real-time feedback and concurrency. The platform supports 14 environments across various domains like web navigating, text games, house-holding tasks, digital games, and more. AgentGym includes a trajectory set (AgentTraj) and a benchmark suite (AgentEval) to facilitate agent exploration and evaluation. The framework allows for agent self-evolution beyond existing data, showcasing comparable results to state-of-the-art models.

CodeGeeX4-ALL-9B is an open-source multilingual code generation model based on GLM-4-9B, offering enhanced code generation capabilities. It supports functions like code completion, code interpreter, web search, function call, and repository-level code Q&A. The model has competitive performance on benchmarks like BigCodeBench and NaturalCodeBench, outperforming larger models in terms of speed and performance.

GPTQModel is an easy-to-use LLM quantization and inference toolkit based on the GPTQ algorithm. It provides support for weight-only quantization and offers features such as dynamic per layer/module flexible quantization, sharding support, and auto-heal quantization errors. The toolkit aims to ensure inference compatibility with HF Transformers, vLLM, and SGLang. It offers various model supports, faster quant inference, better quality quants, and security features like hash check of model weights. GPTQModel also focuses on faster quantization, improved quant quality as measured by PPL, and backports bug fixes from AutoGPTQ.

Consistency Large Language Models (CLLMs) is a family of efficient parallel decoders that reduce inference latency by efficiently decoding multiple tokens in parallel. The models are trained to perform efficient Jacobi decoding, mapping any randomly initialized token sequence to the same result as auto-regressive decoding in as few steps as possible. CLLMs have shown significant improvements in generation speed on various tasks, achieving up to 3.4 times faster generation. The tool provides a seamless integration with other techniques for efficient Large Language Model (LLM) inference, without the need for draft models or architectural modifications.

Langfun is a Python library that aims to make language models (LM) fun to work with. It enables a programming model that flows naturally, resembling the human thought process. Langfun emphasizes the reuse and combination of language pieces to form prompts, thereby accelerating innovation. Unlike other LM frameworks, which feed program-generated data into the LM, langfun takes a distinct approach: It starts with natural language, allowing for seamless interactions between language and program logic, and concludes with natural language and optional structured output. Consequently, langfun can aptly be described as Language as functions, capturing the core of its methodology.

Evalverse is an open-source project designed to support Large Language Model (LLM) evaluation needs. It provides a standardized and user-friendly solution for processing and managing LLM evaluations, catering to AI research engineers and scientists. Evalverse supports various evaluation methods, insightful reports, and no-code evaluation processes. Users can access unified evaluation with submodules, request evaluations without code via Slack bot, and obtain comprehensive reports with scores, rankings, and visuals. The tool allows for easy comparison of scores across different models and swift addition of new evaluation tools.

Ragas is a framework that helps you evaluate your Retrieval Augmented Generation (RAG) pipelines. RAG denotes a class of LLM applications that use external data to augment the LLM’s context. There are existing tools and frameworks that help you build these pipelines but evaluating it and quantifying your pipeline performance can be hard. This is where Ragas (RAG Assessment) comes in. Ragas provides you with the tools based on the latest research for evaluating LLM-generated text to give you insights about your RAG pipeline. Ragas can be integrated with your CI/CD to provide continuous checks to ensure performance.

Qwen is a series of large language models developed by Alibaba DAMO Academy. It outperforms the baseline models of similar model sizes on a series of benchmark datasets, e.g., MMLU, C-Eval, GSM8K, MATH, HumanEval, MBPP, BBH, etc., which evaluate the models’ capabilities on natural language understanding, mathematic problem solving, coding, etc. Qwen models outperform the baseline models of similar model sizes on a series of benchmark datasets, e.g., MMLU, C-Eval, GSM8K, MATH, HumanEval, MBPP, BBH, etc., which evaluate the models’ capabilities on natural language understanding, mathematic problem solving, coding, etc. Qwen-72B achieves better performance than LLaMA2-70B on all tasks and outperforms GPT-3.5 on 7 out of 10 tasks.

Codefuse-ModelCache is a semantic cache for large language models (LLMs) that aims to optimize services by introducing a caching mechanism. It helps reduce the cost of inference deployment, improve model performance and efficiency, and provide scalable services for large models. The project facilitates sharing and exchanging technologies related to large model semantic cache through open-source collaboration.

NSMusicS is a local music software that is expected to support multiple platforms with AI capabilities and multimodal features. The goal of NSMusicS is to integrate various functions (such as artificial intelligence, streaming, music library management, cross platform, etc.), which can be understood as similar to Navidrome but with more features than Navidrome. It wants to become a plugin integrated application that can almost have all music functions.

Lance is a modern columnar data format optimized for ML workflows and datasets. It offers high-performance random access, vector search, zero-copy automatic versioning, and ecosystem integrations with Apache Arrow, Pandas, Polars, and DuckDB. Lance is designed to address the challenges of the ML development cycle, providing a unified data format for collection, exploration, analytics, feature engineering, training, evaluation, deployment, and monitoring. It aims to reduce data silos and streamline the ML development process.

Kernel Memory (KM) is a multi-modal AI Service specialized in the efficient indexing of datasets through custom continuous data hybrid pipelines, with support for Retrieval Augmented Generation (RAG), synthetic memory, prompt engineering, and custom semantic memory processing. KM is available as a Web Service, as a Docker container, a Plugin for ChatGPT/Copilot/Semantic Kernel, and as a .NET library for embedded applications. Utilizing advanced embeddings and LLMs, the system enables Natural Language querying for obtaining answers from the indexed data, complete with citations and links to the original sources. Designed for seamless integration as a Plugin with Semantic Kernel, Microsoft Copilot and ChatGPT, Kernel Memory enhances data-driven features in applications built for most popular AI platforms.

CAMEL is an open-source library designed for the study of autonomous and communicative agents. We believe that studying these agents on a large scale offers valuable insights into their behaviors, capabilities, and potential risks. To facilitate research in this field, we implement and support various types of agents, tasks, prompts, models, and simulated environments.

ScrapeGraphAI is a web scraping Python library that utilizes LLM and direct graph logic to create scraping pipelines for websites and local documents. It offers various standard scraping pipelines like SmartScraperGraph, SearchGraph, SpeechGraph, and ScriptCreatorGraph. Users can extract information by specifying prompts and input sources. The library supports different LLM APIs such as OpenAI, Groq, Azure, and Gemini, as well as local models using Ollama. ScrapeGraphAI is designed for data exploration and research purposes, providing a versatile tool for extracting information from web pages and generating outputs like Python scripts, audio summaries, and search results.

ABQ-LLM is a novel arbitrary bit quantization scheme that achieves excellent performance under various quantization settings while enabling efficient arbitrary bit computation at the inference level. The algorithm supports precise weight-only quantization and weight-activation quantization. It provides pre-trained model weights and a set of out-of-the-box quantization operators for arbitrary bit model inference in modern architectures.

LightEval is a lightweight LLM evaluation suite that Hugging Face has been using internally with the recently released LLM data processing library datatrove and LLM training library nanotron. We're releasing it with the community in the spirit of building in the open. Note that it is still very much early so don't expect 100% stability ^^' In case of problems or question, feel free to open an issue!

Firefly is an open-source large model training project that supports pre-training, fine-tuning, and DPO of mainstream large models. It includes models like Llama3, Gemma, Qwen1.5, MiniCPM, Llama, InternLM, Baichuan, ChatGLM, Yi, Deepseek, Qwen, Orion, Ziya, Xverse, Mistral, Mixtral-8x7B, Zephyr, Vicuna, Bloom, etc. The project supports full-parameter training, LoRA, QLoRA efficient training, and various tasks such as pre-training, SFT, and DPO. Suitable for users with limited training resources, QLoRA is recommended for fine-tuning instructions. The project has achieved good results on the Open LLM Leaderboard with QLoRA training process validation. The latest version has significant updates and adaptations for different chat model templates.

Awesome Text2SQL is a curated repository containing tutorials and resources for Large Language Models, Text2SQL, Text2DSL, Text2API, Text2Vis, and more. It provides guidelines on converting natural language questions into structured SQL queries, with a focus on NL2SQL. The repository includes information on various models, datasets, evaluation metrics, fine-tuning methods, libraries, and practice projects related to Text2SQL. It serves as a comprehensive resource for individuals interested in working with Text2SQL and related technologies.

The 'create-million-parameter-llm-from-scratch' repository provides a detailed guide on creating a Large Language Model (LLM) with 2.3 million parameters from scratch. The blog replicates the LLaMA approach, incorporating concepts like RMSNorm for pre-normalization, SwiGLU activation function, and Rotary Embeddings. The model is trained on a basic dataset to demonstrate the ease of creating a million-parameter LLM without the need for a high-end GPU.

StableToolBench is a new benchmark developed to address the instability of Tool Learning benchmarks. It aims to balance stability and reality by introducing features such as a Virtual API System with caching and API simulators, a new set of solvable queries determined by LLMs, and a Stable Evaluation System using GPT-4. The Virtual API Server can be set up either by building from source or using a prebuilt Docker image. Users can test the server using provided scripts and evaluate models with Solvable Pass Rate and Solvable Win Rate metrics. The tool also includes model experiments results comparing different models' performance.

The Beta Machine Learning Toolkit is a package containing various algorithms and utilities for implementing machine learning workflows in multiple languages, including Julia, Python, and R. It offers a range of supervised and unsupervised models, data transformers, and assessment tools. The models are implemented entirely in Julia and are not wrappers for third-party models. Users can easily contribute new models or request implementations. The focus is on user-friendliness rather than computational efficiency, making it suitable for educational and research purposes.

AI-TOD is a dataset for tiny object detection in aerial images, containing 700,621 object instances across 28,036 images. Objects in AI-TOD are smaller with a mean size of 12.8 pixels compared to other aerial image datasets. To use AI-TOD, download xView training set and AI-TOD_wo_xview, then generate the complete dataset using the provided synthesis tool. The dataset is publicly available for academic and research purposes under CC BY-NC-SA 4.0 license.

Uni-MoE is a MoE-based unified multimodal model that can handle diverse modalities including audio, speech, image, text, and video. The project focuses on scaling Unified Multimodal LLMs with a Mixture of Experts framework. It offers enhanced functionality for training across multiple nodes and GPUs, as well as parallel processing at both the expert and modality levels. The model architecture involves three training stages: building connectors for multimodal understanding, developing modality-specific experts, and incorporating multiple trained experts into LLMs using the LoRA technique on mixed multimodal data. The tool provides instructions for installation, weights organization, inference, training, and evaluation on various datasets.

Weave is a toolkit for developing Generative AI applications, built by Weights & Biases. With Weave, you can log and debug language model inputs, outputs, and traces; build rigorous, apples-to-apples evaluations for language model use cases; and organize all the information generated across the LLM workflow, from experimentation to evaluations to production. Weave aims to bring rigor, best-practices, and composability to the inherently experimental process of developing Generative AI software, without introducing cognitive overhead.

LLMStack is a no-code platform for building generative AI agents, workflows, and chatbots. It allows users to connect their own data, internal tools, and GPT-powered models without any coding experience. LLMStack can be deployed to the cloud or on-premise and can be accessed via HTTP API or triggered from Slack or Discord.

The VisionCraft API is a free API for using over 100 different AI models. From images to sound.

Kaito is an operator that automates the AI/ML inference model deployment in a Kubernetes cluster. It manages large model files using container images, avoids tuning deployment parameters to fit GPU hardware by providing preset configurations, auto-provisions GPU nodes based on model requirements, and hosts large model images in the public Microsoft Container Registry (MCR) if the license allows. Using Kaito, the workflow of onboarding large AI inference models in Kubernetes is largely simplified.

PyRIT is an open access automation framework designed to empower security professionals and ML engineers to red team foundation models and their applications. It automates AI Red Teaming tasks to allow operators to focus on more complicated and time-consuming tasks and can also identify security harms such as misuse (e.g., malware generation, jailbreaking), and privacy harms (e.g., identity theft). The goal is to allow researchers to have a baseline of how well their model and entire inference pipeline is doing against different harm categories and to be able to compare that baseline to future iterations of their model. This allows them to have empirical data on how well their model is doing today, and detect any degradation of performance based on future improvements.

Tabby is a self-hosted AI coding assistant, offering an open-source and on-premises alternative to GitHub Copilot. It boasts several key features: * Self-contained, with no need for a DBMS or cloud service. * OpenAPI interface, easy to integrate with existing infrastructure (e.g Cloud IDE). * Supports consumer-grade GPUs.

SPEAR (Simulator for Photorealistic Embodied AI Research) is a powerful tool for training embodied agents. It features 300 unique virtual indoor environments with 2,566 unique rooms and 17,234 unique objects that can be manipulated individually. Each environment is designed by a professional artist and features detailed geometry, photorealistic materials, and a unique floor plan and object layout. SPEAR is implemented as Unreal Engine assets and provides an OpenAI Gym interface for interacting with the environments via Python.

Magick is a groundbreaking visual AIDE (Artificial Intelligence Development Environment) for no-code data pipelines and multimodal agents. Magick can connect to other services and comes with nodes and templates well-suited for intelligent agents, chatbots, complex reasoning systems and realistic characters.