README.md

---
license: mit
tags:
- pytorch
- transformers
- text-generation
- hello-world
- educational
language:
- en
---

# Hello World Model

A minimal "Hello World" transformer model for demonstration purposes on Hugging Face.

## Model Description

This is a simple transformer-based language model that serves as a basic example for uploading models to Hugging Face. It demonstrates the minimum required files and structure for a custom model.

### Associated Dataset

This model works with the [chiedo/hello-world dataset](https://huggingface.co/datasets/chiedo/hello-world), which contains 20 examples of "Hello World" variations for demonstration purposes.

### Architecture Details
- **Model Type**: Custom Transformer (hello_world)
- **Vocabulary Size**: 13 tokens
- **Hidden Size**: 64 dimensions
- **Number of Layers**: 1 transformer encoder layer
- **Attention Heads**: 1
- **Intermediate Size**: 128
- **Max Position Embeddings**: 512
- **Activation Function**: GELU

## Files Included

- `config.json` - Model configuration
- `pytorch_model.bin` - Model weights (PyTorch format)
- `tokenizer.json` - Tokenizer vocabulary and settings
- `tokenizer_config.json` - Tokenizer configuration
- `model.py` - Model implementation (HelloWorldModel class with dataset loading methods)
- `test_model.py` - Test script for local validation
- `example_with_dataset.py` - Example script showing dataset integration

## Installation

### Using Virtual Environment (Recommended)

It's recommended to use a virtual environment to manage dependencies:

```bash
# Create a virtual environment
python -m venv venv

# Activate the virtual environment
# On macOS/Linux:
source venv/bin/activate
# On Windows:
# venv\Scripts\activate

# Install required packages
pip install torch transformers
```

### Direct Installation

If you prefer to install directly:

```bash
pip install torch transformers
```

## How to Use This Model - Complete Beginner's Guide

### Understanding How Hugging Face Models Work

When you use a model from Hugging Face, you have two options:
1. **Download automatically** - The model downloads itself when you run the code (easiest!)
2. **Download manually** - You download the files yourself and use them locally

### Method 1: Automatic Download (Easiest - No Manual Download Needed!)

The model will automatically download from Hugging Face when you run this code:

**Step 1:** Install the required libraries (one-time setup):
```bash
pip install torch transformers
```

**Step 2:** Create a new Python file on your computer (e.g., `test_model.py`):
```python
from transformers import AutoModel, AutoTokenizer

# This will AUTOMATICALLY download the model from Hugging Face!
# No need to manually download anything!
model_name = "chiedo/hello-world"  # Replace with your actual model name

print("Downloading model... (this happens only once)")
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
model = AutoModel.from_pretrained(model_name, trust_remote_code=True)

# Test the model
output = model.generate_hello_world()
print(output)  # "Hello World!"
```

**Step 3:** Run the script:
```bash
python test_model.py
```

**What happens behind the scenes:**
- The model files automatically download to `~/.cache/huggingface/hub/` (hidden folder)
- You don't need to know where they are - it just works!
- Next time you run it, it uses the cached version (no re-download)

### Method 2: Manual Download (If You Want the Files on Your Computer)

Want to see and control the actual model files? Here's how:

**Step 1:** Download the model files from Hugging Face:

**Option A: Using Git (Recommended)**
```bash
# Install git-lfs first (one time only)
git lfs install

# Clone the model repository
git clone https://huggingface.co/chiedo/hello-world
cd hello-world
```

**Option B: Download ZIP from website**
1. Go to https://huggingface.co/chiedo/hello-world
2. Click "Files and versions" tab
3. Click the download button to get all files as ZIP
4. Extract the ZIP to a folder on your computer

**Step 2:** Install required libraries:
```bash
pip install torch transformers
```

**Step 3:** Use the local model files:
```python
import sys
sys.path.append('/path/to/hello-world')  # Add the model folder to Python path

from model import HelloWorldModel, HelloWorldConfig
from transformers import PreTrainedTokenizerFast

# Load from local files
model_path = "/path/to/hello-world"  # Change this to your actual path!

config = HelloWorldConfig.from_pretrained(model_path)
model = HelloWorldModel.from_pretrained(model_path)
tokenizer = PreTrainedTokenizerFast.from_pretrained(model_path)

# Test it
output = model.generate_hello_world()
print(output)  # "Hello World!"
```

**Where to save the model folder:**
- Anywhere on your computer is fine!
- Common locations:
  - Windows: `C:\Users\YourName\Documents\models\hello-world`
  - Mac: `/Users/YourName/Documents/models/hello-world`
  - Linux: `/home/YourName/models/hello-world`

### Method 3: Using in Google Colab (Zero Setup Required!)

Perfect for beginners - no installation needed!

1. Go to https://colab.research.google.com
2. Click "New notebook"
3. Copy and paste this code:

```python
# Install dependencies (Colab needs this every time)
!pip install torch transformers

# Load and use the model (auto-downloads from Hugging Face!)
from transformers import AutoModel, AutoTokenizer

model_name = "chiedo/hello-world"
print("Downloading model from Hugging Face...")
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
model = AutoModel.from_pretrained(model_name, trust_remote_code=True)

# Test it
print("Testing model:")
print(model.generate_hello_world())
```

4. Click the "Play" button or press Shift+Enter to run

### FAQ - Frequently Asked Questions

**Q: Do I need to download the model files manually?**
A: No! The transformers library automatically downloads them when you use `from_pretrained()`

**Q: Where does the model download to?**
A: It downloads to a hidden cache folder (`~/.cache/huggingface/hub/`). You don't need to manage this.

**Q: How big is the download?**
A: This demo model is tiny (< 1 MB). Real models can be much larger (several GB).

**Q: Can I use this without internet?**
A: After the first download, yes! The model is cached locally.

**Q: What's the difference between this and pip install?**
A: `pip install` installs Python libraries. Hugging Face models aren't libraries - they're data files (weights, config, etc.) that get downloaded separately.

### What Does This Model Actually Do?

This is a demonstration model that:
- **Always outputs:** "Hello World!" when you call `generate_hello_world()`
- **Purpose:** Shows the minimum files needed to upload a model to Hugging Face
- **Not for real use:** It's like a "Hello World" program - just for learning!

### Common Issues and Solutions

**Issue: "ModuleNotFoundError: No module named 'transformers'"**
- Solution: Run `pip install transformers torch`

**Issue: "Can't load the model"**
- Solution: Make sure to include `trust_remote_code=True` parameter

**Issue: "Model not found"**
- Solution: Check the model name matches exactly (case-sensitive)

### More Examples

#### Example 1: Tokenizing Text
```python
# See how the model breaks down text into tokens
text = "Hello World"
tokens = tokenizer.encode(text)
print(f"Text '{text}' becomes tokens: {tokens}")

# Convert tokens back to text
decoded = tokenizer.decode(tokens)
print(f"Tokens {tokens} become text: '{decoded}'")
```

#### Example 2: Getting Model Predictions
```python
# Get raw predictions from the model
input_text = "Hello"
inputs = tokenizer(input_text, return_tensors="pt")

with torch.no_grad():
    outputs = model(**inputs)
    logits = outputs.logits
    print(f"Model output shape: {logits.shape}")
```

### Using the Model with Its Dataset

This model includes built-in methods to work with the [chiedo/hello-world dataset](https://huggingface.co/datasets/chiedo/hello-world):

#### Loading the Dataset Through the Model
```python
from transformers import AutoModel, AutoTokenizer
from datasets import load_dataset

# Load model and tokenizer
model = AutoModel.from_pretrained("chiedo/hello-world", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("chiedo/hello-world", trust_remote_code=True)

# Method 1: Use the model's built-in dataset loading
dataset = model.load_dataset("chiedo/hello-world")
print(f"Dataset splits: {list(dataset.keys())}")

# Method 2: Load dataset directly
dataset = load_dataset("chiedo/hello-world")

# Process a batch from the dataset
texts = dataset["train"]["text"][:5]
inputs = model.prepare_dataset_batch(texts, tokenizer)
outputs = model(**inputs)
```

#### Complete Example with Dataset
```python
# Run the full example script
python example_with_dataset.py
```

This will demonstrate:
- Loading the model and dataset
- Processing batches from the dataset
- Running inference on dataset examples
- Accessing dataset labels and features

## Model Vocabulary

The model includes a minimal vocabulary:
- Special tokens: `[PAD]`, `[UNK]`, `[CLS]`, `[SEP]`, `[MASK]`
- Content tokens: `Hello`, `World`, `!`, `hello`, `world`, `.`, `,`, `?`

## Training

This is a demonstration model and has not been trained on any dataset. The weights are randomly initialized using a normal distribution with standard deviation of 0.02.

## Testing

Run the included test script to verify the model works correctly:

```bash
# Make sure your virtual environment is activated if using one
# source venv/bin/activate  # On macOS/Linux
# venv\Scripts\activate     # On Windows

python test_model.py
```

## Uploading to Hugging Face

To upload this model to your Hugging Face account:

```bash
# Install huggingface-hub
pip install huggingface-hub

# Login to Hugging Face
huggingface-cli login

# Create a new model repository (if it doesn't exist)
huggingface-cli repo create hello-world-model --type model

# Upload all model files
huggingface-cli upload your-username/hello-world-model . --repo-type model
```

## Technical Details

- **Framework**: PyTorch
- **Transformers Version**: 4.36.0+
- **Python Version**: 3.6+
- **License**: MIT

## Limitations

- This model is for demonstration and educational purposes only
- Not trained on any real data
- Should not be used for production applications
- Limited vocabulary of 13 tokens
- Single layer architecture is too simple for real NLP tasks

## Citation

If you use this model as a template:

```bibtex
@misc{hello-world-model,
  title={Hello World Model - A Minimal Hugging Face Model Example},
  author={Your Name},
  year={2024},
  publisher={Hugging Face}
}
```

## License

MIT License - This model is open source and available for any use.

## Contact

For questions or issues with this demonstration model, please open an issue on the repository.