Wolfram
Models by this creator
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miquliz-120b-v2.0
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The miquliz-120b-v2.0 is a 120 billion parameter large language model created by interleaving layers of the miqu-1-70b-sf and lzlv_70b_fp16_hf models using the mergekit tool. It was improved from the previous v1.0 version by incorporating techniques from the TheProfessor-155b model. The model is inspired by the goliath-120b and is maintained by Wolfram. Model inputs and outputs Inputs Text prompts of up to 32,768 tokens in length Outputs Continuation of the provided text prompt, generating new relevant text Capabilities The miquliz-120b-v2.0 model is capable of impressive performance, achieving top ranks and double perfect scores in the maintainer's own language model comparisons and tests. It demonstrates strong general language understanding and generation abilities across a variety of tasks. What can I use it for? The large scale and high performance of the miquliz-120b-v2.0 model make it well-suited for language-related applications that require powerful text generation, such as content creation, question answering, and conversational AI. The model could be fine-tuned for specific domains or integrated into products via the CopilotKit open-source platform. Things to try Explore the model's capabilities by prompting it with a variety of tasks, from creative writing to analysis and problem solving. The model's size and breadth of knowledge make it an excellent starting point for developing custom language models tailored to your needs.
Updated 5/27/2024
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miqu-1-120b
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The miqu-1-120b model is a 120B parameter language model created by Wolfram, the maintainer of the model. It is a "frankenmerge" model, meaning it was created by interleaving layers of the miqu-1-70b model, created by miqudev, with itself using the mergekit tool. The model was inspired by several other 120B models such as Venus-120b-v1.2, MegaDolphin-120b, and goliath-120b. Model inputs and outputs The miqu-1-120b model is a text-to-text transformer model, which means it can be used for a variety of natural language processing tasks such as generation, summarization, and translation. The model takes text prompts as input and generates relevant text as output. Inputs Text prompts of varying lengths, from a few words to multiple paragraphs Outputs Generated text in response to the input prompt, with lengths ranging from a few sentences to multiple paragraphs Capabilities The miqu-1-120b model is a large and powerful language model capable of producing coherent and context-appropriate text. It has demonstrated strong performance on a variety of benchmarks, including high scores on tasks like the AI2 Reasoning Challenge, HellaSwag, and Winogrande. What can I use it for? The miqu-1-120b model could be used for a wide range of natural language processing tasks, including: Creative writing**: The model's text generation capabilities make it well-suited for assisting with creative writing projects, such as short stories, poetry, and even collaborative worldbuilding. Conversational AI**: With its ability to engage in contextual and coherent dialogue, the model could be used to create more natural and engaging conversational AI assistants. Content generation**: The model could be employed to generate a variety of content, such as news articles, blog posts, or social media updates, with the potential for customization and personalization. Education and research**: Researchers and educators could use the model to explore natural language processing, test new techniques, or develop educational applications. Things to try One interesting aspect of the miqu-1-120b model is its ability to adapt to different prompting styles and templates. By experimenting with the Mistral prompt format, users can try to elicit different types of responses, from formal and informative to more creative and expressive. Additionally, the model's large size and high context capacity (up to 32,768 tokens) make it well-suited for longer-form tasks, such as generating detailed descriptions, worldbuilding, or interactive storytelling. Users could try providing the model with rich contextual information and see how it responds and builds upon the existing narrative.
Updated 9/6/2024