Sweaterdog
/

Andy-4-base-DEPRECATED

+---
+license: apache-2.0
+datasets:
+- Sweaterdog/Andy-4-preview-1
+- Sweaterdog/Andy-4-preview-2
+language:
+- en
+base_model:
+- unsloth/DeepSeek-R1-Distill-Llama-8B-bnb-4bit
+tags:
+- gaming
+- minecraft
+---
+# 🧠 Andy-4-Preview 🧠
+Welcome to **Andy-4-Preview** – a revolutionary new model designed for playing Minecraft via the Mindcraft framework.
+This AI is crafted to push the boundaries of gameplay, reasoning, and multi-language summarization, making it one of the most versatile and powerful models available for Minecraft enthusiasts.
+# Overview
+Andy-4-Preview is an 8B parameter model built on the efficient Llama3.1 8B DeepSeek-R1 distill architecture.
+It has been meticulously trained over three weeks on a single RTX 3090 using two carefully curated datasets.
+The model underwent 2 epochs on the first dataset with a higher learning rate and 4 epochs on the second dataset with a much lower learning rate, ensuring a balanced and robust learning process.
+This training regimen, coupled with advanced techniques like manual learning rate adjustments and dynamic dataset modifications, has allowed Andy-4-Preview to outperform its competitors, including GPT-4o-mini, Claude 3.5 Haiku, Mineslayer v2, and the former leader, Andy-3.6.
+# Key Features
+- **Revolutionary Performance:** Specifically engineered for Minecraft gameplay, providing creative, strategic, and efficient in-game decision-making.
+- **Advanced Reasoning Capabilities:** If you include `Reason in your responses with <think> and </think>` in your prompt, or something similar, Andy-4-Preview will provide detailed reasoning to enhance performance, at the cost of speed.
+- **Multi-Language Summarization:** Capable of summarizing content in multiple languages, making it more efficient at remembering it's history.
+- **Building and Creativity:** Not only can it play Minecraft, but it also excels in constructing complex structures and solving intricate in-game challenges.
+- **Vast Knowledge Base:** Possesses extensive knowledge about Minecraft, including game mechanics, strategies, and creative builds.
+- **Open Source:** Completely open source and available to the community for further development and experimentation.
+- **Versatile Utility:** Excels in building, reasoning, summarizing, and strategic gameplay.
+# Open Source and Licensing
+Andy-4-Preview is 100% open source and is licensed under the [Apache 2.0 License](LICENSE). We believe in transparency and community collaboration, and all source code and training details are available for review and contribution.
+## LoRA Weights
+Access the LoRA weights for Andy-4-Preview here:
+[Andy-4-Preview LoRA Weights](https://huggingface.co/Sweaterdog/Andy-4-preview-LoRA)
+# Datasets
+The model was trained on two distinct datasets:
+- [Dataset 1: Andy-4-Preview-1](https://huggingface.co/datasets/Sweaterdog/Andy-4-preview-1) (trained for 2 epochs with a higher learning rate)
+- [Dataset 2: Andy-4-Preview-2](https://huggingface.co/datasets/Sweaterdog/Andy-4-preview-2) (trained for 4 epochs with a much lower learning rate)
+# Usage
+To integrate Andy-4-Preview with the Mindcraft framework, adjust your configuration settings as needed and follow the execution instructions provided separately.
+Customize the personality in the `conversing` profile section and configuration files to optimize performance for specific in-game tasks or environments.
+Andy-4-Preview is designed to seamlessly integrate with your existing Mindcraft setups, enhancing both gameplay and creative capabilities.
+# Mindcraft Project
+Download and explore the Mindcraft project on GitHub:
+[Mindcraft Project on GitHub](https://github.com/kolbytn/mindcraft)
+## How to use
+On Mindcraft, ensure you put `ollama/` before the model name, this can be something like `ollama/hf.co/sweaterdog/andy-4-preview:q5_k_m`
+## Notes on Quantization
+Typically, the smaller the quantization, such as Q2_K being the lowest, the more the model is to make a mistake, however the Q8_0 weights perform nearly identically to the F16 weights.
+For most cases, I suggest Q5_K_M or Q4_K_M, this is for 8GB GPUs, anything less and I would follow the guide on how to use free cloud computing on the discord.
+# Disclaimer
+**Important Notice:**
+Andy-4-Preview is a preview model and, while it represents a significant advancement in AI-driven Minecraft gameplay, please be aware of the following:
+- **Performance Variability:** Due to its experimental nature, the model may not always deliver ultimate performance in every scenario.
+- **Ongoing Development:** This preview release is intended for early testing and community feedback. You might encounter occasional inconsistencies or limitations as further refinements are made.
+- **Resource Constraints:** Training was performed on a single RTX 3090 over three weeks. Performance may vary on different hardware setups.
+# Community and Contact
+Join our vibrant community for discussions, support, and feedback:
+- **Discord (Mindcraft Server):** [Join the Mindcraft Discord](https://discord.gg/NGfCGs7aXJ)
+- **Huggingface Page:** Explore more experimental models and projects: [Sweaterdog on Huggingface](https://huggingface.co/Sweaterdog)
+- **Discord Username:** `Sweaterdog`
+We welcome your feedback, suggestions, and contributions as we continue to improve Andy-4-Preview and push the boundaries of AI in gaming.
+# Acknowledgements
+We extend our gratitude to all the developers and researchers who have contributed to the evolution of AI in gaming.