| --- |
| 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** |
