CraneAILabs/ganda-gemma-1b-GGUF

Ganda Gemma 1B

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.

📊 Translation Performance

Model Comparison

Model	Parameters	BLEU	chrF++	Efficiency*
Gemma 3 4B	4B	1.1	20.05	0.28
Gemma 3 27B	27B	3.65	31.37	0.14
GPT-5 Mini	N/A	5.14	36.55	N/A
Ganda Gemma 1B	1B	6.99	40.32	6.99
Gemini 2.0 Flash	Large	7.94	43.38	N/A

*Efficiency = BLEU Score ÷ Parameters (in billions)

Key Performance Insights

🎯 Efficiency Leader: Achieves 6.99 BLEU per billion parameters (highest efficiency ratio)
🚀 Size Advantage: Outperforms Gemma 3 4B (4x larger) by 535% on BLEU score
💎 Competitive Quality: Achieves similar performance to GPT-5 Mini with known 1B parameter count
⚡ Practical Deployment: Runs efficiently on consumer hardware while maintaining quality

Evaluation Details

• Dataset: FLORES-200 English→Luganda (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/ganda-gemma-1b")
tokenizer = AutoTokenizer.from_pretrained("CraneAILabs/ganda-gemma-1b")

# Translate to Luganda
prompt = "Translate to Luganda: 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 + Luganda
• Output Language: Luganda only
• Primary Focus: English-to-Luganda translation and Luganda conversation

🎯 Capabilities

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

💻 Usage Examples

Basic Translation

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

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

# English to Luganda translation
prompt = "Translate to Luganda: Welcome to our school"
inputs = tokenizer(prompt, return_tensors="pt")

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_length=100,
        temperature=0.3,
        do_sample=True,
        pad_token_id=tokenizer.eos_token_id
    )

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

Luganda Conversation

# Direct Luganda conversation
prompt = "Oli otya! Osobola okuntuyamba leero?"
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/ganda-gemma-1b",
    tokenizer="CraneAILabs/ganda-gemma-1b",
    device=0 if torch.cuda.is_available() else -1
)

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

Ollama Usage

# Run with different quantizations
ollama run crane-ai-labs/ganda-gemma-1b:q4-k-m    # Recommended balance
ollama run crane-ai-labs/ganda-gemma-1b:q8-0      # Higher quality
ollama run crane-ai-labs/ganda-gemma-1b:q4-k-s    # Smaller size
ollama run crane-ai-labs/ganda-gemma-1b:f16       # Original quality

🚀 GGUF Advantages

• Fast CPU Inference: Optimized for CPU-only environments
• Memory Efficient: Multiple quantization levels available
• Easy Integration: Works with llama.cpp, Ollama, and other GGUF-compatible tools
• Cross-Platform: Supports Windows, macOS, and Linux

📦 Available Quantizations

Quantization	File Size	Memory Usage	Quality	Use Case
F16	~2.0GB	~3.5GB	Best	GPU inference
Q8_0	~1.1GB	~2.0GB	High	High-end CPU
Q4_K_M	~600MB	~1.2GB	Good	Recommended
Q4_K_S	~550MB	~1.1GB	Decent	Low memory
Q4_0	~550MB	~1.0GB	Basic	Very low memory

🔗 Related Models

• Original Model: CraneAILabs/ganda-gemma-1b - Full precision HuggingFace model
• Mobile (LiteRT): CraneAILabs/ganda-gemma-1b-litert - Optimized for Android/iOS

📄 License

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

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Built with ❤️ by Crane AI Labs

Ganda Gemma - Your helpful Luganda AI companion, optimized for CPU!