Weyaxi
Models by this creator
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Einstein-v6.1-Llama3-8B
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The Einstein-v6.1-Llama3-8B is a fine-tuned version of the Meta-Llama-3-8B model, developed by Weyaxi. This model was trained on diverse datasets using 8xRTX3090 and 1xRTXA6000 GPUs with the axolotl framework. The training was sponsored by sablo.ai. Model inputs and outputs Inputs Textual prompts Outputs Textual responses Capabilities The Einstein-v6.1-Llama3-8B model is a powerful language model capable of generating human-like text across a variety of tasks. It can be used for text generation, question answering, summarization, and more. What can I use it for? The Einstein-v6.1-Llama3-8B model can be used for a wide range of natural language processing tasks, such as chatbots, content generation, and language translation. It can be particularly useful for companies looking to automate customer service or create engaging content. Things to try Experiment with the Einstein-v6.1-Llama3-8B model to see how it performs on your specific natural language processing tasks. Try fine-tuning the model on your own data to further improve its performance for your use case.
Updated 6/13/2024
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Einstein-v6.1-Llama3-8B
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The Einstein-v6.1-Llama3-8B is a fine-tuned version of the Meta-Llama-3-8B model, developed by Weyaxi. This model was trained on diverse datasets using 8xRTX3090 and 1xRTXA6000 GPUs with the axolotl framework. The training was sponsored by sablo.ai. Model inputs and outputs Inputs Textual prompts Outputs Textual responses Capabilities The Einstein-v6.1-Llama3-8B model is a powerful language model capable of generating human-like text across a variety of tasks. It can be used for text generation, question answering, summarization, and more. What can I use it for? The Einstein-v6.1-Llama3-8B model can be used for a wide range of natural language processing tasks, such as chatbots, content generation, and language translation. It can be particularly useful for companies looking to automate customer service or create engaging content. Things to try Experiment with the Einstein-v6.1-Llama3-8B model to see how it performs on your specific natural language processing tasks. Try fine-tuning the model on your own data to further improve its performance for your use case.
Updated 6/13/2024
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Einstein-v4-7B
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The Einstein-v4-7B model is a full fine-tuned version of the mistralai/Mistral-7B-v0.1 model, trained on diverse datasets. This model was fine-tuned using 7xRTX3090 and 1xRTXA6000 GPUs with the axolotl framework, with training sponsored by sablo.ai. Similar AI models include the Einstein-v6.1-Llama3-8B model, which is a fine-tuned version of the meta-llama/Meta-Llama-3-8B model. Model inputs and outputs Inputs Text prompts**: The model takes in text-based prompts or conversations as input. Outputs Text responses**: The model generates relevant, coherent text responses based on the provided input. Capabilities The Einstein-v4-7B model has been fine-tuned on a diverse set of datasets, allowing it to engage in a wide variety of text-to-text tasks. It can provide informative and well-reasoned responses on topics spanning science, history, current events, and more. The model also demonstrates strong language understanding and generation capabilities, making it suitable for chatbot applications, question answering, and creative writing assistance. What can I use it for? The Einstein-v4-7B model can be used for a range of text-based applications, such as: Conversational AI**: Leveraging the model's language understanding and generation abilities to build intelligent chatbots and virtual assistants. Content generation**: Utilizing the model's creativity to assist with tasks like article writing, story generation, and marketing copy creation. Question answering**: Tapping into the model's knowledge to provide informative answers to a wide range of questions. Summarization**: Condensing long-form text into concise summaries. Things to try One interesting aspect of the Einstein-v4-7B model is its ability to engage in multi-turn conversations and maintain context. Try prompting the model with an open-ended question or scenario, and see how it builds upon the discussion over several exchanges. You can also experiment with different prompting techniques, such as providing detailed instructions or framing the conversation in a particular way, to observe how the model responds.
Updated 9/6/2024
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OpenHermes-2.5-neural-chat-v3-3-Slerp
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OpenHermes-2.5-neural-chat-v3-3-Slerp is a state-of-the-art text generation model created by Weyaxi. It is a merge of teknium/OpenHermes-2.5-Mistral-7B and Intel/neural-chat-7b-v3-3 using a slerp merge method. This model aims to combine the strengths of both the OpenHermes and neural-chat models to create a powerful conversational AI system. Model inputs and outputs OpenHermes-2.5-neural-chat-v3-3-Slerp is a text-to-text model, meaning it takes a text prompt as input and generates a text response. The model is capable of handling a wide variety of prompts, from open-ended conversations to specific task-oriented queries. Inputs Text prompts**: The model accepts natural language text prompts that can cover a broad range of topics and tasks. Outputs Generated text**: The model produces fluent, coherent text responses that aim to be relevant and helpful given the input prompt. Capabilities The OpenHermes-2.5-neural-chat-v3-3-Slerp model demonstrates strong performance across a variety of benchmarks, including GPT4All, AGIEval, BigBench, and TruthfulQA. It outperforms previous versions of the OpenHermes model, as well as many other Mistral-based models. What can I use it for? The OpenHermes-2.5-neural-chat-v3-3-Slerp model can be used for a wide range of applications, including: Conversational AI**: The model can be used to power virtual assistants, chatbots, and other conversational interfaces, allowing users to engage in natural language interactions. Content generation**: The model can be used to generate a variety of text content, such as articles, stories, or creative writing. Task-oriented applications**: The model can be fine-tuned or used for specific tasks, such as question-answering, summarization, or code generation. Things to try Some interesting things to try with the OpenHermes-2.5-neural-chat-v3-3-Slerp model include: Exploring the model's capabilities in open-ended conversations, where you can engage it on a wide range of topics and see how it responds. Experimenting with different prompting strategies, such as using system prompts or ChatML templates, to see how the model's behavior and outputs change. Trying the model on specialized tasks, such as code generation or summarization, and evaluating its performance compared to other models. Comparing the performance of the different quantized versions of the model, such as the GGUF, GPTQ, and AWQ models, to find the best fit for your specific hardware and use case. By leveraging the capabilities of this powerful text generation model, you can unlock new possibilities for your AI-powered applications and projects.
Updated 9/6/2024