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. -
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. -
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 -
Amazon
SageMaker
FREEAmazon SageMaker enables developers to create, train, and deploy machine-learning (ML) models in the cloud. SageMaker also enables developers to deploy ML models on embedded systems and edge-devices. Amazon SageMaker JumpStart helps you quickly and easily get started with machine learning. The solutions are fully customizable and supports one-click deployment and fine-tuning of more than 150 popular open source models such as natural language processing, object detection, and image classification models that can help with extracting and analyzing data, fraud detection, churn prediction and personalized recommendations.The Hugging Face LLM Inference DLCs on Amazon SageMaker, allows support the following models: BLOOM / BLOOMZ, MT0-XXL, Galactica, SantaCoder, GPT-Neox 20B (joi, pythia, lotus, rosey, chip, RedPajama, open assistant, FLAN-T5-XXL (T5-11B), Llama (vicuna, alpaca, koala), Starcoder / SantaCoder, and Falcon 7B / Falcon 40B. Hugging Face’s LLM DLC is a new purpose-built Inference Container to easily deploy LLMs in a secure and managed environment. -
TruthGPT
TruthGPT
OtherTruthGPT is a large language model (LLM), and according to Elon Musk, TruthGPT will be a “maximum truth-seeking” AI. In terms of how it works, it filters through thousands of datasets and draws educated conclusions to provide answers that are as unbiased as possible. TruthGPT is powered by $TRUTH, a tradable cryptocurrency on the Binance Smart Chain. $TRUTH holders will soon access additional benefits when using TruthGPT AI. When we learn more, we will update this section.
