Clibrain

Models by this creator

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mamba-2.8b-instruct-openhermes

clibrain

Total Score

70

mamba-2.8b-instruct-openhermes is a state-of-the-art language model fine-tuned on a diverse dataset of over 242,000 entries, including GPT-4 generated data from sources like GPTeacher, WizardLM, Airoboros GPT-4, and Camel-AI's domain expert datasets. It was developed by clibrain and is an evolution of the OpenHermes-2.5-Mistral-7B model, utilizing a novel Mamba architecture that shows promising performance on language modeling tasks. Similar models include the OpenHermes-2.5-Mistral-7B, Nous-Hermes-Llama2-7b, Nous-Hermes-Llama2-13b, and NeuralHermes-2.5-Mistral-7B, all of which are fine-tuned versions of the original Hermes model with various dataset and architectural improvements. Model inputs and outputs The mamba-2.8b-instruct-openhermes model is a text-to-text language model, taking in natural language prompts and generating relevant responses. Inputs Prompt**: Natural language prompts or instructions for the model to generate a relevant response. Outputs Text response**: The model's generated response to the input prompt, which can range from short answers to longer, more elaborative text. Capabilities The mamba-2.8b-instruct-openhermes model excels at a variety of language tasks, including text generation, question answering, and following complex instructions. It has shown strong performance on benchmark tests like GPT4All, AGIEval, and BigBench, outperforming previous versions of the Hermes model. What can I use it for? The mamba-2.8b-instruct-openhermes model can be used for a wide range of applications, from chatbots and virtual assistants to content generation and task completion. Its fine-tuning on a diverse dataset of high-quality data makes it a capable generalist model that can handle a variety of requests and use cases. Things to try One interesting aspect of the mamba-2.8b-instruct-openhermes model is its ability to engage in multi-turn conversations and follow complex instructions, thanks to its training on the ChatML prompt format. Developers can experiment with using system prompts to set the model's persona and instructions, and then engage it in structured dialogues to see the range of its capabilities.

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Updated 5/17/2024