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OttoCapi/Potodoo-V1-135M-Instruct

sourceHugging Facecc-by-nc-4.0updated 2mo agoView on Hugging Face
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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
  • —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) / Q4KM (~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+ (Q4KM)
  • —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 <|imstart|> and <|imend|> tokens

Training Configuration

Framework: Unsloth + Hugging Face Transformers LoRA Rank: 16 LoRA Alpha: 16 Target Modules: qproj, kproj, vproj, oproj, gateproj, upproj, 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