Llama 3.1 Alternatives

Experiment, and push the boundaries of individual alignment, artificial consciousness, open-source software, and decentralization, in ways that monolithic companies and governments are too afraid to try. Hermes 3 contains advanced long-term context retention and multi-turn conversation capability, complex roleplaying and internal monologue abilities, and enhanced agentic function-calling. Our training data aggressively encourages the model to follow the system and instruction prompts exactly and in an adaptive manner. Hermes 3 was created by fine-tuning Llama 3.1 8B, 70B, and 405B, and training on a dataset of primarily synthetically generated responses. The model boasts comparable and superior performance to Llama 3.1 while unlocking deeper capabilities in reasoning and creativity. Hermes 3 is a series of instruct and tool-use models with strong reasoning and creative abilities.

This repository contains the research preview of LongLLaMA, a large language model capable of handling long contexts of 256k tokens or even more. LongLLaMA is built upon the foundation of OpenLLaMA and fine-tuned using the Focused Transformer (FoT) method. LongLLaMA code is built upon the foundation of Code Llama. We release a smaller 3B base variant (not instruction tuned) of the LongLLaMA model on a permissive license (Apache 2.0) and inference code supporting longer contexts on hugging face. Our model weights can serve as the drop-in replacement of LLaMA in existing implementations (for short context up to 2048 tokens). Additionally, we provide evaluation results and comparisons against the original OpenLLaMA models.

The next generation of our open source large language model. This release includes model weights and starting code for pretrained and fine-tuned Llama language models — ranging from 7B to 70B parameters. Llama 2 pretrained models are trained on 2 trillion tokens, and have double the context length than Llama 1. Its fine-tuned models have been trained on over 1 million human annotations. Llama 2 outperforms other open source language models on many external benchmarks, including reasoning, coding, proficiency, and knowledge tests. Llama 2 was pretrained on publicly available online data sources. The fine-tuned model, Llama-2-chat, leverages publicly available instruction datasets and over 1 million human annotations. We have a broad range of supporters around the world who believe in our open approach to today’s AI — companies that have given early feedback and are excited to build with Llama 2.

Vicuna-13B is an open-source chatbot trained by fine-tuning LLaMA on user-shared conversations collected from ShareGPT. Preliminary evaluation using GPT-4 as a judge shows Vicuna-13B achieves more than 90%* quality of OpenAI ChatGPT and Google Bard while outperforming other models like LLaMA and Stanford Alpaca in more than 90%* of cases. The cost of training Vicuna-13B is around $300. The code and weights, along with an online demo, are publicly available for non-commercial use.

Falcon-40B is a 40B parameters causal decoder-only model built by TII and trained on 1,000B tokens of RefinedWeb enhanced with curated corpora. It is made available under the Apache 2.0 license. Why use Falcon-40B? It is the best open-source model currently available. Falcon-40B outperforms LLaMA, StableLM, RedPajama, MPT, etc. See the OpenLLM Leaderboard. It features an architecture optimized for inference, with FlashAttention and multiquery. It is made available under a permissive Apache 2.0 license allowing for commercial use, without any royalties or restrictions. ⚠️ This is a raw, pretrained model, which should be further finetuned for most usecases. If you are looking for a version better suited to taking generic instructions in a chat format, we recommend taking a look at Falcon-40B-Instruct.

We just launched X1 large series of Models. Giga ML's most powerful model is available for pre-training and fine-tuning with on-prem deployment. Since we are Open AI compatible, your existing integrations with long chain, llama-index, and all others work seamlessly. You can continue pre-training of LLM's with domain-specific data books or docs or company docs. The world of large language models (LLMs) rapidly expanding, offering unprecedented opportunities for natural language processing across various domains. However, some critical challenges have remained unaddressed. At Giga ML, we proudly introduce the X1 Large 32k model, a pioneering on-premise LLM solution that addresses these critical issues.

Introducing MPT-7B, the latest entry in our MosaicML Foundation Series. MPT-7B is a transformer trained from scratch on 1T tokens of text and code. It is open source, available for commercial use, and matches the quality of LLaMA-7B. MPT-7B was trained on the MosaicML platform in 9.5 days with zero human intervention at a cost of ~$200k. Now you can train, finetune, and deploy your own private MPT models, either starting from one of our checkpoints or training from scratch. For inspiration, we are also releasing three finetuned models in addition to the base MPT-7B: MPT-7B-Instruct, MPT-7B-Chat, and MPT-7B-StoryWriter-65k+, the last of which uses a context length of 65k tokens!

Stability AI and its CarperAI lab are proud to announce FreeWilly1 and its successor FreeWilly2, two powerful new, open access, Large Language Models (LLMs). Both models demonstrate exceptional reasoning ability across varied benchmarks. FreeWilly1 leverages the original LLaMA 65B foundation model and was carefully fine-tuned with a new synthetically-generated dataset using Supervised Fine-Tune (SFT) in standard Alpaca format. Similarly, FreeWilly2 leverages the LLaMA 2 70B foundation model to reach a performance that compares favorably with GPT-3.5 for some tasks. The training for the FreeWilly models was directly inspired by the methodology pioneered by Microsoft in its paper: "Orca: Progressive Learning from Complex Explanation Traces of GPT-4.” While our data generation process is similar, we differ in our data sources.

Fast and accurate AI models suited for production. Highly-available inference API leveraging the most advanced NVIDIA GPUs. We selected the best open-source natural language processing (NLP) models from the community and deployed them for you. Fine-tune your own models - including GPT-J - or upload your in-house custom models, and deploy them easily to production. Upload or Train/Fine-Tune your own AI models - including GPT-J - from your dashboard, and use them straight away in production without worrying about deployment considerations like RAM usage, high-availability, scalability... You can upload and deploy as many models as you want to production.

LLaMA (Large Language Model Meta AI) is a state-of-the-art foundational large language model designed to help researchers advance their work in this subfield of AI. Smaller, more performant models such as LLaMA enable others in the research community who don’t have access to large amounts of infrastructure to study these models, further democratizing access in this important, fast-changing field. Training smaller foundation models like LLaMA is desirable in the large language model space because it requires far less computing power and resources to test new approaches, validate others’ work, and explore new use cases. Foundation models train on a large set of unlabeled data, which makes them ideal for fine-tuning for a variety of tasks. We are making LLaMA available at several sizes (7B, 13B, 33B, and 65B parameters) and also sharing a LLaMA model card that details how we built the model in keeping with our approach to Responsible AI practices.

Instruction-following models such as GPT-3.5 (text-DaVinci-003), ChatGPT, Claude, and Bing Chat have become increasingly powerful. Many users now interact with these models regularly and even use them for work. However, despite their widespread deployment, instruction-following models still have many deficiencies: they can generate false information, propagate social stereotypes, and produce toxic language. To make maximum progress on addressing these pressing problems, it is important for the academic community to engage. Unfortunately, doing research on instruction-following models in academia has been difficult, as there is no easily accessible model that comes close in capabilities to closed-source models such as OpenAI’s text-DaVinci-003. We are releasing our findings about an instruction-following language model, dubbed Alpaca, which is fine-tuned from Meta’s LLaMA 7B model.

GPT4All is an ecosystem to train and deploy powerful and customized large language models that run locally on consumer-grade CPUs. The goal is simple - be the best instruction-tuned assistant-style language model that any person or enterprise can freely use, distribute and build on. A GPT4All model is a 3GB - 8GB file that you can download and plug into the GPT4All open-source ecosystem software. Nomic AI supports and maintains this software ecosystem to enforce quality and security alongside spearheading the effort to allow any person or enterprise to easily train and deploy their own on-edge large language models. Data is one the most important ingredients to successfully building a powerful, general-purpose large language model. The GPT4All community has built the GPT4All open source data lake as a staging ground for contributing instruction and assistant tuning data for future GPT4All model trains.

Today, we are excited to introduce DBRX, an open, general-purpose LLM created by Databricks. Across a range of standard benchmarks, DBRX sets a new state-of-the-art for established open LLMs. Moreover, it provides the open community and enterprises building their own LLMs with capabilities that were previously limited to closed model APIs; according to our measurements, it surpasses GPT-3.5, and it is competitive with Gemini 1.0 Pro. It is an especially capable code model, surpassing specialized models like CodeLLaMA-70B in programming, in addition to its strength as a general-purpose LLM. This state-of-the-art quality comes with marked improvements in training and inference performance. DBRX advances the state-of-the-art in efficiency among open models thanks to its fine-grained mixture-of-experts (MoE) architecture. Inference is up to 2x faster than LLaMA2-70B, and DBRX is about 40% of the size of Grok-1 in terms of both total and active parameter counts.

Our enterprise-grade multimodal assistant carefully designed with privacy, security, and efficiency in mind. We train Yasa to read text, images, videos, and tabular data, with more modalities to come. Use it to generate ideas for creative tasks, get answers to basic questions, or derive insights from your internal data. Generate, train, compress, or deploy on-premise with a few simple commands. Use our proprietary algorithms to personalize our model to your data and use cases. We design proprietary algorithms involving retrieval, fine-tuning, self-supervised instruction tuning, and reinforcement learning to tune our model on your datasets.

PygmalionAI is a community dedicated to creating open-source projects based on EleutherAI's GPT-J 6B and Meta's LLaMA models. In simple terms, Pygmalion makes AI fine-tuned for chatting and roleplaying purposes. The current actively supported Pygmalion AI model is the 7B variant, based on Meta AI's LLaMA model. With only 18GB (or less) VRAM required, Pygmalion offers better chat capability than much larger language models with relatively minimal resources. Our curated dataset of high-quality roleplaying data ensures that your bot will be the optimal RP partner. Both the model weights and the code used to train it are completely open-source, and you can modify/re-distribute it for whatever purpose you want. Language models, including Pygmalion, generally run on GPUs since they need access to fast memory and massive processing power in order to output coherent text at an acceptable speed.

StarCoder and StarCoderBase are Large Language Models for Code (Code LLMs) trained on permissively licensed data from GitHub, including from 80+ programming languages, Git commits, GitHub issues, and Jupyter notebooks. Similar to LLaMA, we trained a ~15B parameter model for 1 trillion tokens. We fine-tuned StarCoderBase model for 35B Python tokens, resulting in a new model that we call StarCoder. We found that StarCoderBase outperforms existing open Code LLMs on popular programming benchmarks and matches or surpasses closed models such as code-cushman-001 from OpenAI (the original Codex model that powered early versions of GitHub Copilot). With a context length of over 8,000 tokens, the StarCoder models can process more input than any other open LLM, enabling a wide range of interesting applications. For example, by prompting the StarCoder models with a series of dialogues, we enabled them to act as a technical assistant.

Apply advanced coding and language models to a variety of use cases. Leverage large-scale, generative AI models with deep understandings of language and code to enable new reasoning and comprehension capabilities for building cutting-edge applications. Apply these coding and language models to a variety of use cases, such as writing assistance, code generation, and reasoning over data. Detect and mitigate harmful use with built-in responsible AI and access enterprise-grade Azure security. Gain access to generative models that have been pretrained with trillions of words. Apply them to new scenarios including language, code, reasoning, inferencing, and comprehension. Customize generative models with labeled data for your specific scenario using a simple REST API. Fine-tune your model's hyperparameters to increase accuracy of outputs. Use the few-shot learning capability to provide the API with examples and achieve more relevant results.

Code Llama is a large language model (LLM) that can use text prompts to generate code. Code Llama is state-of-the-art for publicly available LLMs on code tasks, and has the potential to make workflows faster and more efficient for current developers and lower the barrier to entry for people who are learning to code. Code Llama has the potential to be used as a productivity and educational tool to help programmers write more robust, well-documented software. Code Llama is a state-of-the-art LLM capable of generating code, and natural language about code, from both code and natural language prompts. Code Llama is free for research and commercial use. Code Llama is built on top of Llama 2 and is available in three models: Code Llama, the foundational code model; Codel Llama - Python specialized for Python; and Code Llama - Instruct, which is fine-tuned for understanding natural language instructions.

Qwen2 is the large language model series developed by Qwen team, Alibaba Cloud. Qwen2 is a series of large language models developed by the Qwen team at Alibaba Cloud. It includes both base language models and instruction-tuned models, ranging from 0.5 billion to 72 billion parameters, and features both dense models and a Mixture-of-Experts model. The Qwen2 series is designed to surpass most previous open-weight models, including its predecessor Qwen1.5, and to compete with proprietary models across a broad spectrum of benchmarks in language understanding, generation, multilingual capabilities, coding, mathematics, and reasoning.

Smaug-72B is a powerful open-source large language model (LLM) known for several key features: High Performance: It currently holds the top spot on the Hugging Face Open LLM leaderboard, surpassing models like GPT-3.5 in various benchmarks. This means it excels at tasks like understanding, responding to, and generating human-like text. Open Source: Unlike many other advanced LLMs, Smaug-72B is freely available for anyone to use and modify, fostering collaboration and innovation in the AI community. Focus on Reasoning and Math: It specifically shines in handling reasoning and mathematical tasks, attributing this strength to unique fine-tuning techniques developed by Abacus AI, the creators of Smaug-72B. Based on Qwen-72B: It's technically a fine-tuned version of another powerful LLM called Qwen-72B, released by Alibaba, further improving upon its capabilities. Overall, Smaug-72B represents a significant step forward in open-source AI.

Mistral NeMo, our new best small model. A state-of-the-art 12B model with 128k context length, and released under the Apache 2.0 license. Mistral NeMo is a 12B model built in collaboration with NVIDIA. Mistral NeMo offers a large context window of up to 128k tokens. Its reasoning, world knowledge, and coding accuracy are state-of-the-art in its size category. As it relies on standard architecture, Mistral NeMo is easy to use and a drop-in replacement in any system using Mistral 7B. We have released pre-trained base and instruction-tuned checkpoints under the Apache 2.0 license to promote adoption for researchers and enterprises. Mistral NeMo was trained with quantization awareness, enabling FP8 inference without any performance loss. The model is designed for global, multilingual applications. It is trained on function calling and has a large context window. Compared to Mistral 7B, it is much better at following precise instructions, reasoning, and handling multi-turn conversations.

Chinchilla is a large language model. Chinchilla uses the same compute budget as Gopher but with 70B parameters and 4× more more data. Chinchilla uniformly and significantly outperforms Gopher (280B), GPT-3 (175B), Jurassic-1 (178B), and Megatron-Turing NLG (530B) on a large range of downstream evaluation tasks. This also means that Chinchilla uses substantially less compute for fine-tuning and inference, greatly facilitating downstream usage. As a highlight, Chinchilla reaches a state-of-the-art average accuracy of 67.5% on the MMLU benchmark, greater than a 7% improvement over Gopher.

AI21 Studio provides API access to Jurassic-1 large-language-models. Our models power text generation and comprehension features in thousands of live applications. Take on any language task. Our Jurassic-1 models are trained to follow natural language instructions and require just a few examples to adapt to new tasks. Use our specialized APIs for common tasks like summarization, paraphrasing and more. Access superior results at a lower cost without reinventing the wheel. Need to fine-tune your own custom model? You're just 3 clicks away. Training is fast, affordable and trained models are deployed immediately. Give your users superpowers by embedding an AI co-writer in your app. Drive user engagement and success with features like long-form draft generation, paraphrasing, repurposing and custom auto-complete.

Aya is a new state-of-the-art, open-source, massively multilingual, generative large language research model (LLM) covering 101 different languages — more than double the number of languages covered by existing open-source models. Aya helps researchers unlock the powerful potential of LLMs for dozens of languages and cultures largely ignored by most advanced models on the market today. We are open-sourcing both the Aya model, as well as the largest multilingual instruction fine-tuned dataset to-date with a size of 513 million covering 114 languages. This data collection includes rare annotations from native and fluent speakers all around the world, ensuring that AI technology can effectively serve a broad global audience that have had limited access to-date.

Dolly is a cheap-to-build LLM that exhibits a surprising degree of the instruction following capabilities exhibited by ChatGPT. Whereas the work from the Alpaca team showed that state-of-the-art models could be coaxed into high quality instruction-following behavior, we find that even years-old open source models with much earlier architectures exhibit striking behaviors when fine tuned on a small corpus of instruction training data. Dolly works by taking an existing open source 6 billion parameter model from EleutherAI and modifying it ever so slightly to elicit instruction following capabilities such as brainstorming and text generation not present in the original model, using data from Alpaca.

ChatGLM-6B is an open-source, Chinese-English bilingual dialogue language model based on the General Language Model (GLM) architecture with 6.2 billion parameters. Combined with model quantization technology, users can deploy locally on consumer-grade graphics cards (only 6GB of video memory is required at the INT4 quantization level). ChatGLM-6B uses technology similar to ChatGPT, optimized for Chinese Q&A and dialogue. After about 1T identifiers of Chinese and English bilingual training, supplemented by supervision and fine-tuning, feedback self-help, human feedback reinforcement learning and other technologies, ChatGLM-6B with 6.2 billion parameters has been able to generate answers that are quite in line with human preferences.

Mixtral 8x7B is a high-quality sparse mixture of experts model (SMoE) with open weights. Licensed under Apache 2.0. Mixtral outperforms Llama 2 70B on most benchmarks with 6x faster inference. It is the strongest open-weight model with a permissive license and the best model overall regarding cost/performance trade-offs. In particular, it matches or outperforms GPT-3.5 on most standard benchmarks.

ChatGPT is a language model developed by OpenAI. It has been trained on a diverse range of internet text, allowing it to generate human-like responses to a variety of prompts. ChatGPT can be used for various natural language processing tasks, such as question answering, conversation, and text generation. ChatGPT is a pre-trained language model that uses deep learning algorithms to generate text. It was trained on a large corpus of text data, allowing it to generate human-like responses to a wide range of prompts. The model has a transformer architecture, which has been shown to be effective in many NLP tasks. In addition to generating text, ChatGPT can also be fine-tuned for specific NLP tasks such as question answering, text classification, and language translation. This allows developers to build powerful NLP applications that can perform specific tasks more accurately. ChatGPT can also process and generate code.

Koala is a chatbot trained by fine-tuning Meta’s LLaMA on dialogue data gathered from the web. Our results show that Koala can effectively respond to a variety of user queries, generating responses that are often preferred over Alpaca, and at least tied with ChatGPT in over half of the cases.

Announcing the launch of Jurassic-2, the latest generation of AI21 Studio’s foundation models, a game-changer in the field of AI, with top-tier quality and new capabilities. And that's not all, we're also releasing our task-specific APIs, with plug-and-play reading and writing capabilities that outperform competitors. Our focus at AI21 Studio is to help developers and businesses leverage reading and writing AI to build real-world products with tangible value. Today marks two important milestones with the release of Jurassic-2 and Task-Specific APIs, empowering you to bring generative AI to production. Jurassic-2 (or J2, as we like to call it) is the next generation of our foundation models with significant improvements in quality and new capabilities including zero-shot instruction-following, reduced latency, and multi-language support. Task-specific APIs provide developers with industry-leading APIs that perform specialized reading and writing tasks out-of-the-box.

CodeGemma is a collection of powerful, lightweight models that can perform a variety of coding tasks like fill-in-the-middle code completion, code generation, natural language understanding, mathematical reasoning, and instruction following. CodeGemma has 3 model variants, a 7B pre-trained variant that specializes in code completion and generation from code prefixes and/or suffixes, a 7B instruction-tuned variant for natural language-to-code chat and instruction following; and a state-of-the-art 2B pre-trained variant that provides up to 2x faster code completion. Complete lines, and functions, and even generate entire blocks of code, whether you're working locally or using Google Cloud resources. Trained on 500 billion tokens of primarily English language data from web documents, mathematics, and code, CodeGemma models generate code that's not only more syntactically correct but also semantically meaningful, reducing errors and debugging time.

With our enterprise Gen AI stack, go beyond your imagination and offload manual tasks to powerful assistants instantly – the sky is the limit. For enterprises where data security is paramount, fine-tune and deploy generative AI models on your own cloud, securely.

Megatron-Turing Natural Language Generation model (MT-NLG), is the largest and the most powerful monolithic transformer English language model with 530 billion parameters. This 105-layer, transformer-based MT-NLG improves upon the prior state-of-the-art models in zero-, one-, and few-shot settings. It demonstrates unmatched accuracy in a broad set of natural language tasks such as, Completion prediction, Reading comprehension, Commonsense reasoning, Natural language inferences, Word sense disambiguation, etc. With the intent of accelerating research on the largest English language model till date and enabling customers to experiment, employ and apply such a large language model on downstream language tasks - NVIDIA is pleased to announce an Early Access program for its managed API service to MT-NLG mode.

We tackle the hardest problems to make AI models compute efficient, helpful and trustworthy. We spearhead the family of open models, we give to our users and empower them to contribute their ideas. Mistral-7B-v0.1 is a small, yet powerful model adaptable to many use-cases. Mistral 7B is better than Llama 2 13B on all benchmarks, has natural coding abilities, and 8k sequence length. It’s released under Apache 2.0 license, and we made it easy to deploy on any cloud.

We are now releasing Phi-2, a 2.7 billion-parameter language model that demonstrates outstanding reasoning and language understanding capabilities, showcasing state-of-the-art performance among base language models with less than 13 billion parameters. On complex benchmarks Phi-2 matches or outperforms models up to 25x larger, thanks to new innovations in model scaling and training data curation. With its compact size, Phi-2 is an ideal playground for researchers, including for exploration around mechanistic interpretability, safety improvements, or fine-tuning experimentation on a variety of tasks. We have made Phi-2 available in the Azure AI Studio model catalog to foster research and development on language models.

An End-to-End MLLM that Accept Any-Form Referring and Ground Anything in Response. Ferret Model - Hybrid Region Representation + Spatial-aware Visual Sampler enable fine-grained and open-vocabulary referring and grounding in MLLM. GRIT Dataset (~1.1M) - A Large-scale, Hierarchical, Robust ground-and-refer instruction tuning dataset. Ferret-Bench - A multimodal evaluation benchmark that jointly requires Referring/Grounding, Semantics, Knowledge, and Reasoning.

Foundation models such as GPT-4 have driven rapid improvement in AI. However, the most powerful models are closed commercial models or only partially open. RedPajama is a project to create a set of leading, fully open-source models. Today, we are excited to announce the completion of the first step of this project: the reproduction of the LLaMA training dataset of over 1.2 trillion tokens. The most capable foundation models today are closed behind commercial APIs, which limits research, customization, and their use with sensitive data. Fully open-source models hold the promise of removing these limitations, if the open community can close the quality gap between open and closed models. Recently, there has been much progress along this front. In many ways, AI is having its Linux moment. Stable Diffusion showed that open-source can not only rival the quality of commercial offerings like DALL-E but can also lead to incredible creativity from broad participation by communities.

Baichuan-13B is an open source and commercially available large-scale language model containing 13 billion parameters developed by Baichuan Intelligent following Baichuan -7B . It has achieved the best results of the same size on authoritative Chinese and English benchmarks. This release contains two versions of pre-training ( Baichuan-13B-Base ) and alignment ( Baichuan-13B-Chat ). Larger size, more data : Baichuan-13B further expands the number of parameters to 13 billion on the basis of Baichuan -7B , and trains 1.4 trillion tokens on high-quality corpus, which is 40% more than LLaMA-13B. It is currently open source The model with the largest amount of training data in the 13B size. Support Chinese and English bilingual, use ALiBi position code, context window length is 4096.

Language Understanding (LUIS): A machine learning-based service to build natural language into apps, bots, and IoT devices. Quickly create enterprise-ready, custom models that continuously improve. Add natural language to your apps. Designed to identify valuable information in conversations, LUIS interprets user goals (intents) and distills valuable information from sentences (entities), for a high quality, nuanced language model. LUIS integrates seamlessly with the Azure Bot Service, making it easy to create a sophisticated bot. Powerful developer tools are combined with customizable pre-built apps and entity dictionaries, such as Calendar, Music, and Devices, so you can build and deploy a solution more quickly. Dictionaries are mined from the collective knowledge of the web and supply billions of entries, helping your model to correctly identify valuable information from user conversations. Active learning is used to continuously improve the quality of the models.

Large language models, which are often trained for hundreds of thousands of compute days, have shown remarkable capabilities for zero- and few-shot learning. Given their computational cost, these models are difficult to replicate without significant capital. For the few that are available through APIs, no access is granted to the full model weights, making them difficult to study. We present Open Pre-trained Transformers (OPT), a suite of decoder-only pre-trained transformers ranging from 125M to 175B parameters, which we aim to fully and responsibly share with interested researchers. We show that OPT-175B is comparable to GPT-3, while requiring only 1/7th the carbon footprint to develop. We are also releasing our logbook detailing the infrastructure challenges we faced, along with code for experimenting with all of the released models.

GPT-J is a cutting-edge language model created by the research organization EleutherAI. In terms of performance, GPT-J exhibits a level of proficiency comparable to that of OpenAI's renowned GPT-3 model in a range of zero-shot tasks. Notably, GPT-J has demonstrated the ability to surpass GPT-3 in tasks related to generating code. The latest iteration of this language model, known as GPT-J-6B, is built upon a linguistic dataset referred to as The Pile. This dataset, which is publicly available, encompasses a substantial volume of 825 gibibytes of language data, organized into 22 distinct subsets. While GPT-J shares certain capabilities with ChatGPT, it is important to note that GPT-J is not designed to operate as a chatbot; rather, its primary function is to predict text. In a significant development in March 2023, Databricks introduced Dolly, a model that follows instructions and is licensed under Apache.

Significant advancements in the field of natural language processing, understanding, and generation have been achieved through the expansion of large language models. This study introduces a system which utilizes Ascend 910 AI processors and the MindSpore framework to train a language model with over a trillion parameters, specifically 1.085T, named PanGu-{\Sigma}. This model, which builds upon the foundation laid by PanGu-{\alpha}, takes the traditionally dense Transformer model and transforms it into a sparse one using a concept known as Random Routed Experts (RRE). The model was efficiently trained on a dataset of 329 billion tokens using a technique called Expert Computation and Storage Separation (ECSS), leading to a 6.3-fold increase in training throughput via heterogeneous computing. Experimentation indicates that PanGu-{\Sigma} sets a new standard in zero-shot learning for various downstream Chinese NLP tasks.

GPT-4 is a large multimodal model (accepting text or image inputs and outputting text) that can solve difficult problems with greater accuracy than any of our previous models, thanks to its broader general knowledge and advanced reasoning capabilities. GPT-4 is available in the OpenAI API to paying customers. Like gpt-3.5-turbo, GPT-4 is optimized for chat but works well for traditional completions tasks using the Chat Completions API. GPT-4 is the latest GPT-4 model with improved instruction following, JSON mode, reproducible outputs, parallel function calling, and more. Returns a maximum of 4,096 output tokens. This preview model is not yet suited for production traffic.

OpenLLaMA is a permissively licensed open source reproduction of Meta AI’s LLaMA 7B trained on the RedPajama dataset. Our model weights can serve as the drop in replacement of LLaMA 7B in existing implementations. We also provide a smaller 3B variant of LLaMA model.

NVIDIA NeMo Megatron is an end-to-end framework for training and deploying LLMs with billions and trillions of parameters. NVIDIA NeMo Megatron, part of the NVIDIA AI platform, offers an easy, efficient, and cost-effective containerized framework to build and deploy LLMs. Designed for enterprise application development, it builds upon the most advanced technologies from NVIDIA research and provides an end-to-end workflow for automated distributed data processing, training large-scale customized GPT-3, T5, and multilingual T5 (mT5) models, and deploying models for inference at scale. Harnessing the power of LLMs is made easy through validated and converged recipes with predefined configurations for training and inference. Customizing models is simplified by the hyperparameter tool, which automatically searches for the best hyperparameter configurations and performance for training and inference on any given distributed GPU cluster configuration.

As a tribute to Cleopatra, whose glorious destiny ended in tragic snake circumstances, we are proud to release Codestral Mamba, a Mamba2 language model specialized in code generation, available under an Apache 2.0 license. Codestral Mamba is another step in our effort to study and provide new architectures. It is available for free use, modification, and distribution, and we hope it will open new perspectives in architecture research. Mamba models offer the advantage of linear time inference and the theoretical ability to model sequences of infinite length. It allows users to engage with the model extensively with quick responses, irrespective of the input length. This efficiency is especially relevant for code productivity use cases, this is why we trained this model with advanced code and reasoning capabilities, enabling it to perform on par with SOTA transformer-based models.

PaLM API is an easy and safe way to build on top of our best language models. Today, we’re making an efficient model available, in terms of size and capabilities, and we’ll add other sizes soon. The API also comes with an intuitive tool called MakerSuite, which lets you quickly prototype ideas and, over time, will have features for prompt engineering, synthetic data generation and custom-model tuning — all supported by robust safety tools. Select developers can access the PaLM API and MakerSuite in Private Preview today, and stay tuned for our waitlist soon.

The complete LLM platform with a superior developer experience and robust infrastructure. Build, deploy, and manage hyper-personalized, streamlined, and trusted generative AI apps. Langbase is an open source OpenAI alternative, a new inference engine & AI tool for any LLM. The most "developer-friendly" LLM platform to ship hyper-personalized AI apps in seconds.

A family of state-of-the-art, light-open models created from the same research and technology that were used to create Gemini models. These models incorporate comprehensive security measures and help ensure responsible and reliable AI solutions through selected data sets and rigorous adjustments. Gemma models achieve exceptional comparative results in their 2B, 7B, 9B, and 27B sizes, even outperforming some larger open models. With Keras 3.0, enjoy seamless compatibility with JAX, TensorFlow, and PyTorch, allowing you to effortlessly choose and change frameworks based on task. Redesigned to deliver outstanding performance and unmatched efficiency, Gemma 2 is optimized for incredibly fast inference on various hardware. The Gemma family of models offers different models that are optimized for specific use cases and adapt to your needs. Gemma models are large text-to-text lightweight language models with a decoder, trained in a huge set of text data, code, and mathematical content.

Falcon-7B is a 7B parameters causal decoder-only model built by TII and trained on 1,500B tokens of RefinedWeb enhanced with curated corpora. It is made available under the Apache 2.0 license. Why use Falcon-7B? It outperforms comparable open-source models (e.g., MPT-7B, StableLM, RedPajama etc.), thanks to being trained on 1,500B tokens of RefinedWeb enhanced with curated corpora. See the OpenLLM Leaderboard. It features an architecture optimized for inference, with FlashAttention and multiquery. It is made available under a permissive Apache 2.0 license allowing for commercial use, without any royalties or restrictions.