Swahili Gemma 1B

A fine-tuned Gemma 3 1B instruction model specialized for English-to-Swahili translation and Swahili conversational AI. The model accepts input in both English and Swahili but outputs responses exclusively in Swahili.

📊 Translation Performance

Model Comparison

*Efficiency = BLEU Score / Parameters (in billions)

Key Performance Insights

🎯 Efficiency Leader: Achieves the highest BLEU-to-parameter ratio (27.6 BLEU per billion parameters)
🚀 Size Advantage: Outperforms Gemma 3 4B (4x larger) by 153% on BLEU score
💎 Competitive Quality: Achieves 94% of Gemma 3 27B performance with 27x fewer parameters
⚡ Practical Deployment: Runs efficiently on consumer hardware while maintaining quality

Evaluation Details

• Dataset: FLORES-200 English→Swahili (1,012 translation pairs)
• Metrics: BLEU (bilingual evaluation understudy) and chrF++ (character F-score)
• Evaluation: Zero-shot translation performance

🚀 Quick Start

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained("CraneAILabs/swahili-gemma-1b")
tokenizer = AutoTokenizer.from_pretrained("CraneAILabs/swahili-gemma-1b")

# Translate to Swahili
prompt = "Translate to Swahili: Hello, how are you today?"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=100, temperature=0.3)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)

🌍 Language Capabilities

• Input Languages: English + Swahili
• Output Language: Swahili only
• Primary Focus: English-to-Swahili translation and Swahili conversation

🎯 Capabilities

• Translation: English-to-Swahili translation
• Conversational AI: Natural dialogue in Swahili
• Summarization: Text summarization in Swahili
• Writing: Creative and informational writing in Swahili
• Question Answering: General knowledge responses in Swahili

💻 Usage Examples

Basic Translation

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("CraneAILabs/swahili-gemma-1b")
tokenizer = AutoTokenizer.from_pretrained("CraneAILabs/swahili-gemma-1b")

# English to Swahili translation
prompt = "Translate to Swahili: Good morning, how did you sleep?"
inputs = tokenizer(prompt, return_tensors="pt")

with torch.no_grad():
    outputs = model.generate(
        inputs.input_ids,
        max_length=128,
        temperature=0.3,
        top_p=0.95,
        top_k=64,
        repetition_penalty=1.1,
        do_sample=True,
        pad_token_id=tokenizer.eos_token_id
    )

response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)

Swahili Conversation

# Direct Swahili conversation
prompt = "Hujambo! Je, unaweza kunisaidia leo?"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=100, temperature=0.3)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)

Using the Pipeline

from transformers import pipeline

# Create a text generation pipeline
generator = pipeline(
    "text-generation",
    model="CraneAILabs/swahili-gemma-1b",
    tokenizer="CraneAILabs/swahili-gemma-1b",
    device=0 if torch.cuda.is_available() else -1
)

# Generate Swahili text
result = generator(
    "Translate to Swahili: Welcome to our school",
    max_length=100,
    temperature=0.3,
    do_sample=True
)
print(result[0]['generated_text'])

Ollama Usage

# Run the recommended Q4_K_M quantization
ollama run crane-ai-labs/swahili-gemma-1b:q4-k-m

# Try different quantizations based on your needs
ollama run crane-ai-labs/swahili-gemma-1b:q8-0    # Higher quality
ollama run crane-ai-labs/swahili-gemma-1b:q4-k-s  # Smaller size
ollama run crane-ai-labs/swahili-gemma-1b:f16     # Original quality

Available Quantizations

💡 Generation Parameters

Recommended settings for optimal results:

generation_config = {
    "temperature": 0.3,      # Focused, coherent responses
    "top_p": 0.95,          # Nucleus sampling
    "top_k": 64,            # Top-k sampling
    "max_length": 128,      # Response length limit
    "repetition_penalty": 1.1,  # Reduces repetition
    "do_sample": True,
    "pad_token_id": tokenizer.eos_token_id
}

🔗 Related Models

• GGUF Quantizations: CraneAILabs/swahili-gemma-1b-GGUF - Optimized for llama.cpp/Ollama
• LiteRT Mobile: CraneAILabs/swahili-gemma-1b-litert - Mobile deployment
• Ollama: crane-ai-labs/swahili-gemma-1b - Ready-to-run with Ollama

🎨 Use Cases

• Language Learning: Practice English-Swahili translation
• Cultural Preservation: Create and document Swahili content
• Educational Tools: Swahili learning assistants
• Content Localization: Translate materials to Swahili
• Conversational Practice: Improve Swahili dialogue skills
• Text Summarization: Summarize content in Swahili

⚠️ Limitations

• Language Output: Responds only in Swahili
• Factual Knowledge: General knowledge only, not trained on specific factual datasets
• No Coding/Math: Not designed for programming or mathematical tasks
• Context Length: Limited to 4,096 tokens for optimal performance
• Specialized Domains: May require domain-specific fine-tuning

📄 License

This model is released under the Gemma Terms of Use. Please review the terms before use.

🙏 Acknowledgments

• Google: For the Gemma 3 base model, support and guidance.
• Community: For Swahili language resources and datasets
• Gilbert Korir (Msingi AI, Nairobi, Kenya)
• Alfred Malengo Kondoro (Hanyang University, Seoul, South Korea)

Citation

If you use this model in your research or applications, please cite:

@misc{crane_ai_labs_2025,
    author    = {Bakunga Bronson and Kato Steven Mubiru and Lwanga Caleb and Gimei Alex and Kavuma Lameck and Roland Ganafa and Sibomana Glorry and Atuhaire Collins and JohnRoy Nangeso and Tukamushaba Catherine},
    title     = {Swahili Gemma: A Fine-tuned Gemma 3 1B Model for Swahili conversational AI},
    year      = {2025},
    url       = {https://huggingface.co/CraneAILabs/swahili-gemma-1b},
    organization = {Crane AI Labs}
}

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Swahili Gemma - Your helpful Swahili AI companion