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Model: CraneAILabs/ganda-gemma-1b Source: Original Platform
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README.md
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README.md
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---
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base_model: google/gemma-3-1b-it
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language:
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- en
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- lg
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library_name: transformers
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license: gemma
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tags:
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- luganda
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- translation
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- conversational
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- gemma
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- gemma3
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- fine-tuned
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pipeline_tag: text-generation
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---
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# Ganda Gemma 1B
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A fine-tuned Gemma 3 1B instruction model specialized for **English-to-Luganda translation and Luganda conversational AI**. The model accepts input in both English and Luganda but outputs responses exclusively in Luganda.
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## 📊 Translation Performance
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### Model Comparison
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| Model | Parameters | BLEU | chrF++ | Efficiency* |
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|-------|------------|------|--------|-----------|
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| Gemma 3 4B | 4B | 1.1 | 20.05 | 0.28 |
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| Gemma 3 27B | 27B | 3.65 | 31.37 | 0.14 |
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| GPT-5 Mini | N/A | 5.14 | 36.55 | N/A |
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| **Ganda Gemma 1B** | **1B** | **6.99** | **40.32** | **6.99** |
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| Gemini 2.0 Flash | Large | 7.94 | 43.38 | N/A |
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*Efficiency = BLEU Score ÷ Parameters (in billions)
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### Key Performance Insights
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🎯 **Efficiency Leader**: Achieves 6.99 BLEU per billion parameters (highest efficiency ratio)
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🚀 **Size Advantage**: Outperforms Gemma 3 4B (4x larger) by 535% on BLEU score
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💎 **Competitive Quality**: Achieves similar performance to GPT-5 Mini with known 1B parameter count
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⚡ **Practical Deployment**: Runs efficiently on consumer hardware while maintaining quality
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### Evaluation Details
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- **Dataset**: FLORES-200 English→Luganda (1,012 translation pairs)
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- **Metrics**: BLEU (bilingual evaluation understudy) and chrF++ (character F-score)
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- **Evaluation**: Zero-shot translation performance
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## 🚀 Quick Start
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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# Load model and tokenizer
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model = AutoModelForCausalLM.from_pretrained("CraneAILabs/ganda-gemma-1b")
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tokenizer = AutoTokenizer.from_pretrained("CraneAILabs/ganda-gemma-1b")
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# Translate to Luganda
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prompt = "Translate to Luganda: Hello, how are you today?"
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inputs = tokenizer(prompt, return_tensors="pt")
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outputs = model.generate(**inputs, max_length=100, temperature=0.3)
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response = tokenizer.decode(outputs[0], skip_special_tokens=True)
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print(response)
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```
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## 🌍 Language Capabilities
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- **Input Languages**: English + Luganda
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- **Output Language**: Luganda only
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- **Primary Focus**: English-to-Luganda translation and Luganda conversation
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## 🎯 Capabilities
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- **Translation**: English-to-Luganda translation
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- **Conversational AI**: Natural dialogue in Luganda
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- **Summarization**: Text summarization in Luganda
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- **Writing**: Creative and informational writing in Luganda
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- **Question Answering**: General knowledge responses in Luganda
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## 💻 Usage Examples
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### Basic Translation
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```python
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model = AutoModelForCausalLM.from_pretrained("CraneAILabs/ganda-gemma-1b")
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tokenizer = AutoTokenizer.from_pretrained("CraneAILabs/ganda-gemma-1b")
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# English to Luganda translation
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prompt = "Translate to Luganda: Welcome to our school"
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inputs = tokenizer(prompt, return_tensors="pt")
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with torch.no_grad():
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outputs = model.generate(
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**inputs,
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max_length=100,
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temperature=0.3,
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do_sample=True,
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pad_token_id=tokenizer.eos_token_id
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)
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response = tokenizer.decode(outputs[0], skip_special_tokens=True)
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print(response)
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```
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### Luganda Conversation
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```python
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# Direct Luganda conversation
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prompt = "Oli otya! Osobola okuntuyamba leero?"
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inputs = tokenizer(prompt, return_tensors="pt")
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outputs = model.generate(**inputs, max_length=100, temperature=0.3)
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response = tokenizer.decode(outputs[0], skip_special_tokens=True)
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print(response)
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```
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### Using the Pipeline
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```python
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from transformers import pipeline
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# Create a text generation pipeline
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generator = pipeline(
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"text-generation",
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model="CraneAILabs/ganda-gemma-1b",
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tokenizer="CraneAILabs/ganda-gemma-1b",
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device=0 if torch.cuda.is_available() else -1
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)
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# Generate Luganda text
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result = generator(
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"Translate to Luganda: Welcome to our school",
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max_length=100,
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temperature=0.3,
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do_sample=True
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)
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print(result[0]['generated_text'])
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```
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## 🔗 Related Models
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- **GGUF Quantizations**: [CraneAILabs/ganda-gemma-1b-GGUF](https://huggingface.co/CraneAILabs/ganda-gemma-1b-GGUF) - Optimized for llama.cpp/Ollama
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- **Mobile (LiteRT)**: [CraneAILabs/ganda-gemma-1b-litert](https://huggingface.co/CraneAILabs/ganda-gemma-1b-litert) - Optimized for Android/iOS
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## 🎨 Use Cases
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- **Translation Apps**: Offline English-Luganda translation
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- **Language Learning**: Practice Luganda with instant feedback
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- **Cultural Apps**: Create culturally aware Luganda content
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- **Educational Tools**: Luganda learning assistants
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- **Research**: Natural language processing for Luganda
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- **Content Creation**: Generate Luganda content for media
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## ⚠️ Limitations
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- **Language Output**: Responds only in Luganda
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- **Context Length**: Optimized for shorter conversational inputs
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- **Cultural Context**: May not capture all nuances of Luganda culture
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- **Regional Variations**: Trained on standard Luganda, may not reflect all dialects
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## 🛠️ Technical Details
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- **Base Model**: Google Gemma 3 1B Instruct
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- **Fine-tuning Method**: Supervised fine-tuning on English-Luganda pairs
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- **Context Length**: 2048 tokens
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- **Precision**: 16-bit floating point
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- **Framework**: Transformers (PyTorch)
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## 📄 License
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This model is released under the [Gemma Terms of Use](https://ai.google.dev/gemma/terms). Please review the terms before use.
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## 🙏 Acknowledgments
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- **Google**: For Gemma 3 base model and research
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- **Luganda Community**: For language resources and cultural guidance
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- **FLORES Team**: For evaluation dataset and benchmarking framework
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---
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**Built with ❤️ by Crane AI Labs**
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*Ganda Gemma - Your helpful Luganda AI companion!*
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