Steelstorage

llama-3-cat-8b-instruct-v1

SteelStorage

47

llama-3-cat-8b-instruct-v1 is a variant of the Llama 3 language model developed by SteelStorage, a researcher on the Hugging Face platform. This model is an 8 billion parameter version of the Llama 3 language model that has been fine-tuned for instruction following, helpfulness, and character engagement. The model was developed by a team including dataset builder Dr. Kal'tsit, trainer/funder SteelSkull, and facilitator Potatooff. It was trained using a combination of techniques including supervised fine-tuning, rejection sampling, proximal policy optimization, and direct policy optimization. The training data includes high-quality instruction-response pairs from the Hugging Face dataset as well as specialized health-related data from the Chat Doctor dataset. Similar models include the 70B variant of this model developed by Dr. Kal'tsit and posted by Turboderp, as well as other Llama 3 models from the Meta-Llama project. Model Inputs and Outputs Inputs Text prompt containing instructions, context, and/or a query Outputs Generated text response that follows the provided instructions and context, demonstrating helpfulness and character engagement Capabilities The llama-3-cat-8b-instruct-v1 model is particularly adept at: Faithfully following system prompts and instructions Engaging in multi-step "chain of thought" reasoning to solve complex tasks Immersing the user in a character or role-playing scenario Providing helpful information on topics like biosciences and general science What Can I Use It For? This model could be useful for a variety of applications that require an AI assistant to be highly responsive to instructions, helpful, and engaging. Some potential use cases include: Virtual assistant for customer service or research support Interactive educational or training tool Creative writing aid or story generation Scientific research and analysis assistant SteelStorage's profile on Hugging Face provides more information on the researchers behind this model. Things to Try One interesting aspect of this model is its ability to provide detailed "chain of thought" explanations as it solves complex tasks. You could try giving it challenging prompts that require multi-step reasoning, and observe how it walks through the problem-solving process. Additionally, experimenting with different system prompt setups could allow you to explore the model's capacity for character immersion and role-playing.

Read more