Best AI tools for< F1 Historian >

PIXIU is a project designed to support the development, fine-tuning, and evaluation of Large Language Models (LLMs) in the financial domain. It includes components like FinBen, a Financial Language Understanding and Prediction Evaluation Benchmark, FIT, a Financial Instruction Dataset, and FinMA, a Financial Large Language Model. The project provides open resources, multi-task and multi-modal financial data, and diverse financial tasks for training and evaluation. It aims to encourage open research and transparency in the financial NLP field.

SuperPrompt is an open-source project designed to help users understand AI agents. The project includes a prompt with theoretical, mathematical, and binary instructions for users to follow. It aims to serve as a universal catalyst for infinite conceptual evolution, focusing on metamorphic abstract reasoning and self-transcending objectives. The prompt encourages users to explore fundamental truths, create order from cognitive chaos, and prepare for paradigm shifts in understanding. It provides guidelines for analyzing multidimensional states, synthesizing emergent patterns, and integrating new paradigms.

AIDE is a machine learning code generation agent that can generate solutions for machine learning tasks from natural language descriptions. It has the following features: 1. **Instruct with Natural Language**: Describe your problem or additional requirements and expert insights, all in natural language. 2. **Deliver Solution in Source Code**: AIDE will generate Python scripts for the **tested** machine learning pipeline. Enjoy full transparency, reproducibility, and the freedom to further improve the source code! 3. **Iterative Optimization**: AIDE iteratively runs, debugs, evaluates, and improves the ML code, all by itself. 4. **Visualization**: We also provide tools to visualize the solution tree produced by AIDE for a better understanding of its experimentation process. This gives you insights not only about what works but also what doesn't. AIDE has been benchmarked on over 60 Kaggle data science competitions and has demonstrated impressive performance, surpassing 50% of Kaggle participants on average. It is particularly well-suited for tasks that require complex data preprocessing, feature engineering, and model selection.

Sailor is a suite of open language models tailored for South-East Asia (SEA), focusing on languages such as Indonesian, Thai, Vietnamese, Malay, and Lao. Developed with careful data curation, Sailor models are designed to understand and generate text across diverse linguistic landscapes of the SEA region. Built from Qwen 1.5, Sailor encompasses models of varying sizes, spanning from 0.5B to 7B versions for different requirements. Benchmarking results demonstrate Sailor's proficiency in tasks such as question answering, commonsense reasoning, reading comprehension, and more in SEA languages.

HuixiangDou is a **group chat** assistant based on LLM (Large Language Model). Advantages: 1. Design a two-stage pipeline of rejection and response to cope with group chat scenario, answer user questions without message flooding, see arxiv2401.08772 2. Low cost, requiring only 1.5GB memory and no need for training 3. Offers a complete suite of Web, Android, and pipeline source code, which is industrial-grade and commercially viable Check out the scenes in which HuixiangDou are running and join WeChat Group to try AI assistant inside. If this helps you, please give it a star ⭐

This repository provides a curated list of papers, datasets, and resources related to the detection and mitigation of hallucinations in large language models (LLMs). Hallucinations refer to the generation of factually incorrect or nonsensical text by LLMs, which can be a significant challenge for their use in real-world applications. The resources in this repository aim to help researchers and practitioners better understand and address this issue.

Rageval is an evaluation tool for Retrieval-augmented Generation (RAG) methods. It helps evaluate RAG systems by performing tasks such as query rewriting, document ranking, information compression, evidence verification, answer generation, and result validation. The tool provides metrics for answer correctness and answer groundedness, along with benchmark results for ASQA and ALCE datasets. Users can install and use Rageval to assess the performance of RAG models in question-answering tasks.

Benchmark various LLM Structured Output frameworks like Instructor, Mirascope, Langchain, LlamaIndex, Fructose, Marvin, Outlines, LMFormatEnforcer, etc on tasks like multi-label classification, named entity recognition, synthetic data generation. The tool provides benchmark results, methodology, instructions to run the benchmark, add new data, and add a new framework. It also includes a roadmap for framework-related tasks, contribution guidelines, citation information, and feedback request.

RAG Foundry is a library designed to enhance Large Language Models (LLMs) by fine-tuning models on RAG-augmented datasets. It helps create training data, train models using parameter-efficient finetuning (PEFT), and measure performance using RAG-specific metrics. The library is modular, customizable using configuration files, and facilitates prototyping with various RAG settings and configurations for tasks like data processing, retrieval, training, inference, and evaluation.

Streamline Analyst is a cutting-edge, open-source application powered by Large Language Models (LLMs) designed to revolutionize data analysis. This Data Analysis Agent effortlessly automates tasks such as data cleaning, preprocessing, and complex operations like identifying target objects, partitioning test sets, and selecting the best-fit models based on your data. With Streamline Analyst, results visualization and evaluation become seamless. It aims to expedite the data analysis process, making it accessible to all, regardless of their expertise in data analysis. The tool is built to empower users to process data and achieve high-quality visualizations with unparalleled efficiency, and to execute high-performance modeling with the best strategies. Future enhancements include Natural Language Processing (NLP), neural networks, and object detection utilizing YOLO, broadening its capabilities to meet diverse data analysis needs.

RAG-FiT is a library designed to improve Language Models' ability to use external information by fine-tuning models on specially created RAG-augmented datasets. The library assists in creating training data, training models using parameter-efficient finetuning (PEFT), and evaluating performance using RAG-specific metrics. It is modular, customizable via configuration files, and facilitates fast prototyping and experimentation with various RAG settings and configurations.

LLM training in simple, pure C/CUDA. There is no need for 245MB of PyTorch or 107MB of cPython. For example, training GPT-2 (CPU, fp32) is ~1,000 lines of clean code in a single file. It compiles and runs instantly, and exactly matches the PyTorch reference implementation. I chose GPT-2 as the first working example because it is the grand-daddy of LLMs, the first time the modern stack was put together.

LLMClient is a JavaScript/TypeScript library that simplifies working with large language models (LLMs) by providing an easy-to-use interface for building and composing efficient prompts using prompt signatures. These signatures enable the automatic generation of typed prompts, allowing developers to leverage advanced capabilities like reasoning, function calling, RAG, ReAcT, and Chain of Thought. The library supports various LLMs and vector databases, making it a versatile tool for a wide range of applications.

AGiXT is a dynamic Artificial Intelligence Automation Platform engineered to orchestrate efficient AI instruction management and task execution across a multitude of providers. Our solution infuses adaptive memory handling with a broad spectrum of commands to enhance AI's understanding and responsiveness, leading to improved task completion. The platform's smart features, like Smart Instruct and Smart Chat, seamlessly integrate web search, planning strategies, and conversation continuity, transforming the interaction between users and AI. By leveraging a powerful plugin system that includes web browsing and command execution, AGiXT stands as a versatile bridge between AI models and users. With an expanding roster of AI providers, code evaluation capabilities, comprehensive chain management, and platform interoperability, AGiXT is consistently evolving to drive a multitude of applications, affirming its place at the forefront of AI technology.

TrustLLM is a comprehensive study of trustworthiness in LLMs, including principles for different dimensions of trustworthiness, established benchmark, evaluation, and analysis of trustworthiness for mainstream LLMs, and discussion of open challenges and future directions. Specifically, we first propose a set of principles for trustworthy LLMs that span eight different dimensions. Based on these principles, we further establish a benchmark across six dimensions including truthfulness, safety, fairness, robustness, privacy, and machine ethics. We then present a study evaluating 16 mainstream LLMs in TrustLLM, consisting of over 30 datasets. The document explains how to use the trustllm python package to help you assess the performance of your LLM in trustworthiness more quickly. For more details about TrustLLM, please refer to project website.

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Creates F1 posters of real and fictional Grand Prix races.

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Creating text-free, visually striking F1 car designs.