---
language:
  - de
license: mit
library_name: transformers
pipeline_tag: text2text-generation
tags:
  - llama
  - translation
  - german
  - dialect
  - swabian
  - qlora
  - dpo
datasets:
  - custom
model-index:
  - name: swabian-german-translator
    results:
      - task:
          type: translation
          name: German-Swabian Translation
        metrics:
          - type: accuracy
            value: 0.8
            name: Training Loss
          - type: bleu
            value: N/A
            name: BLEU Score
metadata:
  author: [Your Name]
  framework: pytorch
  fine_tuning_type: 
    - dpo
    - qlora
base_model: llama-3.1-8b
training_data: Custom dataset based on Schwäbisch-Schwätza wordbook
training_processes:
  - sft
  - dpo
---

# Swabian-German Translation Model (DPO-Enhanced)

This model fine-tunes LLAMA 3.1 8B for bidirectional translation between Standard German and Swabian dialect, enhanced through Direct Preference Optimization (DPO).

## Model Details
- Base Model: LLAMA 3.1 8B
- Training Method: Two-stage fine-tuning (SFT + DPO)
- Training Data: 12,000+ word-pair translations with contextual sentences
- Hardware Requirements: Compatible with single-GPU setups (thanks to QLoRA)

## Intended Use
- Translating between Standard German and Swabian dialect
- Understanding and preserving regional linguistic variations
- Educational purposes for language learners

## Usage

### Basic Translation

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

# Load model and tokenizer
model_name = "your-username/swabian-translator-dpo"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Example translation from Swabian to Standard German
def translate(text, direction="to_german"):
    if direction == "to_german":
        prompt = f"Übersetze ins Hochdeutsche: {text}"
    else:
        prompt = f"Übersetze ins Schwäbische: {text}"
    
    inputs = tokenizer(prompt, return_tensors="pt")
    outputs = model.generate(**inputs, max_length=100)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example usage
swabian_text = "Du hosch ja a blaus Mol am Arm!"
german_translation = translate(swabian_text, "to_german")
print(german_translation)  # Expected: "Du hast ja einen Bluterguss am Arm!"
```

### Translation Examples

Swabian to German:
```
Input: "I han koi Zeit"
Output: "Ich habe keine Zeit"

Input: "Des goht et"
Output: "Das geht nicht"

Input: "Wo bisch du her komma?"
Output: "Woher kommst du?"
```

German to Swabian:
```
Input: "Ich verstehe das nicht"
Output: "I versteh des et"

Input: "Das schmeckt sehr gut"
Output: "Des schmeckt arg guat"
```

## Model Architecture & Training

### Training Process
1. **Initial Dataset Preparation**
   - Base dataset: 12,000+ word pairs from Schwäbisch-Schwätza wordbook
   - Context enhancement using LLM-generated sentences
   - Manual verification and cleanup

2. **SFT (Supervised Fine-Tuning)**
   - QLoRA implementation for efficient training
   - 2 epochs on the complete dataset
   - Loss convergence at ~0.8

3. **DPO (Direct Preference Optimization)**
   - 300 carefully curated preference pairs
   - 3 epochs of preference learning
   - Focus on natural and accurate translations

### Technical Implementation
- Quantized training using QLoRA
- 4-bit precision for efficient resource usage
- Training framework: UnslothAI
- Single GPU training (~16GB VRAM required)

## Limitations and Considerations

1. **Dialect Variations**
   - Swabian varies significantly by region
   - Model focuses on common/standard Swabian expressions
   - May not capture all local variations

2. **Translation Quality**
   - Best performance on common phrases and expressions
   - May struggle with very colloquial or context-dependent translations
   - Not recommended for official or legal translations

3. **Technical Limitations**
   - Input length limited to 512 tokens
   - Generation speed affected by quantization
   - Memory requirements: ~8GB RAM minimum

## Community and Contributions

We welcome community contributions to improve the model:
- Additional training data
- Regional variant documentation
- Bug reports and fixes
- Performance improvements

Please submit issues or pull requests through the Hugging Face repository.

## Citation and Attribution

```bibtex
@misc{swabian-german-translator,
  author = {[Your Name]},
  title = {Swabian-German Translation Model},
  year = {2024},
  publisher = {Hugging Face},
  journal = {Hugging Face Model Hub}
}
```

## License
This project is licensed under the MIT License - see the LICENSE file for details.

## Acknowledgments
- Original dictionary data: [schwäbisch-schwätza.de](http://xn--schwbisch-schwtza-tqbk.de/)
- UnslothAI for the training framework
- LLAMA 3.1 8B base model