Sweaterdog/Andy-4-tiny-safetensors

🧠 Andy-4-tiny 🐜

Andy‑4-tiny is an 360 Million‑parameter specialist model tuned for Minecraft gameplay via the Mindcraft framework. The Current version of Andy-4-tiny is Andy-4-tiny-0522.

This is the Safetensors repository

⚠️ Certification:
Andy‑4 is not yet certified by the Mindcraft developers. Use in production at your own discretion.

🔍 Model Specifications

• Parameters: 360M

• Training Hardware: 1 × NVIDIA RTX 3070

• Duration: ~ 36 hours total

• Data Volumes:

  • Messages: 179,384
  • Tokens: 425,535,198
  • Conversations: 62,149

• Base Architecture: SmolLM2

• License: Andy 1.0 License

• Repository: https://huggingface.co/Sweaterdog/Andy‑4

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📊 Training Regimen

1. Andy‑4‑base‑1 dataset

   • Epochs: 2
   • Learning Rate: 5e-5
   • Dataset Size: 47.4k

2. Andy‑4‑base-2 dataset

   • Epochs: 2
   • Learning Rate: 7e-5
   • Dataset Size: 49.2k

3. Fine‑tune (FT) dataset

   • Epochs: 2.5
   • Learning Rate: 2e-5
   • Dataset Size: 4.12k

• Optimizer: AdamW_8bit with cosine decay
• Quantization: 4‑bit (bnb-4bit) for inference
• Warm Up Steps: 0.1% of each dataset

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🚀 Installation

Andy-4-tiny is an Edge-case model, built to run on the CPU and use minimal ram

Quantization	RAM Required
F16	CPU
Q8_0	CPU
Q4_K_M	CPU

1. Installation directly on Ollama

1. Visit Andy-4 on Ollama
2. Copy the command after choosing model type / quantization
3. Run the command in the terminal
4. Set the profile's model to be what you installed, such as ollama/sweaterdog/andy-4:tiny-q8_0

2. Manual Download & Modelfile

1. Download

   • From the HF Files tab, grab your chosen .GGUF quant weights (e.g. Andy-4-tiny.Q4_K_M.gguf).
   • Download the provided Modelfile.

2. Edit

   Change

   FROM YOUR/PATH/HERE
   

   to

   FROM /path/to/Andy-4-tiny.Q4_K_M.gguf
   

Optional: Increase the parameter num_ctx to a higher value for longer conversations if you:

A. Have extra VRAM

B. Quantized the context window

C. Can use a smaller model

3. Create

   ollama create andy-4-tiny -f Modelfile
   

This registers the Andy‑4-tiny model locally.

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📌 Acknowledgments

Click to expand

• Data & Models by: @Sweaterdog
• Framework: Mindcraft (https://github.com/kolbytn/mindcraft)
• LoRA Weights: https://huggingface.co/Sweaterdog/Andy-4-LoRA
• *Explicit credit is not granted to Meta since this model was trained off of a slightly different architecture, from DeepSeek-R1

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⚖️ License

See Andy 1.0 License.

This work uses data and models created by @Sweaterdog.