Alternatives to Stable LM

Dolly is an inexpensive LLM that demonstrates a surprising amount of the capabilities of 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 uses an open source model with 6 billion parameters from EleutherAI, which is modified to include new capabilities like brainstorming and text creation that were not present in the original.

The training of state-of-the art language models is extremely difficult. They require large compute budgets, complex distributed computing techniques and deep ML knowledge. Few organizations are able to train large language models from scratch. The number of organizations that do not open source their results is increasing, even though they have the expertise and resources to do so. We at Cerebras believe in open access to the latest models. Cerebras is proud to announce that Cerebras GPT, a family GPT models with 111 million to thirteen billion parameters, has been released to the open-source community. These models are trained using the Chinchilla Formula and provide the highest accuracy within a given computing budget. Cerebras GPT has faster training times and lower training costs. It also consumes less power than any other publicly available model.

Falcon-7B is a 7B parameter causal decoder model, built by TII. It was trained on 1,500B tokens from RefinedWeb enhanced by curated corpora. It is available under the Apache 2.0 licence. Why use Falcon-7B Falcon-7B? It outperforms similar open-source models, such as MPT-7B StableLM RedPajama, etc. It is a result of being trained using 1,500B tokens from RefinedWeb enhanced by curated corpora. OpenLLM Leaderboard. It has an architecture optimized for inference with FlashAttention, multiquery and multiquery. It is available under an Apache 2.0 license that allows commercial use without any restrictions or royalties.

Falcon-40B is a 40B parameter causal decoder model, built by TII. It was trained on 1,000B tokens from RefinedWeb enhanced by curated corpora. It is available under the Apache 2.0 licence. Why use Falcon-40B Falcon-40B is the best open source model available. Falcon-40B outperforms LLaMA, StableLM, RedPajama, MPT, etc. OpenLLM Leaderboard. It has an architecture optimized for inference with FlashAttention, multiquery and multiquery. It is available under an Apache 2.0 license that allows commercial use without any restrictions or royalties. This is a raw model that should be finetuned to fit most uses. If you're looking for a model that can take generic instructions in chat format, we suggest Falcon-40B Instruct.

Megatron-Turing Natural Language Generation Model (MT-NLG) is the largest and most powerful monolithic English language model. It has 530 billion parameters. This 105-layer transformer-based MTNLG improves on the previous state-of-the art models in zero, one, and few shot settings. It is unmatched in its accuracy across a wide range of natural language tasks, including Completion prediction and Reading comprehension. NVIDIA has announced an Early Access Program for its managed API service in MT-NLG Mode. This program will allow customers to experiment with, employ and apply a large language models on downstream language tasks.

Introducing MPT-7B - the latest addition to our MosaicML Foundation Series. MPT-7B, a transformer that is trained from scratch using 1T tokens of code and text, is the latest entry in our MosaicML Foundation Series. It is open-source, available for commercial purposes, and has the same quality as LLaMA-7B. MPT-7B trained on the MosaicML Platform in 9.5 days, with zero human interaction at a cost $200k. You can now train, fine-tune and deploy your private MPT models. You can either start from one of our checkpoints, or you can start 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!

GPT-J, a cutting edge language model developed by EleutherAI, is a leading-edge language model. GPT-J's performance is comparable to OpenAI's GPT-3 model on a variety of zero-shot tasks. GPT-J, in particular, has shown that it can surpass GPT-3 at tasks relating to code generation. The latest version of this language model is GPT-J-6B and is built on a linguistic data set called The Pile. This dataset is publically available and contains 825 gibibytes worth of language data organized into 22 subsets. GPT-J has some similarities with ChatGPT. However, GPTJ is not intended to be a chatbot. Its primary function is to predict texts. Databricks made a major development in March 2023 when they introduced Dolly, an Apache-licensed model that follows instructions.

PaLM API allows you to easily and safely build on top our best language models. We are currently making an efficient model, both in terms of size, and capabilities, available today. We will soon add more sizes. MakerSuite is an intuitive tool that allows you to quickly prototype ideas. Over time, it will include features for prompt engineering and synthetic data generation. It also supports custom-model tuning. All of this is supported by robust safety tools. Only a few developers have access to the PaLM API and MakerSuite in private preview today. Stay tuned for our waitlist.

Introducing DeepSeek LLM - an advanced language model with 67 billion parameters. It was trained from scratch using a massive dataset of 2 trillion tokens, both in English and Chinese. To encourage research, we made DeepSeek LLM 67B Base and DeepSeek LLM 67B Chat available as open source to the research community.

PygmalionAI, a community of open-source projects based upon EleutherAI’s GPT-J 6B models and Meta’s LLaMA model, was founded in 2009. Pygmalion AI is designed for roleplaying and chatting. The 7B variant of the Pygmalion AI is currently actively supported. It is based on Meta AI’s LLaMA AI model. Pygmalion's chat capabilities are superior to larger language models that require much more resources. Our curated datasets of high-quality data on roleplaying ensure that your bot is the best RP partner. The model weights as well as the code used to train the model are both open-source. You can modify/re-distribute them for any purpose you like. Pygmalion and other language models run on GPUs because they require fast memory and massive processing to produce coherent text at a reasonable speed.

The next generation of the large language model. This release includes modelweights and starting code to pretrained and fine tuned Llama languages models, ranging from 7B-70B parameters. Llama 1 models have a context length of 2 trillion tokens. Llama 2 models have a context length double that of Llama 1. The fine-tuned Llama 2 models have been trained using over 1,000,000 human annotations. Llama 2, a new open-source language model, outperforms many other open-source language models in external benchmarks. These include tests of reasoning, coding and proficiency, as well as knowledge tests. Llama 2 has been pre-trained using publicly available online data sources. Llama-2 chat, a fine-tuned version of the model, is based on publicly available instruction datasets, and more than 1 million human annotations. We have a wide range of supporters in the world who are committed to our open approach for today's AI. These companies have provided early feedback and have expressed excitement to build with Llama 2

Qwen LLM is a family of large-language models (LLMs), developed by Damo Academy, an Alibaba Cloud subsidiary. These models are trained using a large dataset of text and codes, allowing them the ability to understand and generate text that is human-like, translate languages, create different types of creative content and answer your question in an informative manner. Here are some of the key features of Qwen LLMs. Variety of sizes: Qwen's series includes sizes ranging from 1.8 billion parameters to 72 billion, offering options that meet different needs and performance levels. Open source: Certain versions of Qwen have open-source code, which is available to anyone for use and modification. Qwen is multilingual and can translate multiple languages including English, Chinese and Japanese. Qwen models are capable of a wide range of tasks, including text summarization and code generation, as well as generation and translation.

OpenELM is a family of open-source language models developed by Apple. It uses a layering strategy to allocate parameters efficiently within each layer of a transformer model. This leads to improved accuracy compared to other open language models. OpenELM was trained using publicly available datasets, and it achieves the best performance for its size.

Baichuan-13B, a large-scale language model with 13 billion parameters that is open source and available commercially by Baichuan Intelligent, was developed following Baichuan -7B. It has the best results for a language model of the same size in authoritative Chinese and English benchmarks. This release includes two versions of pretraining (Baichuan-13B Base) and alignment (Baichuan-13B Chat). Baichuan-13B has more data and a larger size. It expands the number parameters to 13 billion based on Baichuan -7B, and trains 1.4 trillion coins on high-quality corpus. This is 40% more than LLaMA-13B. It is open source and currently the model with the most training data in 13B size. Support Chinese and English bi-lingual, use ALiBi code, context window is 4096.

The expansion of large language model has led to significant advancements in natural language processing, understanding and generation. This study introduces a new system that uses Ascend 910 AI processing units and the MindSpore framework in order to train a language with over one trillion parameters, 1.085T specifically, called PanGu-Sigma. This model, which builds on the foundation laid down by PanGu-alpha transforms the traditional dense Transformer model into a sparse model using a concept called Random Routed Experts. The model was trained efficiently on a dataset consisting of 329 billion tokens, using a technique known as Expert Computation and Storage Separation. This led to a 6.3 fold increase in training performance via heterogeneous computer. The experiments show that PanGu-Sigma is a new standard for zero-shot learning in various downstream Chinese NLP tasks.

GPT-4 and other foundation models have accelerated AI's development. The most powerful models, however, are closed commercial models or partially open. RedPajama aims to create a set leading, open-source models. Today, we're excited to announce that the first phase of this project is complete: the reproduction of LLaMA's training dataset of more than 1.2 trillion tokens. The most capable foundations models are currently closed behind commercial APIs. This limits research, customization and their use with sensitive information. If the open community can bridge the quality gap between closed and open models, fully open-source models could be the answer to these limitations. Recent progress has been made in this area. AI is in many ways having its Linux moment. Stable Diffusion demonstrated that open-source software can not only compete with commercial offerings such as DALL-E, but also lead to incredible creative results from community participation.

Aya is an open-source, state-of-the art, massively multilingual large language research model (LLM), which covers 101 different languages. This is more than twice the number of languages that are covered by open-source models. Aya helps researchers unlock LLMs' powerful potential for dozens of cultures and languages that are largely ignored by the most advanced models available today. We open-source both the Aya Model, as well as the most comprehensive multilingual instruction dataset with 513 million words covering 114 different languages. This data collection contains rare annotations by native and fluent speakers from around the world. This ensures that AI technology is able to effectively serve a global audience who have had limited access up until now.

ChatGLM-6B, a Chinese-English bilingual dialogue model based on General Language Model architecture (GLM), has 6.2 billion parameters. Users can deploy model quantization locally on consumer-grade graphic cards (only 6GB video memory required at INT4 quantization levels). ChatGLM-6B is based on technology similar to ChatGPT and optimized for Chinese dialogue and Q&A. After approximately 1T identifiers for Chinese and English bilingual training and supplemented with supervision and fine-tuning as well as feedback self-help and human feedback reinforcement learning, ChatGLM-6B, with 6.2 billion parameters, has been able generate answers that are in line with human preference.

Qwen-7B, also known as Qwen-7B, is the 7B-parameter variant of the large language models series Qwen. Tongyi Qianwen, proposed by Alibaba Cloud. Qwen-7B, a Transformer-based language model, is pretrained using a large volume data, such as web texts, books, code, etc. Qwen-7B is also used to train Qwen-7B Chat, an AI assistant that uses large models and alignment techniques. The Qwen-7B features include: Pre-trained with high quality data. We have pretrained Qwen-7B using a large-scale, high-quality dataset that we constructed ourselves. The dataset contains over 2.2 trillion tokens. The dataset contains plain texts and codes and covers a wide range domains including general domain data as well as professional domain data. Strong performance. We outperform our competitors in a series benchmark datasets that evaluate natural language understanding, mathematics and coding. And more.

Production-ready AI models that are fast and accurate. High-availability inference API that leverages the most advanced NVIDIA GPUs. We have selected the most popular open-source natural language processing models (NLP) and deployed them for the community. You can fine-tune your models (including GPT-J) or upload your custom models. Then, deploy them to production. Upload your AI models, including GPT-J, to your dashboard and immediately use them in production.

Stability AI, in collaboration with its CarperAI Lab, announces Stable Beluga 1 (formerly codenamed FreeWilly) and its successor Stable Beluga 2 - two powerful, new Large Language Models. Both models show exceptional reasoning abilities across a variety of benchmarks. Stable Beluga 1 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. Stable Beluga 2 uses the LLaMA 270B foundation model for industry-leading performance.

Phi-2 is a 2.7-billion-parameter language-model that shows outstanding reasoning and language-understanding capabilities. It represents the state-of-the art performance among language-base models with less than thirteen billion parameters. Phi-2 can match or even outperform models 25x larger on complex benchmarks, thanks to innovations in model scaling. Phi-2's compact size makes it an ideal playground for researchers. It can be used for exploring mechanistic interpretationability, safety improvements or fine-tuning experiments on a variety tasks. We have included Phi-2 in the Azure AI Studio catalog to encourage research and development of language models.

NVIDIA Nemotron, a family open-source models created by NVIDIA is designed to generate synthetic language data for commercial applications. The Nemotron-4 model 340B is an important release by NVIDIA. It offers developers a powerful tool for generating high-quality data, and filtering it based upon various attributes, using a reward system.

We are developing large language models that are efficient for India's diverse cultural diversity and enabling GenAI applications with bespoke enterprise models. We are building a platform for enterprise-grade apps that allows you to develop and evaluate them. We believe that open-source can accelerate AI innovation. We will be contributing open-source datasets and models, and leading efforts for large data curation projects in the public-good space. We are a dynamic team of AI experts, combining expertise in research, product design, engineering and business operations. Our diverse backgrounds are united by a commitment to excellence in science, and creating societal impact. We create an environment in which tackling complex tech problems is not only a job but a passion.

Instruction-following models such as GPT-3.5 (text-DaVinci-003), ChatGPT, Claude, and Bing Chat have become increasingly powerful. These models are now used by many users, and some even 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. It is vital that the academic community engages in order to make maximum progress towards addressing these pressing issues. 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.

The TinyLlama Project aims to pretrain an 1.1B Llama on 3 trillion tokens. We can achieve this in "just" 90 day using 16 A100-40G graphics cards with some optimization. We used the exact same architecture and tokenizers as Llama 2 TinyLlama is compatible with many open-source Llama projects. TinyLlama has only 1.1B of parameters. This compactness allows TinyLlama to be used for a variety of applications that require a small computation and memory footprint.

Jurassic-1 comes in two sizes. The Jumbo version is the most advanced language model, with 178B parameters. It was released to developers for general use. AI21 Studio, currently in open beta allows anyone to sign up for the service and immediately begin querying Jurassic-1 with our API and interactive website environment. AI21 Labs' mission is to fundamentally change the way humans read and compose by introducing machines as partners in thought. We can only achieve this if we work together. Since the Mesozoic Era, or 2017, we have been researching language models. Jurassic-1 is based on this research and is the first generation we are making available to wide use.

OLMo 2 is an open language model family developed by the Allen Institute for AI. It provides researchers and developers with open-source code and reproducible training recipes. These models can be trained with up to 5 trillion tokens, and they are competitive against other open-weight models such as Llama 3.0 on English academic benchmarks. OLMo 2 focuses on training stability by implementing techniques that prevent loss spikes in long training runs. It also uses staged training interventions to address capability deficits during late pretraining. The models incorporate the latest post-training methods from AI2's Tulu 3 resulting in OLMo 2-Instruct. The Open Language Modeling Evaluation System, or OLMES, was created to guide improvements throughout the development stages. It consists of 20 evaluation benchmarks assessing key capabilities.

Smaug 72B is an open-source large-language model (LLM), which is known for its key features. High Performance: It is currently ranked first on the Hugging face Open LLM leaderboard. This model has surpassed models such as GPT-3.5 across a range of benchmarks. This means that it excels in tasks such as understanding, responding to and generating text similar to human speech. Open Source: Smaug-72B, unlike many other advanced LLMs is available to anyone for free use and modification, fostering collaboration, innovation, and creativity in the AI community. Focus on Math and Reasoning: It excels at handling mathematical and reasoning tasks. This is attributed to the unique fine-tuning technologies developed by Abacus, the creators Smaug 72B. Based on Qwen 72B: This is a finely tuned version of another powerful LLM, called Qwen 72B, released by Alibaba. It further improves its capabilities. Smaug-72B is a significant advance in open-source AI.

There are now more versions of the open-source AI model that you can refine, distill and deploy anywhere. Choose from 1B or 3B, or build with Llama 3. Llama 3.2 consists of a collection large language models (LLMs), which are pre-trained and fine-tuned. They come in sizes 1B and 3B, which are multilingual text only. Sizes 11B and 90B accept both text and images as inputs and produce text. Our latest release allows you to create highly efficient and performant applications. Use our 1B and 3B models to develop on-device applications, such as a summary of a conversation from your phone, or calling on-device features like calendar. Use our 11B and 90B models to transform an existing image or get more information from a picture of your surroundings.

A model parallel autoregressive transformator implementation on GPUs based on the DeepSpeed Library. This repository contains EleutherAI’s library for training large language models on GPUs. Our current framework is based upon NVIDIA's Megatron Language Model, and has been enhanced with techniques from DeepSpeed, as well as some novel improvements. This repo is intended to be a central and accessible place for techniques to train large-scale autoregressive models and to accelerate research into large scale training.

Qwen2 is a large language model developed by Qwen Team, Alibaba Cloud. Qwen2 is an extensive series of large language model developed by the Qwen Team at Alibaba Cloud. It includes both base models and instruction-tuned versions, with parameters ranging from 0.5 to 72 billion. It also features dense models and a Mixture of Experts model. The Qwen2 Series is designed to surpass previous open-weight models including its predecessor Qwen1.5 and to compete with proprietary model across a wide spectrum of benchmarks, such as language understanding, generation and multilingual capabilities.

InstructGPT is an open source framework that trains language models to generate natural language instruction from visual input. It uses a generative, pre-trained transformer model (GPT) and the state of the art object detector Mask R-CNN to detect objects in images. Natural language sentences are then generated that describe the image. InstructGPT has been designed to be useful in all domains including robotics, gaming, and education. It can help robots navigate complex tasks using natural language instructions or it can help students learn by giving descriptive explanations of events or processes.

GPT-4 (Generative Pretrained Transformer 4) a large-scale, unsupervised language model that is yet to be released. GPT-4, which is the successor of GPT-3, is part of the GPT -n series of natural-language processing models. It was trained using a dataset of 45TB text to produce text generation and understanding abilities that are human-like. GPT-4 is not dependent on additional training data, unlike other NLP models. It can generate text and answer questions using its own context. GPT-4 has been demonstrated to be capable of performing a wide range of tasks without any task-specific training data, such as translation, summarization and sentiment analysis.

IBM® Granite™ is an AI family that was designed from scratch for business applications. It helps to ensure trust and scalability of AI-driven apps. Granite models are open source and available today. We want to make AI accessible to as many developers as we can. We have made the core Granite Code, Time Series models, Language and GeoSpatial available on Hugging Face, under a permissive Apache 2.0 licence that allows for broad commercial use. Granite models are all trained using carefully curated data. The data used to train them is transparent at a level that is unmatched in the industry. We have also made the tools that we use available to ensure that the data is of high quality and meets the standards required by enterprise-grade applications.

Hermes 3 contains advanced long-term context retention and multi-turn conversation capabilities, complex roleplaying and internal monologue abilities, and enhanced agentic function-calling. Hermes 3 has advanced long-term contextual retention, multi-turn conversation capabilities, complex roleplaying, internal monologue, and enhanced agentic functions-calling. Our training data encourages the model in a very aggressive way to follow the system prompts and instructions exactly and in a highly adaptive manner. Hermes 3 was developed by fine-tuning Llama 3.0 8B, 70B and 405B and training with a dataset primarily containing synthetic responses. The model has a performance that is comparable to Llama 3.1, but with deeper reasoning and creative abilities. Hermes 3 is an instruct and tool-use model series with strong reasoning and creativity abilities.

Teuken-7B, a multilingual open source language model, was developed under the OpenGPT-X project. It is specifically designed to accommodate Europe's diverse linguistic landscape. It was trained on a dataset that included over 50% non-English text, covering all 24 official European Union languages, to ensure robust performance. Teuken-7B's custom multilingual tokenizer is a key innovation. It has been optimized for European languages and enhances training efficiency. The model comes in two versions: Teuken-7B Base, a pre-trained foundational model, and Teuken-7B Instruct, a model that has been tuned to better follow user prompts. Hugging Face makes both versions available, promoting transparency and cooperation within the AI community. The development of Teuken-7B demonstrates a commitment to create AI models that reflect Europe’s diversity.

OpenGPT is a German initiative that focuses on developing large AI languages models tailored to European requirements, with an emphasis on versatility, trustworthiness and multilingual capabilities. It also emphasizes open-source accessibility. The project brings together partners to cover the whole generative AI value-chain, from scalable GPU-based infrastructure to data for training large language model to model design, practical applications, and prototypes and proofs-of concept. OpenGPT-X aims at advancing cutting-edge research, with a focus on business applications. This will accelerate the adoption of generative AI within the German economy. The project also stresses responsible AI development to ensure that the models are reliable and aligned with European values and laws. The project provides resources, such as the LLM Workbook and a three part reference guide with examples and resources to help users better understand the key features and characteristics of large AI language model.

GPT4All provides an ecosystem for training and deploying large language models, which run locally on consumer CPUs. The goal is to be the best assistant-style language models that anyone or any enterprise can freely use and distribute. A GPT4All is a 3GB to 8GB file you can download and plug in the GPT4All ecosystem software. Nomic AI maintains and supports this software ecosystem in order to enforce quality and safety, and to enable any person or company to easily train and deploy large language models on the edge. Data is a key ingredient in building a powerful and general-purpose large-language model. The GPT4All Community has created the GPT4All Open Source Data Lake as a staging area for contributing instruction and assistance tuning data for future GPT4All Model Trains.

StarCoderBase and StarCoder are Large Language Models (Code LLMs), trained on permissively-licensed data from GitHub. This includes data from 80+ programming language, Git commits and issues, Jupyter Notebooks, and Git commits. We trained a 15B-parameter model for 1 trillion tokens, similar to LLaMA. We refined the StarCoderBase for 35B Python tokens. The result is a new model we call StarCoder. StarCoderBase is a model that outperforms other open Code LLMs in popular programming benchmarks. It also matches or exceeds closed models like code-cushman001 from OpenAI, the original Codex model which powered early versions GitHub Copilot. StarCoder models are able to process more input with a context length over 8,000 tokens than any other open LLM. This allows for a variety of interesting applications. By prompting the StarCoder model with a series dialogues, we allowed them to act like a technical assistant.

Our new flagship model, the Small Language Model (SLM), has all the power and performance that you would expect from a leading LLM. Excels at generalized tasks, instruction-following, and human preferences. The best 70B model available. SuperNova is a generalized task-based AI that can be used for any generalized task. It's similar to Open AI's GPT4o and Claude Sonnet 3.5. SuperNova is trained with the most advanced optimization & learning techniques to generate highly accurate responses. It is the most flexible, cost-effective, and secure language model available. Customers can save up to 95% in total deployment costs when compared with traditional closed-source models. SuperNova can be used to integrate AI in apps and products, as well as for general chat and a variety of other uses. Update your models regularly with the latest open source tech to ensure you're not locked into a single solution. Protect your data using industry-leading privacy features.

The ability of large language models to learn in zero- and few shots, despite being trained for hundreds of thousands or even millions of days, has been remarkable. These models are expensive to replicate, due to their high computational cost. The few models that are available via APIs do not allow access to the full weights of the model, making it difficult to study. Open Pre-trained Transformers is a suite decoder-only pre-trained transforms with parameters ranging from 175B to 125M. We aim to share this fully and responsibly with interested researchers. We show that OPT-175B has a carbon footprint of 1/7th that of GPT-3. We will also release our logbook, which details the infrastructure challenges we encountered, as well as code for experimenting on all of the released model.

Pre-trained models of language have achieved state-of the-art results for various Natural Language Processing (NLP). GPT-3 has demonstrated that scaling up language models pre-trained can further exploit their immense potential. Recently, a framework named ERNIE 3.0 for pre-training large knowledge enhanced models was proposed. This framework trained a model that had 10 billion parameters. ERNIE 3.0 performed better than the current state-of-the art models on a variety of NLP tasks. In order to explore the performance of scaling up ERNIE 3.0, we train a hundred-billion-parameter model called ERNIE 3.0 Titan with up to 260 billion parameters on the PaddlePaddle platform. We also design a self supervised adversarial and a controllable model language loss to make ERNIE Titan generate credible texts.

CodeGen is a model for program synthesis that is open-source. Trained on TPU v4. OpenAI Codex is competitive with TPU-v4.

We are proud to introduce Codestral, the first code model we have ever created. Codestral is a generative AI model that is open-weight and specifically designed for code generation. It allows developers to interact and write code using a shared API endpoint for instructions and completion. It can be used for advanced AI applications by software developers as it is able to master both code and English. Codestral has been trained on a large dataset of 80+ languages, including some of the most popular, such as Python and Java. It also includes C, C++ JavaScript, Bash, C, C++. It also performs well with more specific ones, such as Swift and Fortran. Codestral's broad language base allows it to assist developers in a variety of coding environments and projects.

LLaVA is a multimodal model that combines a Vicuna language model with a vision encoder to facilitate comprehensive visual-language understanding. LLaVA's chat capabilities are impressive, emulating multimodal functionality of models such as GPT-4. LLaVA 1.5 has achieved the best performance in 11 benchmarks using publicly available data. It completed training on a single 8A100 node in about one day, beating methods that rely upon billion-scale datasets. The development of LLaVA involved the creation of a multimodal instruction-following dataset, generated using language-only GPT-4. This dataset comprises 158,000 unique language-image instruction-following samples, including conversations, detailed descriptions, and complex reasoning tasks. This data has been crucial in training LLaVA for a wide range of visual and linguistic tasks.

Samsung Gauss, a new AI-model developed by Samsung Electronics, is a powerful AI tool. It is a large-language model (LLM) which has been trained using a massive dataset. Samsung Gauss can generate text, translate different languages, create creative content and answer questions in a helpful way. Samsung Gauss, which is still in development, has already mastered many tasks, including Follow instructions and complete requests with care. Answering questions in an informative and comprehensive way, even when they are open-ended, challenging or strange. Creating different creative text formats such as poems, code, musical pieces, emails, letters, etc. Here are some examples to show what Samsung Gauss is capable of: Translation: Samsung Gauss is able to translate text between many languages, including English and German, as well as Spanish, Chinese, Japanese and Korean. Coding: Samsung Gauss can generate code.

OpenLLaMA, a permissively-licensed open source reproduction of Meta AI’s LLaMA 7B, is trained on the RedPajama data set. Our model weights are a drop-in replacement for LLaMA7B in existing implementations. We also offer a smaller 3B version of the LLaMA Model.

Alpa aims automate large-scale distributed training. Alpa was originally developed by people at UC Berkeley's Sky Lab. Alpa's advanced techniques were described in a paper published by OSDI'2022. Google is adding new members to the Alpa community. A language model is a probabilistic distribution of probability over a sequence of words. It uses all the words it has seen to predict the next word. It is useful in a variety AI applications, including the auto-completion of your email or chatbot service. You can find more information on the language model Wikipedia page. GPT-3 is a large language model with 175 billion parameters that uses deep learning to produce text that looks human-like. GPT-3 was described by many researchers and news articles as "one the most important and interesting AI systems ever created." GPT-3 is being used as a backbone for the latest NLP research.

Mistral Large 2 comes with a 128k window that supports dozens of different languages, including French, German and Spanish. It also supports Arabic, Hindi, Russian and Chinese. It also supports 80+ programming languages, including Python, Java and C++. Mistral Large 2 was designed with single-node applications in mind. Its size of 123 million parameters allows it to run fast on a single computer. Mistral Large 2 is released under the Mistral Research License which allows modification and usage for research and noncommercial purposes.