Clibrain
Models by this creator
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mamba-2.8b-instruct-openhermes
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.
Updated 5/28/2024
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lince-zero
46
LINCE-ZERO (Llm for Instructions from Natural Corpus en Español) is a Spanish instruction-tuned large language model developed by Clibrain. It is a causal decoder-only model with 7B parameters, based on the Falcon-7B model and fine-tuned on an 80k examples proprietary dataset. LINCE-ZERO is designed for instruction-following and general language understanding tasks in Spanish. Similar models like DeciLM-7B-instruct and the CodeLlama series are also instruction-tuned language models, but focused on English and code-related tasks respectively. The LINCE-ZERO model stands out by being specialized for Spanish instruction-following. Model Inputs and Outputs LINCE-ZERO is a text-to-text model, taking text as input and generating text as output. The model can be used for a variety of natural language processing tasks such as language understanding, dialogue, and generation. Inputs Text**: The model takes arbitrary Spanish text as input. Outputs Text**: The model generates Spanish text in response to the input. Capabilities LINCE-ZERO demonstrates strong Spanish language understanding and generation capabilities, particularly for instruction-following tasks. It can assist with a wide range of activities like answering questions, summarizing text, translating between Spanish and other languages, and even helping to write creative content in Spanish. What Can I Use It For? The LINCE-ZERO model is well-suited for building Spanish language chatbots, virtual assistants, and other applications that require fluent Spanish language understanding and generation. Developers could leverage the model's instruction-following abilities to create Spanish-language productivity tools, educational apps, or creative writing aids. Companies in industries like customer service, e-commerce, and healthcare could potentially use LINCE-ZERO to enhance their Spanish-language offerings and improve the experience for their Spanish-speaking users and customers. Things to Try One interesting aspect of LINCE-ZERO is its potential for multilingual applications. Since the model is focused on Spanish, it could be combined with English language models like DeciLM-7B-instruct to build bilingual assistants capable of understanding and responding in both Spanish and English. Developers could also experiment with fine-tuning LINCE-ZERO on domain-specific datasets to create models tailored for specialized Spanish language tasks, such as legal or medical text processing.
Updated 9/6/2024