Instructions to use Kira-Floris/TranslateGemma-4B-EN2RW with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Kira-Floris/TranslateGemma-4B-EN2RW 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-EN2RW")
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-EN2RW")
model = AutoModelForImageTextToText.from_pretrained("Kira-Floris/TranslateGemma-4B-EN2RW")
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-EN2RW 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-EN2RW"
# 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-EN2RW",
		"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-EN2RW

SGLang

How to use Kira-Floris/TranslateGemma-4B-EN2RW 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-EN2RW" \
    --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-EN2RW",
		"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-EN2RW" \
        --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-EN2RW",
		"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-EN2RW with Docker Model Runner:
```
docker model run hf.co/Kira-Floris/TranslateGemma-4B-EN2RW
```

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sft_stage2_lr

This model is a fine-tuned version of saves/translategemma3-4b/sft on the expert_en_rw_gemma__train dataset. It achieves the following results on the evaluation set:

Loss: 0.3614

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-06
train_batch_size: 1
eval_batch_size: 1
seed: 42
distributed_type: multi-GPU
num_devices: 6
gradient_accumulation_steps: 8
total_train_batch_size: 48
total_eval_batch_size: 6
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
0.3937	0.0682	500	0.3737
0.3516	0.1363	1000	0.3694
0.3791	0.2045	1500	0.3690
0.3863	0.2727	2000	0.3664
0.342	0.3409	2500	0.3654
0.3528	0.4090	3000	0.3647
0.3563	0.4772	3500	0.3631
0.3877	0.5454	4000	0.3630
0.3611	0.6136	4500	0.3621
0.4012	0.6817	5000	0.3622
0.3738	0.7499	5500	0.3614
0.4021	0.8181	6000	0.3611
0.378	0.8863	6500	0.3613
0.3636	0.9544	7000	0.3613

Framework versions

Transformers 4.57.1
Pytorch 2.9.1+cu128
Datasets 4.0.0
Tokenizers 0.22.2

Downloads last month: -

Safetensors

Model size

4B params

Tensor type

BF16

Model tree for Kira-Floris/TranslateGemma-4B-EN2RW

Base model

google/translategemma-4b-it

Finetuned

(21)

this model