Instructions to use Sweaterdog/Andy-4-tiny-safetensors with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Local Apps
- Unsloth Studio new
How to use Sweaterdog/Andy-4-tiny-safetensors with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for Sweaterdog/Andy-4-tiny-safetensors to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for Sweaterdog/Andy-4-tiny-safetensors to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Sweaterdog/Andy-4-tiny-safetensors to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="Sweaterdog/Andy-4-tiny-safetensors", max_seq_length=2048, )
🧠 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-0703.
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: ~ 10 hours total
Data Volumes:
- Messages: 79,384
- Tokens: 425,535,198
- Conversations: 62,149
Base Architecture: SmolLM2
License: Andy 1.0 License
Repository: https://huggingface.co/Sweaterdog/Andy‑4
📊 Training Regimen
Andy‑4‑base-2 dataset
- Epochs: 1
- Learning Rate: 2e-5
- Dataset Size: 49.2k
Mindcraft-CE Cloud Logging dataset
- Epochs: 1
- Learning Rate: 7e-6
- Dataset Size: 13.13k
Fine‑tune (FT) dataset
- Epochs: 1
- Learning Rate: 1e-6
- Dataset Size: 9.12k
- Optimizer: AdamW_8bit with cosine decay
- Quantization: 4‑bit (
bnb-4bit) for inference - Warm Up Steps: 0.1% of each dataset
🚀 Installation
Andy-4-tiny is an Edge-case model, built to run on the CPU and use minimal ram (~1GB)
| Quantization | RAM Required |
|---|---|
| F16 | CPU |
| Q8_0 | CPU |
| Q4_K_M | CPU |
1. Installation directly on Ollama
THIS MODEL IS NOT YET ON OLLAMA*
- Visit Andy-4 on Ollama
- Copy the command after choosing model type / quantization
- Run the command in the terminal
- Set the profile's model to be what you installed, such as
ollama/sweaterdog/andy-4:tiny-q8_0
2. Manual Download & Modelfile
Download
- From the HF Files tab, grab your chosen
.GGUFquant weights (e.g.Andy-4-tiny.Q4_K_M.gguf). - Download the provided
Modelfile.
- From the HF Files tab, grab your chosen
Edit
Change
FROM YOUR/PATH/HEREto
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
- Create
ollama create andy-4-tiny -f Modelfile
This registers the Andy‑4-tiny model locally.
📌 Acknowledgments
Click to expand
- Data & Models by: @Sweaterdog
- Framework: Mindcraft (https://github.com/kolbytn/mindcraft)
⚖️ License
See Andy 1.0 License.
This work uses data and models created by @Sweaterdog.
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