Compare Models
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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. -
Cohere
Summarize
$0.015Cohere is a Canadian startup that provides high-performance and secure LLMs for the enterprise. Their models work on public, private, or hybrid clouds and is available as an API that can be integrated into various libraries using Python, Node, or Go software development kits (SDKs).Cohere Summarize generates a succinct version of a provided text. This summary relays the most important messages of the text, and a user can configure the results with a variety of parameters to support unique use cases. It can instantly encapsulate the key points of a document and provides text summarization capabilities at scale. -
OpenAI
text-davinci-003
$0.02Text-davinci-003 is recognized as GPT 3.5 and is a variant of the GPT-3 model. While both Davinci and text-davinci-003 are powerful models, they differ in a few key ways. Text-davinci-003 is a newer and more capable model explicitly designed for instruction-following tasks. Text-davinci-003 was trained on a more recent dataset containing data up to June 2021. It can do any language task with better quality, longer output, and consistent instruction-following than the Curie, Babbage, or Ada models. Text-davinci-003 supports a longer context window (max prompt plus completion length) than Davinci.For those requesting the OpenAI’s API, GPT-3.5-turbo may be a better choice for tasks that require high accuracy in math or zero-shot classification and sentiment analysis than text-davinci-003. To note, GPT-3.5-turbo performs at a similar capability to text-davinci-003 but at 10 percent the price per token. OpenAI recommends GPT-3.5-turbo for most use cases. -
OpenAI
text-embedding-ada-002
$0.0001An embedding API model, such as Ada, is a powerful tool that converts words into numerical representations, enabling computers to understand and process natural language more effectively. This process is crucial for developing machine learning algorithms and artificial intelligence systems that can interact with humans, analyze text, or make predictions based on text. OpenAI’s text embeddings is built for advanced search, clustering, topic modeling, and classification functionality.Access is available through a request to OpenAI’s API. -
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). -
OpenAI
Whisper
0.006Whisper is an automatic speech recognition (ASR) system capable of transcribing in multiple languages as well as translating them into English. With Whisper, you can easily transcribe speech into text, allowing you to capture conversations and meetings for future reference. And if you need to communicate with someone who speaks a different language, Whisper can help with that too — it can translate many different languages into English, making it easier than ever to bridge the gap and ensure that everyone is on the same page.
Whisper is a general-purpose speech recognition model. It is trained on a large dataset of diverse audio and is also a multitasking model that can perform multilingual speech recognition, speech translation, and language identification. The speech to text API has two endpoints (transcriptions and translations) and file uploads are currently limited to 25 MB, and the following input file types are supported: mp3, mp4, mpeg, mpga, m4a, wav, and webm.
