Cohereforai
Models by this creator
🎯
c4ai-command-r-plus
1.4K
c4ai-command-r-plus is a powerful 104 billion parameter AI model developed by CohereForAI. It is part of a family of open-sourced models from Cohere and Cohere For AI, with a smaller companion model called C4AI Command R. The model has highly advanced capabilities, including Retrieval Augmented Generation (RAG) and multi-step tool use to automate complex tasks. Model inputs and outputs Inputs Text**: The c4ai-command-r-plus model takes text as input. Outputs Generated text**: The model outputs generated text, leveraging its advanced capabilities for tasks like reasoning, summarization, and question answering. Capabilities c4ai-command-r-plus excels at a variety of tasks due to its large size and specialized training. It can use retrieval augmented generation to combine its knowledge with information from external sources. The model also has the ability to perform multi-step tool use, allowing it to break down and automate complex tasks. These capabilities make c4ai-command-r-plus a powerful AI assistant for a wide range of applications. What can I use it for? c4ai-command-r-plus can be used for many different types of projects that require advanced language understanding and generation. Some potential use cases include: Conversational AI**: The model's strong performance on tasks like question answering and summarization makes it well-suited for building intelligent chatbots and virtual assistants. Content creation**: The model's text generation capabilities could be leveraged to help with tasks like writing, ideation, and creative content production. Task automation**: The model's multi-step tool use functionality enables it to automate complex, multi-part workflows. Things to try One interesting aspect of c4ai-command-r-plus is its ability to leverage external information sources through retrieval augmented generation. This allows the model to go beyond just generating text based on its training, and instead incorporate relevant facts and details from the web or other databases. Experimenting with different prompts and information sources could lead to some fascinating and helpful outputs. Another intriguing area to explore is the model's multi-step tool use capability. Providing the model with a series of tasks and seeing how it breaks them down and automates the process could uncover new ways to leverage this powerful functionality.
Updated 5/21/2024
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c4ai-command-r-v01
1.0K
c4ai-command-r-v01 is a 35 billion parameter generative model developed by Cohere and Cohere For AI. It is a large language model with open weights optimized for a variety of use cases including reasoning, summarization, and question answering. The model has the capability for multilingual generation evaluated in 10 languages and highly performant RAG capabilities. The c4ai-command-r-v01-4bit model is a 4-bit quantized version of the c4ai-command-r-v01 model using bitsandbytes. Quantized models can offer significant storage and memory savings compared to the full-precision version. Model Inputs and Outputs Inputs Text input only Outputs Generates text only Capabilities The c4ai-command-r-v01 model has been specifically trained with conversational tool use capabilities. It can take a conversation as input along with a list of available tools, and generate a JSON-formatted list of actions to execute on those tools. The model may use a tool more than once, and can also choose to "directly answer" without using any tools. The model also has grounded generation capabilities, meaning it can generate responses based on a list of supplied document snippets and include grounding spans (citations) in the output indicating the source of the information. This enables behaviors like grounded summarization and the final step of Retrieval Augmented Generation (RAG). What Can I Use It For? The c4ai-command-r-v01 model can be used for a variety of language tasks including reasoning, summarization, and question answering. Its multilingual capabilities and tool use functionality make it well-suited for building conversational assistants that can interact with external tools and services. For example, you could use the model to build a research assistant that can search the internet, synthesize information from multiple sources, and provide a grounded response to a user's query. Or you could integrate it into a chatbot that can perform multi-step tasks by leveraging external APIs. Things to Try One interesting aspect of the c4ai-command-r-v01 model is its ability to use multiple tools in sequence to accomplish complex tasks. You could experiment with providing the model with a challenging prompt that requires combining information from several sources, and see how it leverages the available tools to generate a high-quality, grounded response. Additionally, the model's multilingual capabilities open up opportunities for building language-agnostic applications that can serve users across the globe. You could try prompting the model in different languages and observe how it adapts its output accordingly.
Updated 5/23/2024
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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 5/28/2024
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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 5/27/2024
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c4ai-command-r-plus-4bit
178
The c4ai-command-r-plus-4bit model is a 4-bit quantized version of the c4ai-command-r-plus model, a 104 billion parameter model developed by CohereForAI with advanced capabilities including Retrieval Augmented Generation (RAG) and multi-step tool use. The model is multilingual, evaluated in 10 languages, and optimized for tasks like reasoning, summarization, and question answering. This 4-bit quantized version uses bitsandbytes to reduce memory requirements. Model inputs and outputs Inputs The model takes in text as input, with no other special inputs. Outputs The model generates text as output, using its advanced capabilities to produce helpful and informative responses. Capabilities The c4ai-command-r-plus-4bit model has highly advanced capabilities, including Retrieval Augmented Generation (RAG) and multi-step tool use. This allows the model to combine multiple tools over multiple steps to accomplish complex tasks. The model is also multilingual, evaluated in 10 languages, and optimized for a variety of use cases like reasoning, summarization, and question answering. What can I use it for? The c4ai-command-r-plus-4bit model could be used for a wide range of natural language processing tasks, from question answering and summarization to task automation and open-ended conversation. Its RAG and multi-step tool use capabilities make it well-suited for applications that require complex reasoning or the combination of multiple information sources. Companies could potentially use this model to build intelligent assistants, content generation tools, or knowledge management systems. Things to try One interesting thing to try with the c4ai-command-r-plus-4bit model is its tool use functionality. By providing the model with a list of available tools, it can generate a JSON-formatted list of actions to execute on a subset of those tools, potentially using a tool more than once to accomplish a task. This allows for highly flexible and powerful task automation capabilities. Another intriguing aspect is the model's grounded generation and RAG capabilities. By providing the model with relevant document snippets, it can generate responses that include citations back to those sources, enabling grounded summarization and knowledge-augmented generation.
Updated 5/23/2024
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c4ai-command-r-v01-4bit
158
The c4ai-command-r-v01-4bit model is a 4-bit quantized version of the c4ai-command-r-v01 model, a 35 billion parameter generative language model developed by Cohere For AI. This model is part of a family of open-source AI models released by Cohere, with the larger c4ai-command-r-plus-4bit model serving as the flagship release. The c4ai-command-r-v01-4bit model is optimized for a variety of use cases including reasoning, summarization, and question answering, and has the capability for multilingual generation evaluated in 10 languages. Model inputs and outputs The c4ai-command-r-v01-4bit model takes in text input only and generates text output. Key highlights include: Inputs Text data only Outputs Generated text Can generate output in 10 languages including English, French, Spanish, Italian, German, Portuguese, Japanese, Korean, Chinese, and Arabic Capabilities The c4ai-command-r-v01-4bit model has been specifically trained with advanced capabilities like retrieval augmented generation (RAG) and multi-step tool use. This allows the model to combine multiple tools over multiple steps to accomplish complex tasks. The model also has grounded generation abilities, where it can generate responses citing relevant source material. What can I use it for? The c4ai-command-r-v01-4bit model can be used for a variety of text generation tasks like summarization, language translation, and question answering. The model's multilingual abilities also make it useful for international applications. Given its RAG and tool use capabilities, the model could be applied to build sophisticated AI assistants or chatbots that can perform complex, multi-step tasks. Things to try One interesting aspect of the c4ai-command-r-v01-4bit model is its ability to abstain from using its tools when it determines a simple direct answer is more appropriate. This flexibility can be valuable in building conversational AI systems that know when to defer to their core language modeling capabilities versus leveraging additional tools. Experimenting with different prompting strategies to take advantage of this behavior could yield interesting results.
Updated 5/23/2024
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aya-23-35B
147
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.
Updated 5/27/2024
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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 9/29/2024
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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 9/29/2024