Free GPT Tools

What is GPT ?

Generative pre-trained transformers (GPT) are a type of large language model (LLM) and a prominent framework for generative artificial intelligence. The first GPT was introduced in 2018 by OpenAI. GPT models are artificial neural networks that are based on the transformer architecture, pre-trained on large data sets of unlabelled text, and able to generate novel human-like content. As of 2023, most LLMs have these characteristics and are sometimes referred to broadly as GPTs.

GPT

What is the official ChatGPT website?

The official ChatGPT website is https://chat.openai.com/. Start by going to chat.openai.com and then use an email address or a Google or Microsoft account to sign up. You need to create an account on the OpenAI website to log in and access ChatGPT, but it is free.

ChatGPT Homepage

ChatGPT API Resources for Developers

Official ChatGPT API Resources for Developers

ChatGPT Playground

Unofficial ChatGPT API Resources for Developers

Categories of GPT applications

There are various types of applications for GPT (Generative Pre-trained Transformer) models like the one you're currently interacting with. Some common categories of GPT applications include:

Natural Language Understanding (NLU): GPT models are used for tasks like text classification, sentiment analysis, and named entity recognition, where they can understand and process human language.
Language Generation: GPT can generate human-like text, making it useful for tasks like content generation, chatbots, and creative writing assistance.
Translation: GPT models have been applied to machine translation, allowing for more accurate and context-aware translations between languages.
Question Answering: GPT can be used to answer questions based on a given context, which is valuable for chatbots and search engines.
Text Summarization: GPT models can summarize long texts into shorter, coherent summaries, making it useful for news aggregation and content curation.
Conversational AI: GPT-based chatbots and virtual assistants are employed in customer support, help desks, and even as companions in various applications.
Recommendation Systems: GPT models can be used to personalize content recommendations by understanding user preferences from their interactions.
Medical and Scientific Research: GPT is applied in analyzing and generating scientific texts and medical records, aiding in research and diagnosis.
Code Generation: Developers use GPT to assist in writing code, generating code snippets, and even providing explanations for code.
Gaming: GPT can be used to create in-game dialogues, provide hints, and create realistic, dynamic non-player characters (NPCs).
Content Moderation: GPT models help in identifying and moderating harmful or inappropriate content on platforms like social media.
Financial Analysis: GPT is applied in financial markets for sentiment analysis, news analysis, and generating reports.

These are just a few examples, and the field of GPT applications is continually evolving with new use cases emerging as the technology advances. The versatility and natural language understanding capabilities of GPT have made it a powerful tool in various industries.

Awesome ChatGPT Prompts

Open LLM Alternative To GPT

LLaMA - A foundational, 65-billion-parameter large language model.
- Alpaca - A model fine-tuned from the LLaMA 7B model on 52K instruction-following demonstrations.
- Flan-Alpaca - Instruction Tuning from Humans and Machines.
- Baize - Baize is an open-source chat model trained with LoRA. It uses 100k dialogs generated by letting ChatGPT chat with itself.
- Cabrita - A portuguese finetuned instruction LLaMA.
- Vicuna - An Open-Source Chatbot Impressing GPT-4 with 90% ChatGPT Quality.
- Llama-X - Open Academic Research on Improving LLaMA to SOTA LLM.
- Chinese-Vicuna - A Chinese Instruction-following LLaMA-based Model.
- GPTQ-for-LLaMA - 4 bits quantization of LLaMA using GPTQ.
- GPT4All - Demo, data, and code to train open-source assistant-style large language model based on GPT-J and LLaMa.
- Koala - A Dialogue Model for Academic Research
- BELLE - Be Everyone's Large Language model Engine
- StackLLaMA - A hands-on guide to train LLaMA with RLHF.
- RedPajama - An Open Source Recipe to Reproduce LLaMA training dataset.
- Chimera - Latin Phoenix.
- WizardLM|WizardCoder - Family of instruction-following LLMs powered by Evol-Instruct: WizardLM, WizardCoder.
- CaMA - a Chinese-English Bilingual LLaMA Model.
- Orca - Microsoft's finetuned LLaMA model that reportedly matches GPT3.5, finetuned against 5M of data, ChatGPT, and GPT4
- BayLing - an English/Chinese LLM equipped with advanced language alignment, showing superior capability in English/Chinese generation, instruction following and multi-turn interaction.
- UltraLM - Large-scale, Informative, and Diverse Multi-round Chat Models.
- Guanaco - QLoRA tuned LLaMA
BLOOM - BigScience Large Open-science Open-access Multilingual Language Model BLOOM-LoRA
- BLOOMZ&mT0 - a family of models capable of following human instructions in dozens of languages zero-shot.
- Phoenix
T5 - Text-to-Text Transfer Transformer
- T0 - Multitask Prompted Training Enables Zero-Shot Task Generalization
OPT - Open Pre-trained Transformer Language Models.
UL2 - a unified framework for pretraining models that are universally effective across datasets and setups.
GLM- GLM is a General Language Model pretrained with an autoregressive blank-filling objective and can be finetuned on various natural language understanding and generation tasks.
- ChatGLM-6B - ChatGLM-6B is an open-source conversational language model that supports both Chinese and English languages. It is based on the General Language Model (GLM) architecture and has 62 billion parameters.
- ChatGLM2-6B - An Open Bilingual Chat LLM.
RWKV - Parallelizable RNN with Transformer-level LLM Performance.
- ChatRWKV - ChatRWKV is like ChatGPT but powered by my RWKV (100% RNN) language model.
StableLM - Stability AI Language Models.
YaLM - a GPT-like neural network for generating and processing text. It can be used freely by developers and researchers from all over the world.
GPT-Neo - An implementation of model & data parallel GPT3-like models using the mesh-tensorflow library.
GPT-J - A 6 billion parameter, autoregressive text generation model trained on The Pile.
- Dolly - a cheap-to-build LLM that exhibits a surprising degree of the instruction following capabilities exhibited by ChatGPT.
Pythia - Interpreting Autoregressive Transformers Across Time and Scale
- Dolly 2.0 - the first open source, instruction-following LLM, fine-tuned on a human-generated instruction dataset licensed for research and commercial use.
OpenFlamingo - an open-source reproduction of DeepMind's Flamingo model.
Cerebras-GPT - A Family of Open, Compute-efficient, Large Language Models.
GALACTICA - The GALACTICA models are trained on a large-scale scientific corpus.
- GALPACA - GALACTICA 30B fine-tuned on the Alpaca dataset.
Palmyra - Palmyra Base was primarily pre-trained with English text.
Camel - a state-of-the-art instruction-following large language model designed to deliver exceptional performance and versatility.
h2oGPT
PanGu-α - PanGu-α is a 200B parameter autoregressive pretrained Chinese language model develped by Huawei Noah's Ark Lab, MindSpore Team and Peng Cheng Laboratory.
MOSS - MOSS is an open-source conversational language model that supports both Chinese and English languages as well as multiple plugins.
Open-Assistant - a project meant to give everyone access to a great chat based large language model.
- HuggingChat - Powered by Open Assistant's latest model – the best open source chat model right now and @huggingface Inference API.
StarCoder - Hugging Face LLM for Code
MPT-7B - Open LLM for commercial use by MosaicML
Falcon - Falcon LLM is a foundational large language model (LLM) with 40 billion parameters trained on one trillion tokens. TII has now released Falcon LLM – a 40B model.
XGen - Salesforce open-source LLMs with 8k sequence length.
baichuan-7B - baichuan-7B is an open-source, commercially viable large-scale pre-trained language model developed by Baichuan Intelligence.
Aquila - Wudao·Tianying Language Model is the first open-source language model that offers bilingual knowledge in both Chinese and English, supports commercial licensing agreements, and complies with domestic data regulations.

Chinese LLM Alternative To GPT

ChatGLM - One of the best open-source base models in the Chinese domain, optimized for Chinese question-answering and conversations. It has undergone training with approximately 1 trillion identifiers in both Chinese and English, supplemented with techniques such as supervised fine-tuning, self-feedback, and human feedback reinforcement.
ChatGLM2-6B - The second generation version of the open-source bilingual dialogue model ChatGLM-6B. While retaining numerous excellent features of the first-generation model, such as smooth conversation flow and low deployment barriers, it introduces a hybrid objective function for GLM. It has undergone pre-training with 1.4 trillion Chinese-English identifiers and aligning with human preferences. The context length of the base model has been extended to 32K, and during the conversation phase, it uses an 8K context length for training. It also implements more efficient inference speed and lower memory usage through Multi-Query Attention technology. Commercial usage is allowed.
Chinese-LLaMA-Alpaca - Chinese LLaMA & Alpaca large language models with local CPU/GPU deployment. Based on the original LLaMA, it expands the Chinese vocabulary and undergoes secondary pre-training using Chinese data.
Chinese-LLaMA-Alpaca-2 - This project will release Chinese LLaMA-2 & Alpaca-2 large language models based on the commercially available LLaMA-2.
Chinese-LlaMA2 - This project, based on the commercially available LLaMA-2, is undertaking the Chinese localization of Llama 2. This includes Chinese-LlaMA2: pre-training Llama 2 on a 42GB Chinese corpus as the first step, with plans to increase the training scale. It also includes Chinese-LlaMA2-chat: fine-tuning Chinese-LlaMA2 for command-based and multi-turn dialogue interactions to adapt to various application scenarios. Additionally, they are considering a faster Chinese adaptation solution, Chinese-LlaMA2-sft-v0, which involves direct fine-tuning of LlaMA-2 using existing open-source Chinese command or dialogue data (to be open-sourced soon).
Llama2-Chinese - This project focuses on optimizing and building upon the Llama2 model in the Chinese context. It undergoes continuous iterations and upgrades for Chinese capabilities starting from pre-training, using large-scale Chinese data.
OpenChineseLLaMA - Based on LLaMA-7B and incrementally pre-trained with Chinese datasets, this serves as the base model for a Chinese large language model. Compared to the original LLaMA, this model has significantly improved Chinese comprehension and generation capabilities and has achieved outstanding results in various downstream tasks.
BELLE - Open-sourced a series of models optimized based on BLOOMZ and LLaMA, including training data, related models, training code, application scenarios, and will continuously evaluate the impact of different training data and algorithms on model performance.
Panda - Open-sourced language models for continuous pre-training in the Chinese domain using LLaMA-7B, -13B, -33B, -65B. It utilizes close to 15 million data points for secondary pre-training.
Robin - Robin is a bilingual large language model developed by the LMFlow team at the Hong Kong University of Science and Technology. The second-generation Robin model, fine-tuned with only 180,000 data points, achieved first place in the Huggingface rankings. LMFlow supports rapid training of personalized models, requiring only a single RTX 3090 graphics card and 5 hours for fine-tuning a customized 70-billion-parameter model.
Fengshenbang-LM - Fengshenbang-LM (封神榜大模型) is an open-source large model system led by the IDEA Research Institute's Cognitive Computing and Natural Language Research Center. This project has open-sourced the Jiang Ziya General Large Model V1, which is a massive pre-trained model based on LLaMa with 13 billion parameters. It has capabilities for translation, programming, text classification, information extraction, summarization, copywriting generation, common sense question-answering, and mathematical calculations. In addition to the Jiang Ziya series models, this project has also open-sourced other models like Taiyi and Erlangshen series.
BiLLa - This project open-sources an enhanced bilingual LLaMa model with improved Chinese comprehension while minimizing the impact on the original LLaMa English capabilities. The training process includes additional task-specific data, using ChatGPT for generation and reinforcing the model's understanding of task-solving logic. It involves full-parameter updates to achieve better generation results.
Moss - An open-source conversational language model that supports both Chinese and English and various plugins. The MOSS base language model is pre-trained on approximately 700 billion Chinese and English words and code words. It subsequently undergoes fine-tuning for dialogue instructions, plugin-enhanced learning, and human preference training to have multi-turn conversation abilities and use multiple plugins.
Luotuo-Chinese-LLM - This project encompasses a series of open-source Chinese large language model projects, including models fine-tuned based on existing open-source models like ChatGLM, MOSS, and LLaMA. It also includes instruction fine-tuning datasets.
Linly - It provides the Linly-ChatFlow Chinese conversation model, the Linly-Chinese-LLaMA basic model, and their training data. The Chinese basic model is based on LLaMA and undergoes extensive instruction following training using publicly available multilingual instruction data, achieving the Linly-ChatFlow conversation model.
Firefly - Firefly is an open-source Chinese large language model project that includes data, fine-tuning code, and multiple models fine-tuned based on Bloom, Baichuan, and others. It supports full-parameter instruction fine-tuning, QLoRA low-cost, efficient instruction fine-tuning, and LoRA instruction fine-tuning. It integrates LoRA with the base model for more convenient inference.
ChatYuan - A series of functional bilingual conversation large language models released by Yuan Language Intelligence. It includes optimizations in fine-tuning data, human feedback reinforcement learning, and thought chains.
ChatRWKV - It has open-sourced a series of Chat models based on the RWKV architecture, including both English and Chinese. It includes models such as Raven, Novel-ChnEng, Novel-Ch, and Novel-ChnEng-ChnPro, capable of casual conversation and creative writing like poetry and novels, with models of scales 7B and 14B.
CPM-Bee - A fully open-source, commercially usable Chinese-English base model with 100 billion parameters. It uses a Transformer autoregressive architecture and is pre-trained on trillions of high-quality texts. Developers and researchers can adapt CPM-Bee to create domain-specific application models.
TigerBot - A large-scale language model (LLM) for multiple languages and tasks. It has open-sourced models such as TigerBot-7B, TigerBot-7B-base, TigerBot-180B, basic training and inference code, 100GB of pre-training data, and domain-specific data in fields like finance, law, and encyclopedias, along with APIs.
Shusheng·Puyu - Shusheng·Puyu is a large-scale language model developed by SenseTime, in collaboration with the Chinese University of Hong Kong, Fudan University, and Shanghai Jiao Tong University. It boasts a staggering 104 billion parameters and was trained on a multilingual high-quality dataset containing 1.6 trillion tokens.
Aquila - Developed by the Flagship Institute, the Aquila language model inherits architectural design elements from GPT-3 and LLaMA while introducing more efficient low-level operator implementations. It features a redesigned tokenizer for both Chinese and English and leverages improved parallel training methods with BMTrain. Aquila is trained from scratch on high-quality data, offering superior performance on smaller datasets and shorter training times compared to other open-source models. It's the first large-scale open-source language model that supports both Chinese and English knowledge, is commercially licensable, and complies with domestic data regulations.
Baichuan-7B - Developed by Baichuan Intelligence, Baichuan-7B is an open-source, commercially available large-scale pre-trained language model based on the Transformer architecture. It comprises 70 billion parameters and is trained on approximately 12 trillion tokens. Baichuan-7B supports both Chinese and English and has a context window length of 4096. It achieves top-tier results on standard Chinese and English benchmarks (C-EVAL/MMLU).
Baichuan-13B - Baichuan-13B is an open-source, commercially available large-scale language model developed by Baichuan Intelligence, building on the success of Baichuan-7B. It features 13 billion parameters and outperforms other models of the same size on authoritative Chinese and English benchmarks. The project includes two versions: pre-trained (Baichuan-13B-Base) and aligned (Baichuan-13B-Chat).
Baichuan2 - Baichuan2 is the next-generation open-source large language model introduced by Baichuan Intelligence. It is trained on a high-quality corpus with 26 trillion tokens and excels in various general and domain-specific benchmarks in Chinese, English, and multiple languages. It includes Base versions with 7B and 13B parameters and a Chat version with 4-bit quantization.
Anima - Anima is an open-source Chinese language model with 33 billion parameters based on QLoRA. This model was fine-tuned using the guanaco_belle_merge_v1.0 training dataset from the Chinese-Vicuna project, and it achieved favorable results through Elo rating tournament evaluations.
KnowLM - The KnowLM project aims to release open-source large models and corresponding model weights to help address issues related to knowledge inaccuracies, knowledge update difficulties, and potential errors and biases in large models. The project's first phase focuses on an extraction-based large model built on LLaMA (13B) and further pre-trained with Chinese and English texts. Knowledge extraction tasks are optimized using knowledge graph transformation techniques.
BayLing - BayLing is a universal large model developed by the Natural Language Processing Team at the Institute of Computing Technology, Chinese Academy of Sciences. BayLing is built upon the LLaMA model and employs interactive translation tasks for fine-tuning, aiming to achieve language alignment and alignment with human intent. BayLing demonstrates better performance in Chinese/English in various evaluations, including multilingual translation, interactive translation, general tasks, and standardized tests. An online beta version of the demo is available for experimentation.
YuLan-Chat - YuLan-Chat is a large language model based on LLaMA, developed by researchers at the Renmin University of China. It is fine-tuned for English and Chinese instructions and performs well in adhering to these instructions. YuLan-Chat can engage in conversations with users and is optimized for deployment on GPUs (A800-80G or RTX3090) after quantization.
PolyLM - PolyLM is a multilingual language model trained from scratch on a dataset with 640 billion words. It comes in two sizes, 1.7 billion and 13 billion parameters, covering languages such as Chinese, English, Russian, Spanish, French, Portuguese, German, Italian, Dutch, Polish, Arabic, Turkish, Hebrew, Japanese, Korean, Thai, Vietnamese, and Indonesian, with a particular focus on Asian languages.
Qwen-7B - Qwen-7B is a 7-billion-parameter model in the Qwen large model series developed by Alibaba Cloud. It is pre-trained on a vast dataset with over 2.2 trillion tokens, encompassing various data types, including text and code, in both general and specialized domains. It supports an 8K context length, and specific optimizations have been made for plugin invocation and upgrading to an Agent.
huozi - Developed by researchers and students from Harbin Institute of Technology's Natural Language Processing Research Institute, huozi is an open-source, commercially available large-scale pre-trained language model. It is based on a Bloom structure with 70 billion parameters, supports both Chinese and English, has a context window length of 2048, and provides models trained using reinforcement learning from human feedback (RLHF) and a fully manually annotated Chinese preference dataset with 16.9K samples.
YaYi - The YaYi large model is fine-tuned on a dataset of millions of high-quality domain-specific data points, covering media promotion, sentiment analysis, public safety, financial risk control, and urban governance, among other areas. It supports a wide range of natural language instructions and has been enhanced through user feedback during continuous testing. An optimized Chinese version based on LLaMA 2 has been open-sourced, exploring the latest practices for Chinese multi-domain tasks.
XVERSE-13B - XVERSE-13B is a large language model that supports multiple languages, developed by Shenzhen Yuanxiang Technology. It uses a standard Transformer network structure with a context length of 8K, the longest among models of its size. It is extensively trained on a diverse dataset containing over 14 trillion tokens, including Chinese, English, Russian, Spanish, and more. It features a word segmentation model trained on a dataset of over 100GB using the BPE algorithm, enabling support for multiple languages without extending the vocabulary.
VisualGLM-6B - VisualGLM-6B is an open-source multimodal conversational language model that supports images, Chinese, and English. The language model, ChatGLM-6B, has 62 billion parameters, and the image part is constructed by training the BLIP2-Qformer model, resulting in a total of 78 billion parameters. It is pre-trained on high-quality Chinese text-image pairs and filtered English text-image pairs from the CogView dataset.
VisCPM - VisCPM is an open-source multimodal large model series supporting both chat-based multimodal interactions (VisCPM-Chat) and text-to-image generation (VisCPM-Paint). Based on the billion-parameter language model CPM-Bee (10B), VisCPM incorporates visual encoders (Q-Former) and visual decoders (Diffusion-UNet) to handle visual input and output. Thanks to CPM-Bee's strong bilingual capabilities, VisCPM demonstrates excellent multimodal capabilities in Chinese, even when pre-trained only on English multimodal data.
Visual-Chinese-LLaMA-Alpaca - VisualCLA is a multimodal Chinese large model based on the LLaMA and Alpaca projects. It enhances LLaMA with image encoding modules, enabling it to process visual information. VisualCLA undergoes multimodal pre-training on Chinese text-image data, aligning visual and textual representations. It is fine-tuned with multimodal instruction datasets to enhance its understanding, execution, and conversation abilities in response to multimodal instructions.
LLaSM - LLaSM is the first open-source, commercially available dialogue model that supports both spoken and written text in Chinese and English. Convenient speech input significantly improves the user experience with large models based on text input, eliminating the need for complex ASR solutions and potential errors. LLaSM is currently open-sourced in versions such as LLaSM-Chinese-Llama-2-7B and LLaSM-Baichuan-7B, along with datasets.
Qwen-VL - Qwen-VL is a large-scale multimodal language model developed by Alibaba Cloud. It accepts input in the form of images, text, and detection boxes and generates outputs in text and detection boxes. It excels in standard evaluations for various multimodal tasks and supports multilingual conversations in English and Chinese. It also facilitates multi-image input, image-based question-answering, and multi-image content creation. Qwen-VL is the first open-source model to use 448-pixel resolution, providing finer-grained text recognition, document question-answering, and detection box annotation capabilities compared to other open-source LVLM models that use 224-pixel resolution.

Free GPT Tools

OpenGPTs Tutorial: Customizing Language Models and Tools, Quick Deployment, and Flexible Customization

2023-11-09

penGPTs is an open-source project aimed at providing an experience similar to OpenAI's GPT (Generative Pre-trained Transformer) models. It is built on the foundation of LangChain, LangServe, and LangSmith, allowing users greater flexibility in controlling the language models they use, the prompts they employ, and the tools provided to the models.

GPT Pilot: In-Depth Explanation of AI-Assisted Development Tools, Their Uses, Installation and Configuration, Working Principles, and Comparisons

2023-11-06

GPT Pilot is a tool designed to help developers build applications faster. Its primary goal is to explore how GPT-4 can be leveraged to generate fully functional, production-ready applications while allowing developers to oversee the development process.

ChatDev: Installation and Usage Tutorial

2023-11-03

ChatDev is a "virtual software company" that operates through various intelligent agents, each with different roles. These agents work together to develop software applications by participating in specialized functional seminars, including tasks such as designing, coding, testing, and documenting. ChatDev's mission is to "revolutionize the digital world through programming."

ChatGLM3: Open-Source Dialogue Model Deployment Guide and Pros & Cons

2023-10-31

ChatGLM3 is a new generation of pre-trained dialogue models jointly developed by Zhipu AI and Tsinghua KEG. The ChatGLM3-6B model, which is part of the ChatGLM3 series, is open-source.

MemGPT: Revolutionizing Language Models with Unlimited Context

2023-10-31

MemGPT is a novel language model system. It is designed to address the limitations of traditional large language models (LLMs) with fixed-length context windows, such as GPT-3 and GPT-4. These limitations restrict the amount of text and information that the models can consider in their responses.

DB-GPT: Secure Data Interaction with GPT Models - Features and How to Use

2023-10-31

DB-GPT is an experimental open-source project that utilizes localized GPT (Generative Pre-trained Transformer) large models to interact with data and various environments. This project is designed to offer a secure and private way of interacting with data.

GPT4All: Customized Language Models for Everyday Hardware

2023-10-26

GPT4All is an open-source software ecosystem designed to allow individuals to train and deploy large language models (LLMs) on everyday hardware.

Quivr: Your AI-Powered Second Brain for Data Management

2023-10-24

Quivr is a project described as "your second brain" that utilizes the power of Generative AI to store and retrieve unstructured information. It can be thought of as a tool similar to Obsidian but enhanced with AI capabilities.

LaWGPT: Empowering Legal Language Understanding with Chinese Language Models

2023-10-24

LaWGPT is a series of large language models developed with a focus on Chinese legal knowledge. These models are designed to enhance the understanding and generation of legal content in the Chinese language. LaWGPT is based on a foundation of general Chinese language models such as Chinese-LLaMA and ChatGLM but has been fine-tuned and expanded specifically for legal applications.

FlowiseAI: Empowering Chatbots and AI Apps with Low-Code Ease

2023-10-24

FlowiseAI is a low-code/no-code platform designed to make it easy for people to visualize and build LLM (Language Model) apps. It provides a user-friendly interface for creating applications that leverage natural language processing and AI capabilities without requiring extensive programming skills.

Unlocking the Potential of GPT-4 and Language Models with Gpt4free

2023-10-24

Gpt4free is a project that provides access to GPT-4 and other language models for free. It allows users to interact with these models via an API, providing natural language processing capabilities for a wide range of tasks.

ChatGPT Next Web: A Versatile Open-Source Natural Language Interaction Tool

2023-10-24

ChatGPT Next Web is an open-source web application built on top of the GPT-3.5 model. It is designed to facilitate natural language interaction with the model and offers a user-friendly and customizable interface. ChatGPT Next Web aims to provide users with a convenient and flexible way to engage in conversations with the GPT-3.5 model for various purposes, such as chatbots, content generation, language translation, and more.

Unlocking the Power of MetaGPT: A Multi-Agent Framework for Complex Tasks

2023-10-24

MetaGPT is an innovative technology that leverages Standardized Operating Procedures (SOPs) to coordinate Large Language Model (LLM)-driven multi-agent systems, revolutionizing the landscape of software development and collaborative task resolution. By integrating human workflows and domain-specific knowledge into the agent architecture, MetaGPT enables effective multi-agent collaboration, enhancing the efficiency of their cooperative efforts.

From Meta AI: The LLaMA Large Language Models You Need to Know About

2023-10-24

LLaMA (Large Language Model Meta AI) is a large-scale, open, and efficient language model series developed by Meta AI. It comes in four versions: 7B, 13B, 33B, and 65B, with the largest having 650 billion parameters. All these models are trained on publicly available datasets, without any custom datasets, ensuring compatibility with open-source and reproducibility. The total training data, tokenized, comprises about 1.4 trillion tokens.