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 / luganda_results.csv

Bronsn

Upload 3 files

d469553 verified 11 months ago

Raw

History Blame Contribute Delete

802 Bytes

	Rank,Model,Type,BLEU,chrF++,Our Model
	1,Google Translate,Commercial Service,9.27,46.44,FALSE
	2,Claude Sonnet 4,Anthropic,8.07,43.54,FALSE
	3,Gemini 2.0 Flash 001,Google,7.94,43.38,FALSE
	4,Gemini 2.5 Pro,Google,7.46,44.74,FALSE
	5,Ganda Gemma 1B (Our Model),Specialized Fine-tuned,6.99,40.32,TRUE
	6,Gemini 2.5 Flash,Google,6.28,40.51,FALSE
	7,Chatgpt 4o Latest,OpenAI,6.19,40,FALSE
	8,Gpt Oss 120B,OpenAI,5.16,34.7,FALSE
	9,Gpt 5 Mini,OpenAI,5.14,36.55,FALSE
	10,Gpt 5 Nano,OpenAI,4.93,32.86,FALSE
	11,Llama 4 Maverick,Meta,4.52,33.75,FALSE
	12,Gemma 3 27B,Google,3.65,31.37,FALSE
	13,Llama 4 Scout,Meta,3.59,27.63,FALSE
	14,Gpt Oss 20B,OpenAI,3.39,27.98,FALSE
	15,Gemma 3 4B,Google,1.1,20.05,FALSE
	16,Gemma 3N E4B,Google,0.84,17.6,FALSE
	17,Gemma 3 1B (vLLM Baseline),General Model,0.51,9.79,FALSE