Compare Models
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Stanford University
Alpaca
FREEStanford University released an instruction-following language model called Alpaca, which was fine-tuned from Meta’s LLaMA 7B model. The Alpaca model was trained on 52K instruction-following demonstrations generated in the style of self-instruct using text-davinci-003. Alpaca aims to help the academic community engage with the models by providing an open source model that rivals OpenAI’s GPT-3.5 (text-davinci-003) models. To this end, Alpaca has been kept small and cheap (fine-tuning Alpaca took 3 hours on 8x A100s which is less than $100 of cost) to reproduce. All training data and techniques have been released. The Alpaca license explicitly prohibits commercial use, and the model can only be used for research/personal projects, and users need to follow LLaMA’s license agreement. -
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. -
Technology Innovation Institute
Falcon-40B
OTHERThe Technology Innovation Institute (TII), an Abu Dhabi government funded research institution, has introduced Falcon, a state-of-the-art autoregressive decoder-only language model series released under the Apache 2.0 license, which means it can be used for commerical and research uses.
The family includes Falcon-40B and Falcon-7B, trained on 1 trillion tokens, mainly (>80%) from the RefinedWeb datase. A special variant, Falcon-40B-Instruct, has been made available which may be more suitable for assistant-style tasks. Falcon-40B can support English, German, Spanish, French (and limited capabilities in Italian, Portuguese, Polish, Dutch, Romanian, Czech, Swedish). It can be used to generate creative text and solve complex problems, chatbots, virtual assistants, language translation, content generation, and sentiment analysis (and more).To use these models, PyTorch 2.0 is required. TII is now calling for proposals from users worldwide to submit their most creative ideas for Falcon 40B’s deployment – https://falconllm.tii.ae/call-for-proposal.php or you can pay to access it via Amazon SageMaker JumpStart.
A demo of Falcon-Chat is available on Hugging Face at https://huggingface.co/spaces/HuggingFaceH4/falcon-chat. -
Technology Innovation Institute
Falcon-7B
FREEThe Technology Innovation Institute (TII), an Abu Dhabi government funded research institution, has introduced Falcon, a state-of-the-art autoregressive decoder-only language model series released under the Apache 2.0 license, which means it can be used for commerical and research uses. Falcon-7B only needs ~15GB and therefore is accessible even on consumer hardware. The model can support English, German, Spanish, French (and limited capabilities in Italian, Portuguese, Polish, Dutch, Romanian, Czech, Swedish). It can be used to generate creative text and solve complex problems, chatbots, customer service operations, virtual assistants, language translation, content generation, and sentiment analysis.
This raw pretrained model should be finetuned for specific use cases. Falcon-7B-Instruct is also available at https://huggingface.co/tiiuae/falcon-7b-instruct.
If you are looking for a version better-suited model to take generic instructions in a chat format, we recommend Falcon-7B-Instruct rather than the base model. -
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 -
RedPajama
RedPajama-INCITE-7B-Instruct
FREEThe RedPajama project aims to create a set of leading open source models. RedPajama-INCITE-7B-Instruct was developed by Together and leaders from the open source AI community. RedPajama-INCITE-7B-Instruct model represents the top-performing open source entry on the HELM benchmarks, surpassing other cutting-edge open models like LLaMA-7B, Falcon-7B, and MPT-7B. The instruct-tuned model is designed for versatility and shines when tasked with few-shot performance.The Instruct, Chat, Base Model, and ten interim checkpoints are now available on HuggingFace, and all the RedPajama LLMs come with commercial licenses under Apache 2.0.Play with the RedPajama chat model version here – https://lnkd.in/g3npSEbg -
StableLM
StableLM-Base-Alpha -7B
FREEStability AI released a new open-source language model, StableLM. The Alpha version of the model is available in 3 billion and 7 billion parameters. StableLM is trained on a new experimental dataset built on The Pile, but three times larger with 1.5 trillion tokens of content. The richness of this dataset gives StableLM surprisingly high performance in conversational and coding tasks, despite its small size. The models are now available on GitHub and on Hugging Face, and developers can freely inspect, use, and adapt our StableLM base models for commercial or research purposes subject to the terms of the CC BY-SA-4.0 license.
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Yandex
YaLM
FREEYaLM 100B is a GPT-like neural network for generating and processing text. It can be used freely by developers and researchers from all over the world. It took 65 days to train the model on a cluster of 800 A100 graphics cards and 1.7 TB of online texts, books, and countless other sources in both English and Russian. Researchers and developers can use the corporate-size solution to solve the most complex problems associated with natural language processing.Training details and best practices on acceleration and stabilizations can be found on Medium (English) and Habr (Russian) articles. The model is published under the Apache 2.0 license that permits both research and commercial use.
