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Models by this creator
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mixtral-7b-8expert
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The mixtral-7b-8expert is a preliminary HuggingFace implementation of a newly released Mixture of Experts (MoE) model by MistralAi. The model is capable of Text-to-Text tasks and was created by the DiscoResearch team. It is based on an early implementation by Dmytro Dzhulgakov that helped find a working setup. The model was trained with compute provided by LAION and HessianAI. Similar models include the DiscoLM-mixtral-8x7b-v2, Mixtral-8x7B-v0.1, Mixtral-8x7B-Instruct-v0.1, and Mixtral-8x22B-v0.1 models, all of which are based on the Mixtral MoE architecture. Model inputs and outputs The mixtral-7b-8expert model takes text prompts as input and generates text responses. The model can be used for a variety of natural language processing tasks such as text generation, summarization, and question answering. Inputs Text prompts or conversations Outputs Generated text responses Capabilities The mixtral-7b-8expert model is capable of generating coherent and contextually relevant text responses. It has been benchmarked on a range of tasks including HellaSwag, TruthfulQA, and MMLU, demonstrating strong performance compared to other large language models. What can I use it for? The mixtral-7b-8expert model can be used for a variety of applications that require natural language generation, such as chatbots, content creation tools, and language learning assistants. Its ability to generate high-quality text makes it a useful tool for tasks like story writing, article generation, and dialogue systems. Things to try One interesting aspect of the mixtral-7b-8expert model is its Mixture of Experts architecture, which allows it to leverage multiple specialized sub-models to generate more diverse and nuanced outputs. Experimenting with different prompts and prompt engineering techniques may reveal interesting capabilities or biases in the model's knowledge and reasoning.
Updated 5/28/2024
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DiscoLM-mixtral-8x7b-v2
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The DiscoLM Mixtral 8x7b alpha is an experimental 8x7b Mixture-of-Experts model based on Mistral AI's Mixtral 8x7b. The model was created by Bjrn Plster with the DiscoResearch team and has been fine-tuned on the Synthia, MethaMathQA and Capybara datasets. Compared to similar models like Mixtral-8x7B-v0.1 and Mixtral-8x7B-Instruct-v0.1, the DiscoLM Mixtral 8x7b alpha incorporates additional fine-tuning and updates. Model inputs and outputs The DiscoLM Mixtral 8x7b alpha is a large language model that can generate human-like text based on given prompts. It takes in natural language text as input and produces coherent, contextually relevant text as output. Inputs Natural language prompts or text Outputs Continuation of the input text, generating new coherent text Responses to questions or instructions based on the input Capabilities The DiscoLM Mixtral 8x7b alpha demonstrates strong performance on a variety of benchmarks, including the ARC (25-shot), HellaSwag (10-shot), MMLU (5-shot), TruthfulQA (0-shot), and Winogrande (5-shot) tasks. Its diverse capabilities make it suitable for open-ended text generation, question answering, and other language-based applications. What can I use it for? The DiscoLM Mixtral 8x7b alpha can be used for a wide range of natural language processing tasks, such as: Generating creative fiction or poetry Summarizing long-form text Answering questions and providing information Assisting with research and analysis Improving language learning and education Enhancing chatbots and virtual assistants DiscoResearch and the maintainer have made this model available to the community, enabling developers and researchers to explore its potential applications. Things to try One interesting aspect of the DiscoLM Mixtral 8x7b alpha is its potential for generating diverse and imaginative text. Experiment with providing the model with open-ended prompts or creative writing exercises to see how it can expand on and develop new ideas. Additionally, you can leverage the model's question-answering capabilities by posing informational queries and evaluating the coherence and accuracy of its responses.
Updated 5/28/2024
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DiscoLM_German_7b_v1
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DiscoLM German 7b v1 is a large language model developed by DiscoResearch that is focused on German-language applications. It is the successor to the EM German model family and was trained on a large dataset of instructions in German and English using a combination of supervised finetuning and reinforcement learning. The model is optimized for German text, providing proficiency in understanding, generating, and interacting with German language content while preserving its fluency in English and excelling at translation tasks. DiscoLM German 7b v1 was not designed to beat benchmarks, but rather to provide a robust and reliable model for everyday use that can serve as a drop-in replacement for ChatGPT and other proprietary models. The model's German-language output is perceived to be of even higher quality than GPT-4 in many cases, though it may not compete with larger models and top English 7b models for very complex reasoning, math or coding tasks. Model inputs and outputs Inputs The model accepts text inputs in both German and English. It uses the ChatML prompt format, which enables OpenAI endpoint compatibility and is supported by most inference libraries and frontends. System prompts can be used to steer the model's behavior, defining rules, roles, and stylistic choices. Outputs The model generates human-like text outputs in German and English. It can be used for a variety of text-to-text tasks, such as generation, translation, and interaction. Capabilities DiscoLM German 7b v1 demonstrates strong performance on German-language tasks, outperforming GPT-4 in many cases. It can be used for tasks like document generation, language modeling, and translation between German and English. The model also maintains fluency in English, making it a versatile tool for multilingual applications. What can I use it for? DiscoLM German 7b v1 can be a valuable tool for a wide range of applications that require high-quality German-language processing, such as customer service chatbots, content creation, and language learning. Its robust and reliable performance makes it a suitable replacement for proprietary models like ChatGPT in many use cases. Things to try One interesting aspect of DiscoLM German 7b v1 is its ability to leverage system prompts to steer the model's behavior and output. Developers can experiment with different prompts to explore the model's capabilities for tasks like role-playing, task completion, and creative writing. Additionally, the model's strong performance on German-to-English translation could be useful for multilingual applications and research.
Updated 5/28/2024