Find and compare the best Artificial Intelligence software for AI-FLOW in 2024
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Use the comparison tool below to compare the top Artificial Intelligence software for AI-FLOW on the market. You can filter results by user reviews, pricing, features, platform, region, support options, integrations, and more.
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GPT-3 models are capable of understanding and generating natural language. There are four main models available, each with a different level of power and suitable for different tasks. Ada is the fastest and most capable model while Davinci is our most powerful. GPT-3 models are designed to be used in conjunction with the text completion endpoint. There are models that can be used with other endpoints. Davinci is the most versatile model family. It can perform all tasks that other models can do, often with less instruction. Davinci is the best choice for applications that require a deep understanding of the content. This includes summarizations for specific audiences and creative content generation. These higher capabilities mean that Davinci is more expensive per API call and takes longer to process than other models.
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DALL*E 3 is a system that understands nuance and details better than previous systems. This allows you to translate your ideas easily into images with exceptional accuracy. Modern text-to image systems tend to ignore words and descriptions, forcing the user to learn prompt engineering. DALL*E 3 is a significant leap forward in terms of our ability to produce images that adhere exactly to the text provided. DALL*E 3 is a significant improvement over DALL*E 2, even with the same prompt. DALL*E 3 was built on ChatGPT. This allows you to use ChatGPT both as a brainstorming tool and to refine your prompts. Ask ChatGPT to show you anything from a simple phrase to a detailed sentence. ChatGPT will generate detailed, tailored prompts for DALL*E 3, based on your idea. ChatGPT can be asked to tweak an image if you don't like it.
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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.
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MusicGen
MusicGen
FreeMeta's MusicGen, an open-source deep learning language model, can generate short pieces based on text. The model was trained using 20,000 hours worth of music, including full tracks and individual instrument samples. MusicGen can produce about 12 seconds of audio based on a text description. -
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Stable Diffusion
Stability AI
$0.2 per imageWe have all been overwhelmed by the response over the past few weeks and have been hard at work to ensure a safe release. We have incorporated data from our beta models and community for developers to use. HuggingFace's tireless legal, technology and ethics teams and CoreWeave's brilliant engineers worked together. An AI-based Safety Classifier has been developed and is included as a default feature in the overall software package. This can understand concepts and other factors over generations to remove outputs that are not desired by the model user. This can be easily adjusted, and we welcome suggestions from the community on how to improve it. Although image generation models are powerful, we still need to improve our understanding of how to best represent what we want. -
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Llama 2
Meta
FreeThe 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 -
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Mistral 7B
Mistral AI
We solve the most difficult problems to make AI models efficient, helpful and reliable. We are the pioneers of open models. We give them to our users, and empower them to share their ideas. Mistral-7B is a powerful, small model that can be adapted to many different use-cases. Mistral 7B outperforms Llama 13B in all benchmarks. It has 8k sequence length, natural coding capabilities, and is faster than Llama 2. It is released under Apache 2.0 License and we made it simple to deploy on any cloud. -
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GPT-4V (Vision)
OpenAI
GPT-4 with Vision (GPT-4V), our latest capability, allows users to instruct GPT-4 on how to analyze images input by the user. Some researchers and developers of artificial intelligence consider the incorporation of additional modalities, such as image inputs, into large language models. Multimodal LLMs can be used to expand the impact of existing language-only systems by providing them with novel interfaces, capabilities and experiences. In this system card we analyze the GPT-4V safety properties. We have built on the safety work for GPT-4V and here we go deeper into the evaluations and preparations for image inputs.
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