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", device_map="auto")
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 Settings

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
```

ganda-gemma-1b / EVALUATION.md

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Comprehensive FLORES Translation Evaluation Results

Overview

This package contains comprehensive evaluation results for English→Luganda and English→Swahili translation using the FLORES+ dataset. The evaluation includes specialized fine-tuned models, commercial services, and baseline models.

📊 Charts (`/charts/`)

luganda_comprehensive_chart.png - Complete Luganda translation performance comparison (17 models)
swahili_comprehensive_chart.png - Complete Swahili translation performance comparison (16 models)

📈 Data (`/data/`)

luganda_results.csv - Detailed Luganda evaluation results with rankings
swahili_results.csv - Detailed Swahili evaluation results with rankings
summary.csv - Executive summary of our models' performance

Key Results

🏆 Our Models Performance

Language	Model	Rank	BLEU	chrF++	Percentile	Efficiency (BLEU/B)
Luganda	Ganda Gemma 1B	5/17	6.99	40.32	76.5%	6.99
Swahili	Swahili Gemma 1B	12/16	27.59	56.84	31.2%	27.59

🎯 Key Insights

Language Resource Impact:

Swahili significantly outperforms Luganda (27.59 vs 6.99 BLEU)
Reflects the resource availability gap between the two languages
Demonstrates the challenge of low-resource language translation

Competitive Standing:

Luganda: Ranks 5th out of 17 models (76.5th percentile)
Swahili: Ranks 12th out of 16 models (31.2nd percentile)
Both models show excellent parameter efficiency

Baseline Comparison:

Our specialized models vastly outperform the general Gemma-3-1B baseline
Luganda: 6.99 vs 0.51 BLEU (13.8x improvement)
Swahili: 27.59 vs 2.78 BLEU (9.9x improvement)

Methodology

Dataset: FLORES+ devtest split (1,012 sentence pairs per language) Metrics: BLEU and chrF++ scores Evaluation: Comprehensive comparison across 17 different models/services Baseline: vLLM-served Gemma-3-1B-IT for fair comparison

Models Evaluated

Commercial Services

Google Translate (top performer in both languages)

Specialized Models (Ours)

Ganda Gemma 1B (fine-tuned for Luganda)
Swahili Gemma 1B (fine-tuned for Swahili)

General Models

Claude Sonnet 4, GPT variants, Gemini models, Llama models
Gemma-3-1B baseline (vLLM)

Files Description

Data Files

CSV Structure: Rank, Model, Type, Parameters (B), BLEU, chrF++, BLEU per Billion Params, Our Model
Rankings: Sorted by BLEU score (descending)
Efficiency: BLEU score per billion parameters for fair comparison

Charts

Visual comparison of all models with our models highlighted
Color coding: Red (BLEU), Black (chrF++)
Special marking: Diagonal stripes for our models

Evaluation Framework: FLORES+ English→African Languages

CraneAILabs
/

ganda-gemma-1b

Comprehensive FLORES Translation Evaluation Results

Overview

Contents

📊 Charts (`/charts/`)

📈 Data (`/data/`)

Key Results

🏆 Our Models Performance

🎯 Key Insights

Methodology

Models Evaluated

Commercial Services

Specialized Models (Ours)

General Models

Files Description

Data Files

Charts

Comprehensive FLORES Translation Evaluation Results

Overview

Contents

📊 Charts (/charts/)

📈 Data (/data/)

Key Results

🏆 Our Models Performance

🎯 Key Insights

Methodology

Models Evaluated

Commercial Services

Specialized Models (Ours)

General Models

Files Description

Data Files

Charts

📊 Charts (`/charts/`)

📈 Data (`/data/`)