Upstage
Models by this creator
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SOLAR-10.7B-Instruct-v1.0
580
The SOLAR-10.7B-Instruct-v1.0 is an advanced large language model (LLM) with 10.7 billion parameters, developed by upstage. It demonstrates superior performance in various natural language processing (NLP) tasks, outperforming models with up to 30 billion parameters. The model is built upon the Llama2 architecture and incorporates Upstage's innovative "Depth Up-Scaling" technique, which integrates weights from the Mistral 7B model and further continues pre-training. Compared to similar models, SOLAR-10.7B-Instruct-v1.0 stands out for its compact size and remarkable capabilities. It surpasses the recent Mixtral 8X7B model in performance, as evidenced by the experimental results. The model also offers robustness and adaptability, making it an ideal choice for fine-tuning tasks. Model Inputs and Outputs Inputs Text**: The model accepts natural language text as input, which can include instructions, questions, or any other type of prompt. Outputs Text**: The model generates coherent and relevant text in response to the provided input. The output can range from short responses to longer, multi-sentence outputs, depending on the task and prompt. Capabilities SOLAR-10.7B-Instruct-v1.0 demonstrates strong performance across a variety of NLP tasks, including text generation, question answering, and task completion. For example, the model can be used to generate high-quality, human-like responses to open-ended prompts, provide informative answers to questions, and complete various types of instructions or tasks. What Can I Use It For? The SOLAR-10.7B-Instruct-v1.0 model is a versatile tool that can be applied to a wide range of applications. Some potential use cases include: Content Generation**: The model can be used to generate engaging and informative text for various purposes, such as articles, stories, or product descriptions. Chatbots and Virtual Assistants**: The model can be fine-tuned to serve as the conversational backbone for chatbots and virtual assistants, providing natural and contextual responses. Language Learning and Education**: The model can be used to create interactive educational materials, personalized tutoring systems, or language learning tools. Task Automation**: The model can be used to automate various text-based tasks, such as data entry, form filling, or report generation. Things to Try One interesting aspect of SOLAR-10.7B-Instruct-v1.0 is its ability to handle longer input sequences, thanks to the "rope scaling" technique used in its development. This allows the model to work effectively with extended prompts or multi-turn conversations, opening up possibilities for more complex and engaging interactions. Another area to explore is the model's performance on specialized or domain-specific tasks. By fine-tuning SOLAR-10.7B-Instruct-v1.0 on relevant datasets, users can potentially create highly specialized language models tailored to their unique needs, such as legal analysis, medical diagnosis, or scientific research.
Updated 5/28/2024
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solar-pro-preview-instruct
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The solar-pro-preview-instruct model is an advanced 22 billion parameter large language model (LLM) developed by upstage. It is designed to run efficiently on a single GPU, delivering performance comparable to much larger models like Llama 3.1 with 70 billion parameters. The model was developed using an enhanced version of upstage's depth up-scaling method, which scales a smaller 14 billion parameter model to 22 billion parameters. Compared to the SOLAR-10.7B-Instruct-v1.0 model, the solar-pro-preview-instruct demonstrates enhanced performance, particularly on the MMLU-Pro and IFEval benchmarks which test a model's knowledge and instruction-following abilities. It is a pre-release version of the official Solar Pro model, with limitations on language coverage and context length, but with the potential for further expansion. Model inputs and outputs Inputs Instruction prompts**: The model is designed to excel at following instructions and engaging in conversational tasks. It uses the ChatML prompt template for optimal performance. Outputs Conversational responses**: The model generates coherent and relevant responses to instruction-based prompts, demonstrating strong task-completion abilities. Capabilities The solar-pro-preview-instruct model shows superior performance compared to LLMs with under 30 billion parameters. It is capable of engaging in a wide variety of instruction-following tasks, from answering questions to generating summaries and completing multi-step workflows. The model's depth up-scaling approach allows it to pack a lot of capability into a relatively compact size, making it an efficient choice for deployment. What can I use it for? The solar-pro-preview-instruct model is well-suited for building AI assistants and chatbots that need to understand and follow complex instructions. It could be used to power virtual assistants, content generation tools, code completion applications, and more. Its small footprint makes it a compelling choice for edge deployments or other scenarios where compute resources are constrained. Things to try One interesting aspect of the solar-pro-preview-instruct model is its ability to handle long-form instruction-based prompts, thanks to the RoPE scaling techniques used in its development. Try providing the model with multi-step workflows or intricate task descriptions and see how it responds. You can also experiment with fine-tuning the model on your own datasets to adapt it to specialized domains or use cases.
Updated 9/19/2024
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SOLAR-0-70b-16bit
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SOLAR-0-70b-16bit is a large language model developed by Upstage, a fine-tune of the LLaMa 2 model. As a top-ranked model on the HuggingFace Open LLM leaderboard, it demonstrates the progress enabled by open-source AI. The model is available to try on Poe at https://poe.com/Solar-0-70b. Similar models developed by Upstage include solar-10.7b-instruct-v1.0 and the Llama-2-70b-hf model from Meta. Model inputs and outputs Inputs Text prompts Outputs Generated text responses Capabilities SOLAR-0-70b-16bit is a powerful language model capable of understanding and generating human-like text. It can handle long input sequences of up to 10,000 tokens, thanks to the rope_scaling option. The model demonstrates strong performance on a variety of natural language tasks, including open-ended dialogue, question answering, and content generation. What can I use it for? SOLAR-0-70b-16bit can be used for a wide range of natural language processing applications, such as: Conversational AI assistants Automatic text summarization Creative writing and content generation Question answering systems Language understanding for other AI tasks Things to try One interesting aspect of SOLAR-0-70b-16bit is its ability to handle long input sequences. This makes it well-suited for tasks that require processing and generating complex, multi-sentence text. You could try using the model to summarize long articles or generate detailed responses to open-ended prompts. Additionally, the model's fine-tuning on the Llama 2 backbone allows it to leverage the broad knowledge and capabilities of that foundational model. You could experiment with using SOLAR-0-70b-16bit for tasks that require both language understanding and world knowledge, such as question answering or commonsense reasoning.
Updated 5/28/2024
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SOLAR-10.7B-v1.0
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SOLAR-10.7B-v1.0 is an advanced large language model (LLM) with 10.7 billion parameters, developed by Upstage. It demonstrates superior performance in various natural language processing (NLP) tasks compared to models with up to 30 billion parameters. The model was created using a methodology called "depth up-scaling" (DUS), which involves architectural modifications and continued pre-training. SOLAR-10.7B-v1.0 outperforms the recent Mixtral 8X7B model across several benchmarks. It also offers robust and adaptable performance for fine-tuning tasks. Upstage has released an instruction-tuned version of the model, SOLAR-10.7B-Instruct-v1.0, which demonstrates significant performance improvements over the base model. Model Inputs and Outputs Inputs SOLAR-10.7B-v1.0 takes in text as input, similar to other large language models. Outputs The model generates text as output, making it suitable for a variety of natural language processing tasks. Capabilities SOLAR-10.7B-v1.0 has demonstrated strong performance on benchmarks across various categories, including general language understanding, knowledge reasoning, and reading comprehension. The instruction-tuned version, SOLAR-10.7B-Instruct-v1.0, has also shown improved capabilities in areas like multi-task learning and task-oriented dialogue. What Can I Use It For? SOLAR-10.7B-v1.0 and its instruction-tuned variant SOLAR-10.7B-Instruct-v1.0 can be used for a wide range of natural language processing tasks, such as: Content generation**: Generating high-quality text for creative writing, summaries, and other applications. Question answering**: Answering a variety of questions by drawing upon the model's broad knowledge base. Text summarization**: Condensing long-form text into concise, informative summaries. Dialogue systems**: Building conversational agents and chatbots with improved coherence and contextual understanding. These models can be particularly useful for developers and researchers looking to leverage powerful, state-of-the-art language models in their projects and applications. Things to Try One interesting aspect of SOLAR-10.7B-v1.0 is its compact size compared to models with even higher parameter counts, yet its ability to outperform them on various benchmarks. Developers and researchers could explore ways to further leverage the model's efficiency and performance characteristics, such as by fine-tuning it on domain-specific tasks or integrating it into larger systems that require robust language understanding capabilities. The instruction-tuned SOLAR-10.7B-Instruct-v1.0 model also presents opportunities to experiment with task-oriented fine-tuning and prompt engineering, to unlock the model's potential in more specialized applications or to enhance its safety and alignment with user preferences.
Updated 5/28/2024
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llama-30b-instruct-2048
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llama-30b-instruct-2048 is a large language model developed by Upstage, a company focused on creating advanced AI systems. It is based on the LLaMA model released by Facebook Research, with a larger 30 billion parameter size and a longer 2048 token sequence length. The model is designed for text generation and instruction-following tasks, and is optimized for tasks such as open-ended dialogue, content creation, and knowledge-intensive applications. Similar models include the Meta-Llama-3-8B-Instruct and Meta-Llama-3-70B models, which are also large language models developed by Meta with different parameter sizes. The Llama-2-7b-hf model from NousResearch is another similar 7 billion parameter model based on the original LLaMA architecture. Model inputs and outputs Inputs The model takes in text prompts as input, which can be in the form of natural language instructions, conversations, or other types of textual data. Outputs The model generates text outputs in response to the input prompts, producing coherent and contextually relevant responses. The outputs can be used for a variety of language generation tasks, such as open-ended dialogue, content creation, and knowledge-intensive applications. Capabilities The llama-30b-instruct-2048 model is capable of generating human-like text across a wide range of topics and tasks. It has been trained on a diverse set of datasets, allowing it to demonstrate strong performance on benchmarks measuring commonsense reasoning, world knowledge, and reading comprehension. Additionally, the model has been optimized for instruction-following tasks, making it well-suited for conversational AI and virtual assistant applications. What can I use it for? The llama-30b-instruct-2048 model can be used for a variety of language generation and understanding tasks. Some potential use cases include: Conversational AI**: The model can be used to power engaging and informative chatbots and virtual assistants, capable of natural dialogue and task completion. Content creation**: The model can be used to generate creative and informative text, such as articles, stories, or product descriptions. Knowledge-intensive applications**: The model's strong performance on benchmarks measuring world knowledge and reasoning makes it well-suited for applications that require in-depth understanding of a domain, such as question-answering systems or intelligent search. Things to try One interesting aspect of the llama-30b-instruct-2048 model is its ability to handle long input sequences, thanks to the rope_scaling option. This allows the model to process and generate text for more complex and open-ended tasks, beyond simple question-answering or dialogue. Developers could experiment with using the model for tasks like multi-step reasoning, long-form content generation, or even code generation and explanation. Another interesting aspect to explore is the model's safety and alignment features. As mentioned in the maintainer's profile, the model has been carefully designed with a focus on responsible AI development, including extensive testing and the implementation of safety mitigations. Developers could investigate how these features affect the model's behavior and outputs, and how they can be further customized to meet the specific needs of their applications.
Updated 5/28/2024
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Llama-2-70b-instruct
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The Llama-2-70b-instruct model is a large language model developed by Upstage, a company specialized in AI research and development. It is a fine-tuned version of Meta's LLaMA-2 model, which has been further trained on a combination of synthetic instructions and coding tasks, as well as human-generated demonstrations from the Open-Assistant project. Similar models include the llama-30b-instruct-2048 and the SOLAR-0-70b-16bit, which are also fine-tuned versions of the LLaMA-2 model with different parameter sizes and sequence lengths. Model inputs and outputs Inputs Prompts**: The model takes in natural language prompts, which can include instructions, questions, or open-ended requests. Conversation context**: The model can also handle multi-turn conversations, where it maintains context from previous exchanges. Outputs Natural language responses**: The model generates coherent and relevant responses to the input prompts, in the form of natural language text. Code**: In addition to general language tasks, the model has been trained to generate code snippets and solutions to programming problems. Capabilities The Llama-2-70b-instruct model has demonstrated strong performance on a variety of benchmarks, including the ARC-Challenge, HellaSwag, MMLU, and TruthfulQA datasets. It outperforms many other large language models, including GPT-3.5-Turbo-16K and falcon-40b-instruct, on these tasks. The model's capabilities include natural language understanding, question answering, text generation, and code generation. It can handle long-form inputs and outputs, and can also maintain context across multiple turns of a conversation. What can I use it for? The Llama-2-70b-instruct model can be a powerful tool for a variety of applications, including: Virtual assistants**: The model's natural language understanding and generation capabilities make it well-suited for building intelligent virtual assistants that can engage in open-ended conversations. Content creation**: The model can be used to generate high-quality text, such as articles, stories, or even poetry, with the potential for further fine-tuning or customization. Programming assistance**: The model's ability to generate code and solve programming problems can be leveraged to build tools that assist developers in their work. Things to try One interesting aspect of the Llama-2-70b-instruct model is its ability to handle long-form inputs and outputs. This makes it well-suited for tasks that require maintaining context and coherence over multiple turns of a conversation. You could, for example, try engaging the model in a multi-turn dialogue, where you provide it with a complex prompt or request, and then follow up with additional questions or clarifications. Observe how the model maintains the context and provides coherent and relevant responses throughout the exchange. Another interesting thing to try would be to experiment with the model's code generation capabilities. Provide it with programming challenges or open-ended prompts related to coding, and see how it tackles these tasks.
Updated 5/28/2024
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solar-pro-preview-pretrained
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solar-pro-preview-pretrained is an AI model developed by Upstage. It is a pre-trained language model that can be used for various text-to-text tasks. The model was created using an approach called "depth up-scaling", which involves architectural modifications and continued pre-training to enhance the model's performance. The model has shown superior performance compared to other language models with less than 30 billion parameters. It demonstrates capabilities that are on par with much larger models, such as the 70 billion parameter Llama 3.1. This makes solar-pro-preview-pretrained an efficient and powerful option for fine-tuning and deploying in various natural language processing applications. Model inputs and outputs solar-pro-preview-pretrained is a large language model that can be used for a variety of text-to-text tasks. The model takes natural language text as input and generates coherent and relevant text as output. Inputs Natural language text, such as questions, instructions, or prompts Outputs Generated text that is relevant and contextually appropriate based on the input Capabilities solar-pro-preview-pretrained has demonstrated strong performance on a variety of natural language processing tasks, including question answering, text generation, and instruction following. The model's ability to generate high-quality text while remaining compact and efficient makes it a versatile tool for many applications. What can I use it for? solar-pro-preview-pretrained can be used for a wide range of applications that involve text-to-text tasks, such as: Content generation**: The model can be used to generate coherent and relevant text for blog posts, articles, or other types of content. Conversational AI**: By fine-tuning the model on conversational data, it can be used to build chatbots or virtual assistants that can engage in natural language interactions. Question answering**: The model can be used to answer questions or provide information based on provided context. Instructional tasks**: The model's strong performance on instruction following tasks makes it suitable for applications that require interpreting and executing instructions. Things to try One interesting aspect of solar-pro-preview-pretrained is its ability to perform well on tasks with limited data. By leveraging the model's pre-training and efficient architecture, you can potentially fine-tune it on smaller datasets to achieve strong results in specialized domains or applications. This could be particularly useful for businesses or organizations with limited data resources. Additionally, the model's compact size and efficient inference make it a good candidate for deployment on edge devices or in resource-constrained environments, where larger language models may not be feasible.
Updated 9/19/2024