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---
datasets:
- Sweaterdog/Andy-4-base
- Sweaterdog/Andy-4-ft
- Sweaterdog/Andy-base-2
language:
- en
base_model:
- HuggingFaceTB/SmolLM2-360M-Instruct
tags:
- gaming
- minecraft
- mindcraft
---
# 🧠 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`.
These are the LoRA files for the model
> ⚠️ **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](LICENSE)
- **Repository:** https://huggingface.co/Sweaterdog/Andy‑4
---
## 📊 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
---
## 🚀 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](https://ollama.com/Sweaterdog/Andy-4)
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
```text
FROM YOUR/PATH/HERE
```
to
```text
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**
```bash
ollama create andy-4-tiny -f Modelfile
```
This registers the **Andy‑4-tiny** model locally.
---
## 📌 Acknowledgments
<details>
<summary>Click to expand</summary>
- **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](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Llama-8B)
</details>
---
## ⚖️ License
See [Andy 1.0 License](LICENSE).
*This work uses data and models created by @Sweaterdog.* |