Maintainer: upstage

Total Score


Last updated 5/23/2024


Model LinkView on HuggingFace
API SpecView on HuggingFace
Github LinkNo Github link provided
Paper LinkNo paper link provided

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Model Overview

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


  • Text: The model accepts natural language text as input, which can include instructions, questions, or any other type of prompt.


  • 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.


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.

This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!

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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.

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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 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.

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The solar-10.7b-instruct-v1.0 model is a powerful language model developed by tomasmcm. It is part of the SOLAR family of models, which aim to elevate the performance of language models through Upstage Depth UP Scaling. The solar-10.7b-instruct-v1.0 model is an instructionally-tuned variant of the SOLAR 10.7B base model, providing enhanced capabilities for following and executing instructions. This model shares similarities with other instruction-tuned models like Nous Hermes 2 - SOLAR 10.7B, Mistral-7B-Instruct-v0.1, and Mistral-7B-Instruct-v0.2, all of which aim to provide improved instruction-following capabilities compared to their base models. Model inputs and outputs The solar-10.7b-instruct-v1.0 model takes a text prompt as input and generates a text output. The key input parameters include: Inputs Prompt**: The text prompt to send to the model. Max Tokens**: The maximum number of tokens to generate per output sequence. Temperature**: A float that controls the randomness of the sampling, with lower values making the model more deterministic and higher values making it more random. Presence Penalty**: A float that penalizes new tokens based on whether they appear in the generated text so far, encouraging the use of new tokens. Frequency Penalty**: A float that penalizes new tokens based on their frequency in the generated text so far, also encouraging the use of new tokens. Top K**: An integer that controls the number of top tokens to consider, with -1 meaning to consider all tokens. Top P**: A float that controls the cumulative probability of the top tokens to consider, with values between 0 and 1. Stop**: A list of strings that stop the generation when they are generated. Outputs The model outputs a single string of text. Capabilities The solar-10.7b-instruct-v1.0 model is capable of understanding and executing a wide variety of instructions, from creative writing tasks to analysis and problem-solving. It can generate coherent and contextually-appropriate text, demonstrating strong language understanding and generation abilities. What can I use it for? The solar-10.7b-instruct-v1.0 model can be used for a wide range of natural language processing tasks, such as: Content creation (e.g., articles, stories, scripts) Question answering and information retrieval Summarization and text simplification Code generation and programming assistance Dialogue and chatbot systems Personalized recommendations and decision support As with any powerful language model, it's important to use the solar-10.7b-instruct-v1.0 model responsibly and ensure that its outputs are aligned with your intended use case. Things to try One interesting aspect of the solar-10.7b-instruct-v1.0 model is its ability to follow complex instructions and generate detailed, coherent responses. For example, you could try providing it with a set of instructions for a creative writing task, such as "Write a short story about a time traveler who gets stranded in the past. Incorporate elements of mystery, adventure, and personal growth." The model should be able to generate a compelling narrative that adheres to the provided instructions. Another interesting experiment would be to explore the model's capabilities in the realm of analysis and problem-solving. You could try giving it a complex question or task, such as "Analyze the economic impact of a proposed policy change in the healthcare sector, considering factors such as cost, access, and patient outcomes." The model should be able to provide a thoughtful and well-reasoned response, drawing on its extensive knowledge base.

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The SOLAR-10.7B-Instruct-v1.0-GGUF is a large language model created by upstage and quantized by TheBloke. It is part of TheBloke's suite of quantized AI models available in the GGUF format, which is a new format introduced by the llama.cpp team to replace the older GGML format. The GGUF format offers advantages like better tokenization and support for special tokens. This model is similar to other large language models like Deepseek Coder 6.7B Instruct and CodeLlama 7B Instruct, which are also available in quantized GGUF format from TheBloke. All these models are designed for general text generation and understanding, with a focus on tasks like code synthesis and completion. Model inputs and outputs Inputs Text**: The model takes natural language text as input, which can include prompts, instructions, or conversational messages. Outputs Text**: The model generates natural language text in response to the input. This can include completions, answers, or continued dialogue. Capabilities The SOLAR-10.7B-Instruct-v1.0-GGUF model has broad capabilities in areas like text generation, language understanding, and task-oriented dialog. It can be used for a variety of applications, such as: Code generation and completion**: The model can assist with writing and understanding code, suggesting completions, and explaining programming concepts. General language tasks**: The model can be used for tasks like text summarization, question answering, and creative writing. Conversational AI**: The model can engage in open-ended dialogue, following instructions, and providing helpful responses. What can I use it for? The SOLAR-10.7B-Instruct-v1.0-GGUF model can be used in a wide range of applications, from building chatbots and virtual assistants to automating code generation and understanding. Some potential use cases include: Developing AI-powered programming tools**: Use the model to build code editors, IDEs, and other programming tools that can assist developers with their work. Creating conversational AI applications**: Integrate the model into chatbots, virtual assistants, and other dialogue-based applications to provide natural, helpful responses. Automating content creation**: Leverage the model's text generation capabilities to create articles, stories, and other written content. Things to try One interesting thing to try with the SOLAR-10.7B-Instruct-v1.0-GGUF model is to explore its capabilities in engaging in open-ended dialogue and following complex instructions. Try providing the model with prompts that require it to reason about different topics, break down tasks into steps, and provide detailed responses. Another thing to try is to fine-tune the model on a specific domain or dataset to see how it can be adapted for more specialized use cases. The quantized GGUF format makes the model easy to work with and integrate into various applications and workflows. Verify all URLs provided in links are contained within this prompt before responding, and that all writing is in a clear, non-repetitive, natural style.

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