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.

	## Contents

	### 📊 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