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Pankajmathur

Models by this creator

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orca_mini_3b

pankajmathur

Total Score

157

The orca_mini_3b model is an OpenLLaMa-3B model trained on a mix of datasets including WizardLM, Alpaca, and Dolly-V2. It applies the dataset construction approaches from the Orca Research Paper to create an "explain tuned" model designed to learn the thought process from the ChatGPT teacher model. Model inputs and outputs Inputs System prompt**: A short prompt provided at the start of the interaction that sets the context and instructions for the model. User instruction**: The specific task or query that the user wants the model to address. User input** (optional): Additional context or information provided by the user to help the model respond. Outputs Model response**: The generated text from the model addressing the user's instruction. The model aims to provide a well-reasoned and helpful response. Capabilities The orca_mini_3b model is capable of engaging in a wide variety of text-to-text tasks, such as question answering, task completion, and open-ended conversation. It demonstrates strong reasoning and explanatory capabilities, drawing insights from its training data to provide thoughtful and substantive responses. What can I use it for? The orca_mini_3b model could be useful for applications that require natural language understanding and generation, such as chatbots, virtual assistants, and content creation tools. Its ability to learn the thought process from ChatGPT makes it well-suited for tasks that benefit from clear, step-by-step explanations. Things to try One interesting aspect of the orca_mini_3b model is its use of a "system prompt" to set the context and instructions for the interaction. Experimenting with different system prompts could yield insights into how the model's responses change based on the framing and guidance provided upfront. Additionally, prompting the model with open-ended questions or tasks that require reasoning and analysis could reveal its strengths in those areas.

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Updated 5/16/2024

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orca_mini_13b

pankajmathur

Total Score

98

orca_mini_13b is an OpenLLaMa-13B model fine-tuned on explain-tuned datasets. The dataset was created using instructions and input from WizardLM, Alpaca, and Dolly-V2 datasets, applying approaches from the Orca Research Paper. This helps the model learn the thought process from the teacher model, which is the GPT-3.5-turbo-0301 version of ChatGPT. Model inputs and outputs The orca_mini_13b model takes a combination of system prompts and user instructions as input, and generates relevant text responses as output. Inputs System prompt**: A prompt that sets the context for the model, describing the role and goals of the AI assistant. User instruction**: The task or query that the user wants the model to address. Input (optional)**: Additional context or information that the user provides to help the model complete the task. Outputs Response**: The model's generated text response to the user's instruction, which aims to provide a detailed, thoughtful, and step-by-step explanation. Capabilities The orca_mini_13b model is capable of generating high-quality, explain-tuned responses to a variety of tasks and queries. It demonstrates strong performance on reasoning-based benchmarks like BigBench-Hard and AGIEval, indicating its ability to engage in complex, logical thinking. What can I use it for? The orca_mini_13b model can be used for a range of applications that require detailed, step-by-step explanations, such as: Educational or tutoring applications Technical support and customer service Research and analysis tasks General question-answering and information retrieval By leveraging the model's explain-tuned capabilities, users can gain a deeper understanding of the topics and concepts being discussed. Things to try One interesting thing to try with the orca_mini_13b model is to provide it with prompts or instructions that require it to take on different expert roles, such as a logician, mathematician, or physicist. This can help uncover the model's breadth of knowledge and its ability to tailor its responses to the specific needs of the task at hand. Another interesting approach is to explore the model's performance on open-ended, creative tasks, such as generating poetry or short stories. The model's strong grounding in language and reasoning may translate into an ability to produce engaging and insightful creative output.

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Updated 5/16/2024