Instructions to use prithivMLmods/translategemma-4b-it-f32-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use prithivMLmods/translategemma-4b-it-f32-GGUF with Transformers:

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

pipe = pipeline("image-text-to-text", model="prithivMLmods/translategemma-4b-it-f32-GGUF")
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 AutoModel
model = AutoModel.from_pretrained("prithivMLmods/translategemma-4b-it-f32-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use prithivMLmods/translategemma-4b-it-f32-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf prithivMLmods/translategemma-4b-it-f32-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf prithivMLmods/translategemma-4b-it-f32-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf prithivMLmods/translategemma-4b-it-f32-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf prithivMLmods/translategemma-4b-it-f32-GGUF:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf prithivMLmods/translategemma-4b-it-f32-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf prithivMLmods/translategemma-4b-it-f32-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf prithivMLmods/translategemma-4b-it-f32-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf prithivMLmods/translategemma-4b-it-f32-GGUF:Q4_K_M

Use Docker

docker model run hf.co/prithivMLmods/translategemma-4b-it-f32-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use prithivMLmods/translategemma-4b-it-f32-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "prithivMLmods/translategemma-4b-it-f32-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "prithivMLmods/translategemma-4b-it-f32-GGUF",
		"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/prithivMLmods/translategemma-4b-it-f32-GGUF:Q4_K_M

SGLang

How to use prithivMLmods/translategemma-4b-it-f32-GGUF 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 "prithivMLmods/translategemma-4b-it-f32-GGUF" \
    --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": "prithivMLmods/translategemma-4b-it-f32-GGUF",
		"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 "prithivMLmods/translategemma-4b-it-f32-GGUF" \
        --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": "prithivMLmods/translategemma-4b-it-f32-GGUF",
		"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"
						}
					}
				]
			}
		]
	}'

Ollama
How to use prithivMLmods/translategemma-4b-it-f32-GGUF with Ollama:
```
ollama run hf.co/prithivMLmods/translategemma-4b-it-f32-GGUF:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use prithivMLmods/translategemma-4b-it-f32-GGUF with Docker Model Runner:
```
docker model run hf.co/prithivMLmods/translategemma-4b-it-f32-GGUF:Q4_K_M
```

Lemonade

How to use prithivMLmods/translategemma-4b-it-f32-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull prithivMLmods/translategemma-4b-it-f32-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.translategemma-4b-it-f32-GGUF-Q4_K_M

List all available models

lemonade list

Atomic Chat

translategemma-4b-it-f32-GGUF

TranslateGemma-4b-it from Google is a lightweight 4 billion-parameter instruction-tuned translation model from the TranslateGemma family, built on Gemma 3 architecture and trained on TPUv4p/v5p/v5e hardware using supervised fine-tuning (SFT) on human/Gemini-synthesized parallel corpora across 500+ language pairs followed by RL optimization with MetricX-QE and AutoMQM reward models, enabling high-fidelity text-to-text and image-to-text translation across 55 languages (including Spanish, French, Chinese, Hindi, Arabic, and low-resource ones) with a 2K-128K token context window and 896x896 image resolution support (256 tokens/image). Optimized for edge/mobile deployment on laptops/desktops/cloud with half the parameters of baselines yet rivaling 12B models (MetricX↓=5.32, COMET↑=81.6 on WMT24++ across 55 langs), it uses a highly opinionated JSON chat template requiring source_lang_code/target_lang_code and processes inputs like {"type": "text/image_url", "text"/"url": content} for outputs in target languages, democratizing state-of-the-art translation for developers via Transformers/vLLM. Available in 4B (mobile/edge), 12B (laptops), and 27B (cloud/H100) sizes under open license, it prioritizes efficiency, throughput, and low latency without quality compromise for multilingual/multimodal communication.

translategemma-4b-it [GGUF]

File Name	Quant Type	File Size	File Link
translategemma-4b-it.IQ4_XS.gguf	IQ4_XS	2.28 GB	Download
translategemma-4b-it.Q2_K.gguf	Q2_K	1.73 GB	Download
translategemma-4b-it.Q3_K_L.gguf	Q3_K_L	2.24 GB	Download
translategemma-4b-it.Q3_K_M.gguf	Q3_K_M	2.1 GB	Download
translategemma-4b-it.Q3_K_S.gguf	Q3_K_S	1.94 GB	Download
translategemma-4b-it.Q4_K_M.gguf	Q4_K_M	2.49 GB	Download
translategemma-4b-it.Q4_K_S.gguf	Q4_K_S	2.38 GB	Download
translategemma-4b-it.Q5_K_M.gguf	Q5_K_M	2.83 GB	Download
translategemma-4b-it.Q5_K_S.gguf	Q5_K_S	2.76 GB	Download
translategemma-4b-it.Q6_K.gguf	Q6_K	3.19 GB	Download
translategemma-4b-it.Q8_0.gguf	Q8_0	4.13 GB	Download
translategemma-4b-it.f16.gguf	F16	7.77 GB	Download
translategemma-4b-it.f32.gguf	F32	15.5 GB	Download
translategemma-4b-it.mmproj-Q8_0.gguf	mmproj-Q8_0	591 MB	Download
translategemma-4b-it.mmproj-f16.gguf	mmproj-f16	851 MB	Download
translategemma-4b-it.mmproj-f32.gguf	mmproj-f32	1.67 GB	Download

Quants Usage

(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)

Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better):

Downloads last month: 880

GGUF

Model size

4B params

Architecture

gemma3

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

32-bit

Model tree for prithivMLmods/translategemma-4b-it-f32-GGUF

Base model

google/translategemma-4b-it

Quantized

(43)

this model