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For users seeking a cost-effective engine, opting for an open-source model is the recommended choice. Here is the list of best text-to-text generation Open Source Models: . 1. Llama 2. Llama 2 comprises pre-trained and finely-tuned generative text models, with a parameter range spanning 7 billion to 70 billion.
- Use Cases
- Task Variants
- Language Model Variants
- Text Generation from Image and Text
- Inference
- Text Generation Inference
- Chatui Spaces
- Useful Resources
Instruction Models
A model trained for text generation can be later adapted to follow instructions. One of the most used open-source models for instruction is OpenAssistant, which you can try at Hugging Chat.
Code Generation
A Text Generation model, also known as a causal language model, can be trained on code from scratch to help the programmers in their repetitive coding tasks. One of the most popular open-source models for code generation is StarCoder, which can generate code in 80+ languages. You can try it here.
Stories Generation
A story generation model can receive an input like "Once upon a time" and proceed to create a story-like text based on those first words. You can try this applicationwhich contains a model trained on story generation, by MosaicML. If your generative model training data is different than your use case, you can train a causal language model from scratch. Learn how to do it in the free transformers course!
Completion Generation Models
A popular variant of Text Generation models predicts the next word given a bunch of words. Word by word a longer text is formed that results in for example: 1. Given an incomplete sentence, complete it. 2. Continue a story given the first sentences. 3. Provided a code description, generate the code. The most popular models for this task are GPT-based models, Mistral or Llama series. These models are trained on data that has no labels, so you just need plain text to train your own model. You c...
Text-to-Text Generation Models
These models are trained to learn the mapping between a pair of texts (e.g. translation from one language to another). The most popular variants of these models are NLLB, FLAN-T5, and BART. Text-to-Text models are trained with multi-tasking capabilities, they can accomplish a wide range of tasks, including summarization, translation, and text classification.
When it comes to text generation, the underlying language model can come in several types: 1. Base models: refers to plain language models like Mistral 7B and Llama-2-70b. These models are good for fine-tuning and few-shot prompting. 2. Instruction-trained models: these models are trained in a multi-task manner to follow a broad range of instructio...
There are language models that can input both text and image and output text, called vision language models. LLaVA and BLIP-2 are good examples. Although they work just like other language models by means of input parameters for generation, since they also take input images, you can use them with image-to-text pipeline. You can find information abo...
You can use the 🤗 Transformers library text-generationpipeline to do inference with Text Generation models. It takes an incomplete text and returns multiple outputs with which the text can be completed. Text-to-Text generation models have a separate pipeline called text2text-generation. This pipeline takes an input containing the sentence includin...
Text Generation Inference (TGI) is an open-source toolkit for serving LLMs tackling challenges such as response time. TGI powers inference solutions like Inference Endpoints and Hugging Chat, as well as multiple community projects. You can use it to deploy any supported open-source large language model of your choice.
Hugging Face Spaces includes templates to easily deploy your own instance of a specific application. ChatUI is an open-source interface that enables serving conversational interface for large language models and can be deployed with few clicks at Spaces. TGI powers these Spaces under the hood for faster inference. Thanks to the template, you can de...
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Jul 17, 2023 · Brief Background on Text Generation Text generation models are essentially trained with the objective of completing an incomplete text or generating text from scratch as a response to a given instruction or question. Models that complete incomplete text are called Causal Language Models, and famous examples are GPT-3 by OpenAI and Llama by Meta AI.
Text generation is a process where an AI system produces written content, imitating human language patterns and styles. The process involves generating coherent and meaningful text that resembles natural human communication. Text generation has gained significant importance in various fields, including natural language processing, content ...
Jul 24, 2023 · As far as i understant, text-generation is the process of generating text that follows after the given input text (or "predicting the next word"), whereas text2text-generation refers to transforming text, like you would do when you translate text into another language or auto-correcting spelling in a text.
Text generation is the process of automatically producing coherent and meaningful text, which can be in the form of sentences, paragraphs, or even entire documents. It involves various techniques, which can be found under the field such as natural language processing (NLP), machine learning, and deep learning algorithms, to analyze input data and generate human-like text.
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