llama-3-cat-8b-instruct-v1
Maintainer: TheSkullery
47
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| Property | Value |
|---|---|
| Run this model | Run on HuggingFace |
| API spec | View on HuggingFace |
| Github link | No Github link provided |
| Paper link | No paper link provided |
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Model overview
llama-3-cat-8b-instruct-v1 is a Llama 3 8B model that has been finetuned by TheSkullery to focus on system prompt fidelity, helpfulness, and character engagement. The model aims to respect the system prompt to an extreme degree, provide helpful information regardless of the situation, and offer maximum character immersion (role-play) in the given scenes.
This model can be contrasted with similar 70B variants like the Cat-Llama-3-70B-instruct model, which was also trained by Dr. Kal'tsit and posted by Turboderp. The llama-3-cat-8b-instruct-v1 model is smaller but likely more focused on the specific goals outlined above.
Model inputs and outputs
Inputs
- Text prompts following the Llama 3 preset format, which includes a system prompt, user prompt, and assistant response.
Outputs
- Textual responses generated by the model following the provided prompts and system settings. The model aims to produce helpful, detailed, and engaging responses.
Capabilities
The llama-3-cat-8b-instruct-v1 model excels at following detailed system prompts, providing thoughtful and multi-step responses (chain-of-thought), and roleplaying engaging characters. It is particularly well-suited for tasks that require respecting system constraints, offering helpful information, and immersing the user in a specific scenario or persona.
What can I use it for?
This model could be useful for a variety of conversational AI applications that require a high degree of system prompt fidelity and helpful, engaged responses. Some potential use cases include:
- Virtual assistants or chatbots that need to strictly adhere to system settings and provide detailed, thoughtful responses
- Interactive fiction or roleplaying experiences where the AI needs to deeply embody a specific character
- Educational or informational applications that require the AI to provide thorough, multi-step explanations
Things to try
One interesting aspect of this model is its emphasis on chain-of-thought responses. You could try providing it with prompts that require step-by-step reasoning or analysis, and see how it breaks down and explains the problem-solving process. Additionally, experimenting with different system prompts that set the tone or personality of the AI could yield engaging and unexpected interactions.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
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The Llama-3-8B-Instruct-Coder model is an AI language model developed by Meta and uploaded by the Hugging Face user rombodawg. This model is based on the Llama-3 family of large language models and has been fine-tuned on the CodeFeedback dataset, making it specialized for coding tasks. It was trained using the Qalore method, a new training technique developed by rombodawg's colleague at Replete-AI that allows the model to be loaded on 14.5 GB of VRAM. This is a significant improvement compared to previous Llama models, which required more VRAM. The Replete-AI community, which rombodawg is a part of, is very supportive and welcoming, as described on their Discord server. Model inputs and outputs The Llama-3-8B-Instruct-Coder model is a text-to-text model, meaning it takes text as input and generates text as output. The model is particularly adept at understanding and generating code, thanks to its fine-tuning on the CodeFeedback dataset. Inputs Text**: The model can accept a variety of text-based inputs, such as natural language instructions, coding prompts, or existing code snippets. Outputs Text**: The model will generate text-based outputs, which can include code, explanations, or responses to the given input. Capabilities The Llama-3-8B-Instruct-Coder model excels at a variety of coding-related tasks, such as code completion, code generation, and code understanding. It can be used to help developers write and debug code, as well as to generate new code based on natural language descriptions. The model's capabilities have been further enhanced by the Qalore training method, which has improved its performance and efficiency. What can I use it for? The Llama-3-8B-Instruct-Coder model can be a valuable tool for developers, programmers, and anyone working with code. It can be used to automate repetitive coding tasks, generate boilerplate code, or even create entire applications based on high-level requirements. The model's ability to understand and generate code also makes it useful for educational purposes, such as helping students learn programming concepts or providing feedback on their code. Things to try One interesting thing to try with the Llama-3-8B-Instruct-Coder model is to provide it with a natural language description of a coding problem and see how it responds. You can then compare the generated code to your own solution or to the expected output, and use the model's feedback to improve your understanding of the problem and the programming concepts involved.
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