README.md

---
license: cc-by-nc-4.0
language:
- en
tags:
- text-generation
- smollm
- finetuned
- gguf
- small-model
- instruct
- potodoo
pipeline_tag: text-generation
base_model:
- HuggingFaceTB/SmolLM-135M-Instruct
---

#  Potodoo-V1-135M-Instruct

A tiny, efficient, and quirky instruction-following AI fine-tuned by Otto. Potodoo is built on the SmolLM-135M base model and optimized for edge devices, laptops, and mobile phones.

## 📋 Model Details

- **Model Name:** Potodoo-V1-135M-Instruct
- **Base Model:** [HuggingFaceTB/SmolLM-135M-Instruct](https://huggingface.co/HuggingFaceTB/SmolLM-135M-Instruct)
- **Parameters:** 135 Million
- **Architecture:** Llama-based (SmolLM)
- **Training Method:** LoRA (Low-Rank Adaptation) + Full Merge
- **Training Epochs:** 10 epochs
- **Final Training Loss:** ~1.13
- **Quantization:** F16 (269MB) / Q4_K_M (~100MB)
- **Context Length:** 2048 tokens
- **License:** CC BY-NC 4.0 (Non-Commercial)

## 🎯 Use Cases

Potodoo is designed for:
- ✅ Local chat applications on low-end hardware
- ✅ Educational projects and research
- ✅ Mobile and embedded AI assistants
- ✅ Coding help and explanations
- ✅ Fun conversations with a quirky personality
- ✅ Capybara facts (obviously 🦫)

##  Hardware Requirements

Potodoo runs on **minimal hardware**:
- **RAM:** 2GB+ (F16) or 512MB+ (Q4_K_M)
- **CPU:** Any modern x86_64 or ARM processor
- **GPU:** Not required (but supported via Metal/CUDA)
- **Storage:** ~100-300MB

**Will probrably run on:**
- iPhone X and newer
- Android phones with 8-core CPUs
- Laptops with 8GB RAM (Windows, macOS, Linux)
- Raspberry Pi 4/5

## 🚀 How to Use

### Using Ollama
```bash
ollama run potodoo-135m-instruct
```
## Using LM Studio

    Download the GGUF file
    Load it in LM Studio
    Start chatting!

## Using llama.cpp
```python
./main -m potodoo_v1_f16.gguf -p "Hello, Potodoo!" -n 128
```
## 📊 Training Details
### Dataset

    Custom curated dataset with 150 examples
    Focus on corporate-quirky conversational tone
    ChatML format with <|im_start|> and <|im_end|> tokens

### Training Configuration

    Framework: Unsloth + Hugging Face Transformers
    LoRA Rank: 16
    LoRA Alpha: 16
    Target Modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
    Batch Size: 4 (with gradient accumulation)
    Learning Rate: 2e-4 with cosine scheduler
    Warmup Steps: 10
    Optimizer: AdamW 8-bit
    Precision: FP16 (mixed precision training)

### Performance

    Training Loss: Started at ~2.5, ended at ~1.13
    Training Time: ~10-12 minutes on NVIDIA T4 GPU
    Convergence: Excellent for model size

## 🔧 Model Architecture

```
SmolLM-135M Architecture:
├── Hidden Size: 576
├── Intermediate Size: 1536
├── Num Attention Heads: 8
├── Num KV Heads: 4 (GQA)
├── Num Hidden Layers: 30
├── Vocab Size: 49,152
├── RoPE Theta: 10,000
└── RMS Norm Epsilon: 1e-06
```
## 📈 Limitations
- Size: At 135M parameters, this is a very small model. It may struggle with:

  - Complex reasoning tasks
  - Long-context understanding
  - Highly technical or specialized knowledge
  - Multi-step problem solving

- Language: Primarily trained on English data
- Knowledge Cutoff: Inherits base model's knowledge (2024)
- Bias: May exhibit biases present in training data
## ⚖️ License
This model is released under the **Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) License**.
**You are free to:**

- ✅ Share — copy and redistribute the material in any medium or format
- ✅ Adapt — remix, transform, and build upon the material
- ✅ Use for personal, educational, and research purposes

**Under the following terms:**

- 📝 **Attribution** — You must give appropriate credit to the original creator
- 🚫 **NonCommercial** — You may not use the material for commercial purposes

**Commercial use is strictly prohibited.** This includes but is not limited to:

- Selling access to this model
- Using it in commercial products or services
- Training other models on this fine-tuned version
- Any form of monetization

For commercial licensing inquiries, please contact the author.

## 👨‍💻 Author
**Otto11X**
*Fine-tuned as part of a school project on edge AI and model optimization*

## 🙏 Acknowledgments

- Base Model: HuggingFaceTB/SmolLM-135M-Instruct by Hugging Face
- Training Framework: Unsloth for fast fine-tuning
- Conversion: llama.cpp for GGUF conversion

## Random Fun Facts

- Potodoo was trained in less than 15 minutes
- The model is smaller than most smartphone photos
- It can run on a phone from 2017
- The name "Potodoo" comes from potato, as it can run in a potato! Get it? Hahaha... It was bullsh-t wasnt it?
- Training involved exactly 150 examples of corporate-quirky conversation
- The final loss of 1.13 is considered excellent for a 135M parameter model

**Built with ❤️ and a lot of debugging in Google Colab**