Compare Models
-
Microsoft
Azure OpenAI Service
OTHERMicrosoft’s Azure OpenAI Service allows you to take advantage of 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. GPT-4 is available in preview in the Azure OpenAI Service and the billing for GPT-4 8K and 32K instances per 1/K tokens and can be found under those models on the tokes compare site. To note, Microsoft’s Azure OpenAI Service customers can access GPT-3.5, ChatGPT, and DALL·E too. -
BigScience
BLOOM
FREEBigScience Large Open-science Open-access Multilingual Language Model (BLOOM) is a transformer-based LLM. Over 1,000 AI researchers created it to provide a free large language model for everyone who wants to try and it is a multilingual LLM. BLOOM is an autoregressive Large Language Model (LLM), trained to continue text from a prompt on vast amounts of text data using industrial-scale computational resources. It can output coherent text in 46 languages and 13 programming languages. It is free, and everybody who wants to can try it out. To interact with the API, you’ll need to request a token. This is done with a post request to the server. Tokens are only valid for two weeks. After which, a new one must be generated. Trained on around 176B parameters, it is considered an alternative to OpenAI models. There is a downloadable model, and a hosted API is available. -
BloombergGPT
BloombergGPT
OTHERBloombergGPT represents the first step in developing and applying LLM and generative AI technology for the financial industry. Bloomberg GPT has been trained on enormous amounts of financial data and is purpose-built for finance. The mixed dataset training leads to a model that outperforms existing LLMs on financial tasks by significant margins without sacrificing performance on general LLM benchmarks. Bloomberg GPT can perform a range of NLP tasks such as sentiment analysis, named entity recognition, news classification, and even writing headlines. With Bloomberg GPT, traders and analysts can perform financial analysis and insights more quickly and efficiently, saving valuable time that can be used for other critical tasks. To use Bloomberg GPT, you need access to Bloomberg’s terminal software (a platform investors and financial professionals use to access real-time market data, breaking news, financial research, and advanced analytics). Bloomberg also offers a variety of other subscription options, including subscriptions for financial institutions, universities, and governments. The price of a Bloomberg terminal varies depending on the type of subscription and the number of users. -
ChatGLM
ChatGLM-6B
FREEResearchers at the Tsinghua University in China have worked on developing the ChatGLM series of models that have comparable performance to other models such as GPT-3 and BLOOM. ChatGLM-6B is an open bilingual language model (trained on Chinese and English). It is based on General Language Model (GLM) framework, with 6.2B parameters. With the quantization technique, users can deploy locally on consumer-grade graphics cards (only 6GB of GPU memory is required at the INT4 quantization level). The following models are available: ChatGLM-130B (an open source LLM), ChatGLM-100B (not open source but available through invite-only access), and ChatGLM-6 (a lightweight open source alternative). ChatGLM LLMs are available with a Apache-2.0 license that allows commercial use. We have included the link to the Hugging Face page where you can try the ChatGLM-6B Chatbot for free. -
EleutherAI
GPT-J
FREEEleutherAI is a leading non-profit research institute focused on large-scale artificial intelligence research. EleutherAI has trained and released several LLMs and the codebases used to train them. GPT-J can be used for code generation, making a chat bot, story writing, language translation and searching. GPT-J learns an inner representation of the English language that can be used to extract features useful for downstream tasks. The model is best at what it was pretrained for, which is generating text from a prompt. EleutherAI has a web page where you can test to see how the GPT-J works, or you can run GPT-J on google colab, or use the Hugging Face Transformers library. -
EleutherAI
GPT-NeoX-20B
FREEEleutherAI has trained and released several LLMs and the codebases used to train them. EleutherAI is a leading non-profit research institute focused on large-scale artificial intelligence research. GPT-NeoX-20B is a 20 billion parameter autoregressive language model trained on the Pile using the GPT-NeoX library. Its architecture intentionally resembles that of GPT-3, and is almost identical to that of GPT-J- 6B. Its training dataset contains a multitude of English-language texts, reflecting the general-purpose nature of this model. It is a transformer-based language model and is English-language only, and thus cannot be used for translation or generating text in other languages. It is freely and openly available to the public through a permissive license. -
NVIDIA
LaunchPad
FREENVIDIA LaunchPad provides free access to enterprise NVIDIA hardware and software through an internet browser. NVIDIA customers can experience the power of AI with end-to-end solutions through guided hands-on labs or use NVIDIA-Certified Systems as a sandbox, but you need to fill out an Application Form and wait for approval. Sample labs include training and deploying a support chatbot, deploying an end-to-end AI workload, configuring and deploying a language model on the hardware accelerator, and deploying a fraud detection model.*FREE via Application Form -
Meta AI
Llama
FREEMeta has created Llama (Large Language Model Meta AI), its 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. Meta is making Llama available at several sizes (7B, 13B, 33B, and 65B parameters) and they also share a Llama model card that details how we built the model in keeping with our approach to responsible AI practices. -
Meta AI
Llama 2
FREEMeta has released Llama 2. It has an open license, which allows commercial use for businesses. Llama 2 will be available for use in the Hugging Face Transformers library from today (you will need to sign Meta’s Llama 2 Community License Agreement – https://ai.meta.com/resources/models-and-libraries/llama-downloads/, via MSFT Azure cloud computing service, and through Amazon SageMaker JumpStart).Llama 2 is an auto-regressive language model that uses an optimized transformer architecture. Llama 2 is intended for commercial and research use in English. It comes in a range of parameter sizes—7 billion, 13 billion, and 70 billion—as well as pre-trained and fine-tuned variations. According to Meta, the tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align to human preferences for helpfulness and safety. Llama 2 was pre-trained on 2 trillion tokens of data from publicly available sources. The tuned models are intended for assistant-like chat, whereas pre-trained models can be adapted for a variety of natural language generation tasks.Link to the live demo of Llama2 70B Chatbot -https://huggingface.co/spaces/ysharma/Explore_llamav2_with_TGI -
Microsoft, NVIDIA
MT-NLG
OTHERMT-NLG (Megatron-Turing Natural Language Generation) uses the architecture of the transformer-based Megatron to generate coherent and contextually relevant text for a range of tasks, including completion prediction, reading comprehension, commonsense reasoning, natural language inferences, and word sense disambiguation. MT-NLG is the successor to Microsoft Turing NLG 17B and NVIDIA Megatron-LM 8.3B. The MT-NLG model is three times larger than GPT-3 (530B vs 175B). Following the original Megatron work, NVIDIA and Microsoft trained the model on over 4,000 GPUs. NVIDIA has announced an Early Access program for its managed API service to the MT-NLG model for organizations and researchers. -
NVIDIA
NeMo
FREENVIDIA NeMo, part of the NVIDIA AI platform, is an end-to-end, cloud-native enterprise framework to help build, customize, and deploy generative AI models. NeMo makes generative AI model development easy, cost-effective and fast for enterprises. NeMo has separate collections for Automatic Speech Recognition (ASR), Natural Language Processing (NLP), and Text-to-Speech (TTS) models. Each collection consists of prebuilt modules that include everything needed to train on your data. NeMo framework supports both language and image generative AI models. Currently, the workflow for language is in open beta, and the workflow for images is in early access. You must be a member of the NVIDIA Developer Program and logged in with your organization’s email address to access it. It is licensed under the Apache License 2.0, which is a permissive open source license that allows for commercial use. -
Microsoft
VALL-E
OTHERVALL-E is a LLM for text to speech synthesis (TTS) developed by Microsoft (technically it is a neural codec language model). Its creators state that VALL-E could be used for high-quality text-to-speech applications, speech editing where a recording of a person could be edited and changed from a text transcript (making them say something they originally didn’t), and audio content creation when combined with other generative AI models. Studies indicate that VALL-E notably surpasses the leading zero-shot TTS system regarding speech authenticity and resemblance to the speaker. Furthermore, it has been observed that VALL-E is capable of retaining the emotional expression and ambient acoustics of the speaker within the synthesized output. Unfortunately, VALL-E is not available for any form of public consumption at this time. At the time of writing, VALL-E is a research project, and there is no customer onboarding queue or waitlist (but you can apply to be part of the first testers group).
1
2
