Instructions to use Kira-Floris/TranslateGemma-4B-RW2EN with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Kira-Floris/TranslateGemma-4B-RW2EN with Transformers:

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

pipe = pipeline("image-text-to-text", model="Kira-Floris/TranslateGemma-4B-RW2EN")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("Kira-Floris/TranslateGemma-4B-RW2EN")
model = AutoModelForImageTextToText.from_pretrained("Kira-Floris/TranslateGemma-4B-RW2EN")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.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(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Kira-Floris/TranslateGemma-4B-RW2EN with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Kira-Floris/TranslateGemma-4B-RW2EN"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Kira-Floris/TranslateGemma-4B-RW2EN",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/Kira-Floris/TranslateGemma-4B-RW2EN

SGLang

How to use Kira-Floris/TranslateGemma-4B-RW2EN 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 "Kira-Floris/TranslateGemma-4B-RW2EN" \
    --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": "Kira-Floris/TranslateGemma-4B-RW2EN",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

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 "Kira-Floris/TranslateGemma-4B-RW2EN" \
        --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": "Kira-Floris/TranslateGemma-4B-RW2EN",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use Kira-Floris/TranslateGemma-4B-RW2EN with Docker Model Runner:
```
docker model run hf.co/Kira-Floris/TranslateGemma-4B-RW2EN
```

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sft_stage1_rw_en

This model is a fine-tuned version of google/translategemma-4b-it on the synthetic_rw_en_gemma__train and the expert_rw_en_gemma__train datasets. It achieves the following results on the evaluation set:

Loss: 0.7176

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 1e-05
train_batch_size: 1
eval_batch_size: 1
seed: 42
distributed_type: multi-GPU
num_devices: 7
gradient_accumulation_steps: 8
total_train_batch_size: 56
total_eval_batch_size: 7
optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.1
num_epochs: 1.0

Training results

Training Loss	Epoch	Step	Validation Loss
1.0598	0.0570	500	1.0485
0.9572	0.1139	1000	0.9877
0.952	0.1709	1500	0.9316
0.9124	0.2278	2000	0.8920
0.8772	0.2848	2500	0.8695
0.8855	0.3418	3000	0.8423
0.7964	0.3987	3500	0.8256
0.8643	0.4557	4000	0.8093
0.7972	0.5126	4500	0.7886
0.7519	0.5696	5000	0.7725
0.7513	0.6266	5500	0.7591
0.7877	0.6835	6000	0.7449
0.7322	0.7405	6500	0.7371
0.7346	0.7974	7000	0.7274
0.7603	0.8544	7500	0.7215
0.7201	0.9114	8000	0.7186
0.7025	0.9683	8500	0.7174

Framework versions

Transformers 4.57.1
Pytorch 2.9.1+cu128
Datasets 4.0.0
Tokenizers 0.22.2

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Safetensors

Model size

4B params

Tensor type

BF16

Model tree for Kira-Floris/TranslateGemma-4B-RW2EN

Base model

google/translategemma-4b-it

Finetuned

(17)

this model