Instructions to use CraneAILabs/ganda-gemma-1b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use CraneAILabs/ganda-gemma-1b with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="CraneAILabs/ganda-gemma-1b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("CraneAILabs/ganda-gemma-1b")
model = AutoModelForCausalLM.from_pretrained("CraneAILabs/ganda-gemma-1b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use CraneAILabs/ganda-gemma-1b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "CraneAILabs/ganda-gemma-1b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "CraneAILabs/ganda-gemma-1b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/CraneAILabs/ganda-gemma-1b

SGLang

How to use CraneAILabs/ganda-gemma-1b with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "CraneAILabs/ganda-gemma-1b" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "CraneAILabs/ganda-gemma-1b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "CraneAILabs/ganda-gemma-1b" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "CraneAILabs/ganda-gemma-1b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use CraneAILabs/ganda-gemma-1b with Docker Model Runner:
```
docker model run hf.co/CraneAILabs/ganda-gemma-1b
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

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'])

🔗 Related Models

GGUF Quantizations: CraneAILabs/ganda-gemma-1b-GGUF - Optimized for llama.cpp/Ollama
Mobile (LiteRT): CraneAILabs/ganda-gemma-1b-litert - Optimized for Android/iOS

🎨 Use Cases

Translation Apps: Offline English-Luganda translation
Language Learning: Practice Luganda with instant feedback
Cultural Apps: Create culturally aware Luganda content
Educational Tools: Luganda learning assistants
Research: Natural language processing for Luganda
Content Creation: Generate Luganda content for media

⚠️ Limitations

Language Output: Responds only in Luganda
Context Length: Optimized for shorter conversational inputs
Cultural Context: May not capture all nuances of Luganda culture
Regional Variations: Trained on standard Luganda, may not reflect all dialects