Jphme

Models by this creator

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em_german_leo_mistral

jphme

Total Score

62

The em_german_leo_mistral model is a showcase-model of the EM German model family developed by jphme and described as the best open German Large Language Model (LLM) available as of its release. It is based on the LeoLM model, which is a version of the Llama model that has received continued pretraining on German texts, greatly improving its generation capabilities for the German language. The EM German model family includes versions based on 7B, 13B and 70B Llama-2, Mistral and LeoLM architectures, with the em_german_leo_mistral model being the recommended option as it offers the best combination of performance and computing requirements. Model inputs and outputs Inputs Prompts**: The model accepts text prompts in German that can be used to generate coherent, context-appropriate German language outputs. Outputs Generated text**: The model can generate fluent, natural-sounding German text in response to the provided prompts. The outputs cover a wide range of topics and can be used for tasks like language generation, question answering, and creative writing. Capabilities The em_german_leo_mistral model excels at understanding and generating high-quality German text. It can be used for a variety of tasks, such as writing assistance, content generation, language translation, and question answering. The model's strong performance on German language benchmarks makes it a valuable tool for anyone working with German text data. What can I use it for? The em_german_leo_mistral model can be used in a variety of applications that require generating or understanding German language content. Some potential use cases include: Content creation**: Generating German blog posts, articles, or creative writing with human-like fluency. Language learning**: Assisting language learners by providing examples of natural German language usage. Customer service**: Powering German-language chatbots or virtual assistants to provide support and information. Text summarization**: Condensing German language documents into concise summaries. Machine translation**: Translating text from other languages into high-quality German. Things to try One interesting aspect of the em_german_leo_mistral model is its ability to handle a wide range of topics and tasks in the German language. Try prompting the model with diverse subject matter, from creative writing to technical documentation, and see how it responds. You can also experiment with different prompting techniques, such as using specific instructions or starting with partial sentences, to observe how the model generates coherent and contextually appropriate text.

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Updated 5/19/2024

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Llama-2-13b-chat-german

jphme

Total Score

60

Llama-2-13b-chat-german is a variant of Meta's Llama 2 13b Chat model, finetuned by jphme on an additional dataset in German language. This model is optimized for German text, providing proficiency in understanding, generating, and interacting with German language content. However, the model is not yet fully optimized for German, as it has been trained on a small, experimental dataset and has limited capabilities due to the small parameter count. Some of the finetuning data is also targeted towards factual retrieval, and the model should perform better for these tasks than the original Llama 2 Chat. Model inputs and outputs Inputs Text input only Outputs Generates German language text Capabilities The Llama-2-13b-chat-german model is proficient in understanding and generating German language content. It can be used for tasks like answering questions, engaging in conversations, and producing written German text. However, its capabilities are limited compared to a larger, more extensively trained German language model due to the small dataset it was finetuned on. What can I use it for? The Llama-2-13b-chat-german model could be useful for projects that require German language understanding and generation, such as chatbots, language learning applications, or automated content creation in German. While its capabilities are limited, it provides a starting point for experimentation and further development. Things to try One interesting thing to try with the Llama-2-13b-chat-german model is to evaluate its performance on factual retrieval tasks, as the finetuning data was targeted towards this. You could also experiment with prompting techniques to see if you can elicit more robust and coherent German language responses from the model.

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Updated 5/21/2024