aya-23-35B
Maintainer: CohereForAI
147
🌐
| Property | Value |
|---|---|
| Run this model | Run on HuggingFace |
| API spec | View on HuggingFace |
| Github link | No Github link provided |
| Paper link | No paper link provided |
Create account to get full access
Model overview
The aya-23-35B model is a highly capable multilingual language model developed by CohereForAI. It builds on the Command family of models and the Aya Collection dataset to provide 23 languages of support, including Arabic, Chinese, English, French, German, and more. Compared to the smaller aya-23-8B version, the 35B model offers enhanced performance across a variety of tasks.
Model inputs and outputs
The aya-23-35B model takes text as input and generates text as output. It is a powerful autoregressive language model with advanced multilingual capabilities.
Inputs
- Text: The model accepts textual inputs in any of the 23 supported languages.
Outputs
- Generated text: The model will generate coherent text in the target language, following the provided input.
Capabilities
The aya-23-35B model excels at a wide range of language tasks, including generation, translation, summarization, and question answering. Its multilingual nature allows it to perform well across a diverse set of languages and use cases.
What can I use it for?
The aya-23-35B model can be used for a variety of applications that require advanced multilingual language understanding and generation. Some potential use cases include:
- Content creation: Generating high-quality text in multiple languages for blogs, articles, or marketing materials.
- Language translation: Translating text between the 23 supported languages with high accuracy.
- Question answering: Providing informative responses to user questions across a wide range of topics.
- Chatbots and virtual assistants: Building conversational AI systems that can communicate fluently in multiple languages.
Things to try
One interesting aspect of the aya-23-35B model is its ability to follow complex instructions and perform multi-step tasks. Try providing the model with a detailed prompt that requires it to search for information, synthesize insights, and generate a comprehensive response. The model's strong reasoning and grounding capabilities should shine in such scenarios.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
Related Models
🎲
aya-23-8B
181
The aya-23-8B is an open weights research release of an instruction fine-tuned model from CohereForAI with highly advanced multilingual capabilities. It is part of the Aya Collection of models, which focus on pairing a highly performant pre-trained Command family of models with the Aya dataset. The result is a powerful multilingual large language model serving 23 languages, including Arabic, Chinese, English, French, German, and more. Model inputs and outputs The aya-23-8B model takes text as input and generates text as output. It is a large language model optimized for a variety of natural language processing tasks such as language generation, translation, and question answering. Inputs Text prompts in one of the 23 supported languages Outputs Relevant, coherent text responses in the same language as the input Capabilities The aya-23-8B model demonstrates strong multilingual capabilities, allowing it to understand and generate high-quality text in 23 languages. It can be used for a variety of language-related tasks, including translation, summarization, and open-ended question answering. What can I use it for? The aya-23-8B model can be used for a wide range of multilingual natural language processing applications, such as chatbots, language translation services, and content generation. Its broad language support makes it well-suited for global or multilingual projects that need to communicate effectively across different languages. Things to try One interesting aspect of the aya-23-8B model is its ability to follow instructions in multiple languages. You could try prompting it with task descriptions or commands in different languages and see how it responds. Additionally, you could experiment with using the model for translation tasks, feeding it text in one language and seeing if it can accurately translate it to another.
Updated Invalid Date
📊
aya-101
556
The Aya model is a massively multilingual generative language model developed by Cohere For AI. It covers 101 languages and outperforms other multilingual models like mT0 and BLOOMZ across a variety of automatic and human evaluations. The Aya model was trained on datasets like xP3x, Aya Dataset, Aya Collection, and ShareGPT-Command. Model inputs and outputs The Aya-101 model is a Transformer-based autoregressive language model that can generate text in 101 languages. It takes text as input and produces text as output. Inputs Natural language text in any of the 101 supported languages Outputs Generated natural language text in any of the 101 supported languages Capabilities The Aya model has strong multilingual capabilities, allowing it to understand and generate text in a wide range of languages. It can be used for tasks like translation, text generation, and question answering across multiple languages. What can I use it for? The Aya-101 model can be used for a variety of multilingual natural language processing tasks, such as: Multilingual text generation Multilingual translation Multilingual question answering Multilingual summarization Developers and researchers can use the Aya model to build applications and conduct research that require advanced multilingual language understanding and generation capabilities. Things to try Some interesting things to try with the Aya model include: Exploring its performance on specialized multilingual datasets or benchmarks Experimenting with prompting and fine-tuning techniques to adapt the model to specific use cases Analyzing the model's zero-shot transfer capabilities across languages Investigating the model's ability to handle code-switching or multilingual dialogue
Updated Invalid Date
📈
c4ai-command-r-08-2024
134
C4AI Command R 08-2024 is a 35 billion parameter highly performant generative model developed by Cohere and Cohere For AI. The model is optimized for a variety of use cases including reasoning, summarization, and question answering. It has the capability for multilingual generation, trained on 23 languages and evaluated in 10 languages, as well as highly performant RAG capabilities. The C4AI Command R+ model is an open weights research release of a 104 billion parameter model with even more advanced capabilities. This includes Retrieval Augmented Generation (RAG) and multi-step tool use, which allows the model to combine multiple tools over multiple steps to accomplish complex tasks. Model inputs and outputs Inputs Text**: The models take text input only. Outputs Text**: The models generate text output only. Capabilities Both C4AI Command R and C4AI Command R+ have impressive capabilities, including strong performance on reasoning, summarization, and question answering tasks. The models also have advanced features like grounded generation, which allows them to generate responses that cite the sources of the information used, and conversational tool use, where the models can leverage external tools to assist in completing tasks. C4AI Command R+ in particular stands out for its multi-step tool use capabilities, which enable it to combine multiple tools over multiple steps to tackle complex problems. This makes it a powerful tool for automating sophisticated workflows and tasks. What can I use it for? These models could be used in a wide variety of applications, such as: Conversational AI**: Both models can be used to power advanced chatbots and virtual assistants, leveraging their strong language understanding and generation capabilities. Content Generation**: The models can be used to generate high-quality text for applications like article writing, creative writing, and summarization. Task Automation**: The tool use capabilities of C4AI Command R+ make it well-suited for automating complex, multi-step workflows. Research and Development**: As open weights models, C4AI Command R and C4AI Command R+ can be used by researchers and developers to advance the state-of-the-art in language models and AI. Things to try Some interesting things to try with these models include: Experiment with the different tool use and grounded generation capabilities to see how they can be leveraged for your specific use cases. Explore the models' multilingual capabilities by testing them on a variety of languages. Try using C4AI Command R+ for tasks that require combining multiple steps or tools, and see how it performs compared to other models. Use the models for open-ended generation tasks and analyze the quality and coherence of the outputs.
Updated Invalid Date
📉
c4ai-command-r-plus-08-2024
129
The c4ai-command-r-plus-08-2024 model is a highly advanced 104 billion parameter AI model developed by CohereForAI. It is part of a family of open weight releases from Cohere For AI and Cohere, with a smaller companion model being the c4ai-command-r-08-2024. The model has been trained with Retrieval Augmented Generation (RAG) and tool use capabilities, allowing it to automate sophisticated multi-step tasks by combining multiple tools. It is a multilingual model trained on 23 languages and evaluated in 10 languages. Model inputs and outputs Inputs Text**: The c4ai-command-r-plus-08-2024 model takes text as input. Outputs Text**: The model generates text as output. Capabilities The c4ai-command-r-plus-08-2024 model has highly advanced capabilities, including Retrieval Augmented Generation (RAG) and tool use. It can use a variety of tools to automate complex tasks, combining multiple tools over multiple steps. The model has also been trained for grounded generation, allowing it to generate responses that cite relevant information sources. What can I use it for? The c4ai-command-r-plus-08-2024 model is optimized for a variety of use cases, including reasoning, summarization, and question answering. Its tool use and RAG capabilities make it well-suited for automating sophisticated workflows and enhancing human productivity. Potential use cases include research, content creation, task automation, and more. Things to try One key capability of the c4ai-command-r-plus-08-2024 model is its ability to use tools to accomplish complex tasks. Try experimenting with the model's tool use functionality by providing it with a list of available tools and prompting it to generate a sequence of actions to perform. You can also explore its grounded generation capabilities by providing it with a set of relevant documents and observing how it generates responses that cite those sources.
Updated Invalid Date