Evalchemy is a unified and easy-to-use toolkit for evaluating language models, focusing on post-trained models. It integrates multiple existing benchmarks such as RepoBench, AlpacaEval, and ZeroEval. Key features include unified installation, parallel evaluation, simplified usage, and results management. Users can run various benchmarks with a consistent command-line interface and track results locally or integrate with a database for systematic tracking and leaderboard submission.

The Prompt Fuzzer is an open-source tool that helps you assess the security of your GenAI application's system prompt against various dynamic LLM-based attacks. It provides a security evaluation based on the outcome of these attack simulations, enabling you to strengthen your system prompt as needed. The Prompt Fuzzer dynamically tailors its tests to your application's unique configuration and domain. The Fuzzer also includes a Playground chat interface, giving you the chance to iteratively improve your system prompt, hardening it against a wide spectrum of generative AI attacks.

ClearML Fractional GPU is a tool designed to optimize GPU resource utilization by allowing multiple containers to run on the same GPU with driver-level memory limitation and compute time-slicing. It supports CUDA 11.x & CUDA 12.x, preventing greedy processes from grabbing the entire GPU memory. The tool offers options like Dynamic GPU Slicing, Container-based Memory Limits, and Kubernetes-based Static MIG Slicing to enhance hardware utilization and workload performance for AI development.

Mistral.rs is a fast LLM inference platform written in Rust. We support inference on a variety of devices, quantization, and easy-to-use application with an Open-AI API compatible HTTP server and Python bindings.

LLMLight is a novel framework that employs Large Language Models (LLMs) as decision-making agents for Traffic Signal Control (TSC). The framework leverages the advanced generalization capabilities of LLMs to engage in a reasoning and decision-making process akin to human intuition for effective traffic control. LLMLight has been demonstrated to be remarkably effective, generalizable, and interpretable against various transportation-based and RL-based baselines on nine real-world and synthetic datasets.

AgentScope is a multi-agent platform designed to empower developers to build multi-agent applications with large-scale models. It features three high-level capabilities: Easy-to-Use, High Robustness, and Actor-Based Distribution. AgentScope provides a list of `ModelWrapper` to support both local model services and third-party model APIs, including OpenAI API, DashScope API, Gemini API, and ollama. It also enables developers to rapidly deploy local model services using libraries such as ollama (CPU inference), Flask + Transformers, Flask + ModelScope, FastChat, and vllm. AgentScope supports various services, including Web Search, Data Query, Retrieval, Code Execution, File Operation, and Text Processing. Example applications include Conversation, Game, and Distribution. AgentScope is released under Apache License 2.0 and welcomes contributions.

paperless-gpt is a tool designed to generate accurate and meaningful document titles and tags for paperless-ngx using Large Language Models (LLMs). It supports multiple LLM providers, including OpenAI and Ollama. With paperless-gpt, you can streamline your document management by automatically suggesting appropriate titles and tags based on the content of your scanned documents. The tool offers features like multiple LLM support, customizable prompts, easy integration with paperless-ngx, user-friendly interface for reviewing and applying suggestions, dockerized deployment, automatic document processing, and an experimental OCR feature.

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.

HuatuoGPT-o1 is a medical language model designed for advanced medical reasoning. It can identify mistakes, explore alternative strategies, and refine answers. The model leverages verifiable medical problems and a specialized medical verifier to guide complex reasoning trajectories and enhance reasoning through reinforcement learning. The repository provides access to models, data, and code for HuatuoGPT-o1, allowing users to deploy the model for medical reasoning tasks.

Vision Parse is a tool that leverages Vision Language Models to parse PDF documents into beautifully formatted markdown content. It offers smart content extraction, content formatting, multi-LLM support, PDF document support, and local model hosting using Ollama. Users can easily convert PDFs to markdown with high precision and preserve document hierarchy and styling. The tool supports multiple Vision LLM providers like OpenAI, LLama, and Gemini for accuracy and speed, making document processing efficient and effortless.

StableToolBench is a new benchmark developed to address the instability of Tool Learning benchmarks. It aims to balance stability and reality by introducing features like Virtual API System, Solvable Queries, and Stable Evaluation System. The benchmark ensures consistency through a caching system and API simulators, filters queries based on solvability using LLMs, and evaluates model performance using GPT-4 with metrics like Solvable Pass Rate and Solvable Win Rate.

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.

TPI-LLM (Tensor Parallelism Inference for Large Language Models) is a system designed to bring LLM functions to low-resource edge devices, addressing privacy concerns by enabling LLM inference on edge devices with limited resources. It leverages multiple edge devices for inference through tensor parallelism and a sliding window memory scheduler to minimize memory usage. TPI-LLM demonstrates significant improvements in TTFT and token latency compared to other models, and plans to support infinitely large models with low token latency in the future.

Gollama is a delightful tool that brings Ollama, your offline conversational AI companion, directly into your terminal. It provides a fun and interactive way to generate responses from various models without needing internet connectivity. Whether you're brainstorming ideas, exploring creative writing, or just looking for inspiration, Gollama is here to assist you. The tool offers an interactive interface, customizable prompts, multiple models selection, and visual feedback to enhance user experience. It can be installed via different methods like downloading the latest release, using Go, running with Docker, or building from source. Users can interact with Gollama through various options like specifying a custom base URL, prompt, model, and enabling raw output mode. The tool supports different modes like interactive, piped, CLI with image, and TUI with image. Gollama relies on third-party packages like bubbletea, glamour, huh, and lipgloss. The roadmap includes implementing piped mode, support for extracting codeblocks, copying responses/codeblocks to clipboard, GitHub Actions for automated releases, and downloading models directly from Ollama using the rest API. Contributions are welcome, and the project is licensed under the MIT License.

PR Pilot is an AI-powered tool designed to assist users in their daily workflow by delegating routine work to AI with confidence and predictability. It integrates seamlessly with popular development tools and allows users to interact with it through a Command-Line Interface, Python SDK, REST API, and Smart Workflows. Users can automate tasks such as generating PR titles and descriptions, summarizing and posting issues, and formatting README files. The tool aims to save time and enhance productivity by providing AI-powered solutions for common development tasks.

1filellm is a command-line data aggregation tool designed for LLM ingestion. It aggregates and preprocesses data from various sources into a single text file, facilitating the creation of information-dense prompts for large language models. The tool supports automatic source type detection, handling of multiple file formats, web crawling functionality, integration with Sci-Hub for research paper downloads, text preprocessing, and token count reporting. Users can input local files, directories, GitHub repositories, pull requests, issues, ArXiv papers, YouTube transcripts, web pages, Sci-Hub papers via DOI or PMID. The tool provides uncompressed and compressed text outputs, with the uncompressed text automatically copied to the clipboard for easy pasting into LLMs.

Evalchemy is a unified and easy-to-use toolkit for evaluating language models, focusing on post-trained models. It integrates multiple existing benchmarks such as RepoBench, AlpacaEval, and ZeroEval. Key features include unified installation, parallel evaluation, simplified usage, and results management. Users can run various benchmarks with a consistent command-line interface and track results locally or integrate with a database for systematic tracking and leaderboard submission.

This leaderboard evaluates the hallucination rate of various Large Language Models (LLMs) when summarizing documents. It uses a model trained by Vectara to detect hallucinations in LLM outputs. The leaderboard includes models from OpenAI, Anthropic, Google, Microsoft, Amazon, and others. The evaluation is based on 831 documents that were summarized by all the models. The leaderboard shows the hallucination rate, factual consistency rate, answer rate, and average summary length for each model.

H2O LLM Studio is a framework and no-code GUI designed for fine-tuning state-of-the-art large language models (LLMs). With H2O LLM Studio, you can easily and effectively fine-tune LLMs without the need for any coding experience. The GUI is specially designed for large language models, and you can finetune any LLM using a large variety of hyperparameters. You can also use recent finetuning techniques such as Low-Rank Adaptation (LoRA) and 8-bit model training with a low memory footprint. Additionally, you can use Reinforcement Learning (RL) to finetune your model (experimental), use advanced evaluation metrics to judge generated answers by the model, track and compare your model performance visually, and easily export your model to the Hugging Face Hub and share it with the community.

LLM-jp-eval is a tool designed to automatically evaluate Japanese large language models across multiple datasets. It provides functionalities such as converting existing Japanese evaluation data to text generation task evaluation datasets, executing evaluations of large language models across multiple datasets, and generating instruction data (jaster) in the format of evaluation data prompts. Users can manage the evaluation settings through a config file and use Hydra to load them. The tool supports saving evaluation results and logs using wandb. Users can add new evaluation datasets by following specific steps and guidelines provided in the tool's documentation. It is important to note that using jaster for instruction tuning can lead to artificially high evaluation scores, so caution is advised when interpreting the results.

Awesome-LLM is a curated list of resources related to large language models, focusing on papers, projects, frameworks, tools, tutorials, courses, opinions, and other useful resources in the field. It covers trending LLM projects, milestone papers, other papers, open LLM projects, LLM training frameworks, LLM evaluation frameworks, tools for deploying LLM, prompting libraries & tools, tutorials, courses, books, and opinions. The repository provides a comprehensive overview of the latest advancements and resources in the field of large language models.

BoCoEL is a tool that leverages Bayesian Optimization to efficiently evaluate large language models by selecting a subset of the corpus for evaluation. It encodes individual entries into embeddings, uses Bayesian optimization to select queries, retrieves from the corpus, and provides easily managed evaluations. The tool aims to reduce computation costs during evaluation with a dynamic budget, supporting models like GPT2, Pythia, and LLAMA through integration with Hugging Face transformers and datasets. BoCoEL offers a modular design and efficient representation of the corpus to enhance evaluation quality.

CLadder is a repository containing the CLadder dataset for evaluating causal reasoning in language models. The dataset consists of yes/no questions in natural language that require statistical and causal inference to answer. It includes fields such as question_id, given_info, question, answer, reasoning, and metadata like query_type and rung. The dataset also provides prompts for evaluating language models and example questions with associated reasoning steps. Additionally, it offers dataset statistics, data variants, and code setup instructions for using the repository.

Uncheatable Eval is a tool designed to assess the language modeling capabilities of LLMs on real-time, newly generated data from the internet. It aims to provide a reliable evaluation method that is immune to data leaks and cannot be gamed. The tool supports the evaluation of Hugging Face AutoModelForCausalLM models and RWKV models by calculating the sum of negative log probabilities on new texts from various sources such as recent papers on arXiv, new projects on GitHub, news articles, and more. Uncheatable Eval ensures that the evaluation data is not included in the training sets of publicly released models, thus offering a fair assessment of the models' performance.

LLM-jp-eval is a tool designed to automatically evaluate Japanese large language models across multiple datasets. It provides functionalities such as converting existing Japanese evaluation data to text generation task evaluation datasets, executing evaluations of large language models across multiple datasets, and generating instruction data (jaster) in the format of evaluation data prompts. Users can manage the evaluation settings through a config file and use Hydra to load them. The tool supports saving evaluation results and logs using wandb. Users can add new evaluation datasets by following specific steps and guidelines provided in the tool's documentation. It is important to note that using jaster for instruction tuning can lead to artificially high evaluation scores, so caution is advised when interpreting the results.

AlignBench is the first comprehensive evaluation benchmark for assessing the alignment level of Chinese large models across multiple dimensions. It includes introduction information, data, and code related to AlignBench. The benchmark aims to evaluate the alignment performance of Chinese large language models through a multi-dimensional and rule-calibrated evaluation method, enhancing reliability and interpretability.

LiveBench is a benchmark tool designed for Language Model Models (LLMs) with a focus on limiting contamination through monthly new questions based on recent datasets, arXiv papers, news articles, and IMDb movie synopses. It provides verifiable, objective ground-truth answers for accurate scoring without an LLM judge. The tool offers 18 diverse tasks across 6 categories and promises to release more challenging tasks over time. LiveBench is built on FastChat's llm_judge module and incorporates code from LiveCodeBench and IFEval.

Evalchemy is a unified and easy-to-use toolkit for evaluating language models, focusing on post-trained models. It integrates multiple existing benchmarks such as RepoBench, AlpacaEval, and ZeroEval. Key features include unified installation, parallel evaluation, simplified usage, and results management. Users can run various benchmarks with a consistent command-line interface and track results locally or integrate with a database for systematic tracking and leaderboard submission.

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.

AgentCloud is an open-source platform that enables companies to build and deploy private LLM chat apps, empowering teams to securely interact with their data. It comprises three main components: Agent Backend, Webapp, and Vector Proxy. To run this project locally, clone the repository, install Docker, and start the services. The project is licensed under the GNU Affero General Public License, version 3 only. Contributions and feedback are welcome from the community.

This framework generates fuzz targets for real-world `C`/`C++` projects with various Large Language Models (LLM) and benchmarks them via the `OSS-Fuzz` platform. It manages to successfully leverage LLMs to generate valid fuzz targets (which generate non-zero coverage increase) for 160 C/C++ projects. The maximum line coverage increase is 29% from the existing human-written targets.

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.