Instructions to use 17slever17/translate-gemma-4-sub-e2b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use 17slever17/translate-gemma-4-sub-e2b with Transformers:

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

pipe = pipeline("image-text-to-text", model="17slever17/translate-gemma-4-sub-e2b")
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, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("17slever17/translate-gemma-4-sub-e2b")
model = AutoModelForMultimodalLM.from_pretrained("17slever17/translate-gemma-4-sub-e2b", device_map="auto")
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 Settings

vLLM

How to use 17slever17/translate-gemma-4-sub-e2b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "17slever17/translate-gemma-4-sub-e2b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "17slever17/translate-gemma-4-sub-e2b",
		"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/17slever17/translate-gemma-4-sub-e2b

SGLang

How to use 17slever17/translate-gemma-4-sub-e2b 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 "17slever17/translate-gemma-4-sub-e2b" \
    --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": "17slever17/translate-gemma-4-sub-e2b",
		"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 "17slever17/translate-gemma-4-sub-e2b" \
        --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": "17slever17/translate-gemma-4-sub-e2b",
		"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"
						}
					}
				]
			}
		]
	}'

Unsloth Studio

How to use 17slever17/translate-gemma-4-sub-e2b with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for 17slever17/translate-gemma-4-sub-e2b to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for 17slever17/translate-gemma-4-sub-e2b to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for 17slever17/translate-gemma-4-sub-e2b to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="17slever17/translate-gemma-4-sub-e2b",
    max_seq_length=2048,
)

Docker Model Runner
How to use 17slever17/translate-gemma-4-sub-e2b with Docker Model Runner:
```
docker model run hf.co/17slever17/translate-gemma-4-sub-e2b
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Translate Gemma 4 Sub

Translate Gemma 4 Sub is a multilingual Gemma 4 fine-tune specialized for both general translation and context-aware subtitle translation.

👉🏻 The models are used by SubWave, an open-source realtime subtitle translator. 👈🏻

The model supports ordinary source-to-target translation as well as subtitle translation with previous source and translated segments provided as context. It is optimized to preserve meaning, tone, slang, uncertainty, repetitions, incomplete speech, and natural/official conversational style.

Why not use google/translategemma-4b-it?

Subtitle translation often requires previous context because phrases may be split across segments or lose their meaning when translated in isolation. Translate Gemma 4 Sub can use previous source and translated subtitles as context, while also supporting custom style instructions, speaker information, terminology rules, glossaries, and other translation constraints.

Other instruction-following translation models, such as tencent/Hy-MT2-7B, performed even worse than the base Gemma 4 model in our evaluation.

Available formats

E4B:

Transformers: Translate Gemma 4 Sub E4B
GGUF: Translate Gemma 4 Sub E4B GGUF

E2B:

Transformers: Translate Gemma 4 Sub E2B
GGUF: Translate Gemma 4 Sub E2B GGUF

Intended use

The model is especially optimized for live and conversational speech, including streaming content, where meaning often depends on previous lines, speaker intent, tone, and incomplete context. It performs particularly well when previous source and translated segments are supplied and can follow additional user instructions that define the desired style, tone, or level of formality.

Translate Gemma 4 Sub retains the language coverage of the underlying Gemma 4 model. The most extensively trained languages are:

Tier 1: English, Russian, Spanish, German and Japanese;
Tier 2: French, Portuguese, Chinese, Dutch, Italian, and Korean.

Other languages supported by Gemma 4 may also work, but they have not undergone specific translation fine-tuning and have not been evaluated as extensively.

Benchmark results

All models in the following table were evaluated in GGUF Q4 format under the same generation and evaluation pipeline.

Higher is better for chrF++, BERTScore, COMET-DA, and COMETKiwi. Lower is better for MetricX Ref and MetricX QE.

Model	chrF++ ↑	BERTScore ↑	COMET-DA ↑	COMETKiwi ↑	MetricX-24 Ref ↓	MetricX-24 QE ↓
Translate Gemma 4 Sub E4B Q4_K_XL	51.8218	0.867594	0.835393	0.745391	2.7074	2.8150
Translate Gemma 4 Sub E2B Q4_K_XL	49.1003	0.857130	0.822867	0.741567	3.1002	3.0091
Gemma 4 12B QAT Q4_K_XL	51.5596	0.855906	0.828904	0.758124	2.8813	2.7447
Gemma 4 E4B Q4_K_XL	48.4846	0.848822	0.811101	0.752102	3.2390	3.0889
Gemma 4 E2B Q4_K_XL	45.0172	0.836204	0.782135	0.732084	3.8652	3.3755

The evaluation set was independent from the training data. Its reference translations were prepared from randomly selected stream segments that were not used during training.

MetricX-24 Ref by target language

Scores are grouped by target language. Lower is better.

Model	Russian ↓	English ↓	Japanese ↓	Spanish ↓	German ↓
Translate Gemma 4 Sub E4B Q4_K_XL	2.997	2.512	2.809	2.805	2.217
Translate Gemma 4 Sub E2B Q4_K_XL	3.100	2.573	3.138	2.901	2.370
Gemma 4 12B QAT Q4_K_XL	3.252	2.619	2.737	2.921	2.165
Gemma 4 E4B Q4_K_XL	3.477	2.687	3.172	3.310	2.694
Gemma 4 E2B Q4_K_XL	4.108	3.270	3.522	3.646	2.948

Prompt format

The model was trained with a system message that specifies the source language, target language, subtitle task, and style.

System message

TASK: Translate {source_language} subtitles into {target_language}.
RULES: Speakers name: ...; speaker gender: ...; other rules.
STYLE: friendly/official/neutral.
Translate only CURRENT_SOURCE. PREVIOUS_SOURCE and PREVIOUS_TRANSLATION are context only. Preserve meaning, tone, slang, profanity, uncertainty, repetitions and incomplete speech. Return only the final translation without labels or commentary.

User message

[PREVIOUS_SOURCE]
Previous source-language subtitles context.

[PREVIOUS_TRANSLATION]
Previous translated subtitles context.

[CURRENT_SOURCE]
The subtitle segment to translate.

Only CURRENT_SOURCE should be translated. PREVIOUS_SOURCE and PREVIOUS_TRANSLATION are context only.

The previous-context blocks may be omitted when no context is available:

[CURRENT_SOURCE]
The subtitle segment to translate.

Example

System message:

TASK: Translate English subtitles into Russian.
RULES: speaker gender: female.
STYLE: friendly.
Translate only CURRENT_SOURCE. PREVIOUS_SOURCE and PREVIOUS_TRANSLATION are context only. Preserve meaning, tone, slang, profanity, uncertainty, repetitions and incomplete speech. Return only the final translation without labels or commentary.

User message:

[PREVIOUS_SOURCE]
I thought you said you weren't coming.

[PREVIOUS_TRANSLATION]
Я думала, ты сказала, что не придёшь.

[CURRENT_SOURCE]
Yeah, well... I changed my mind.

Expected response:

Ну да... Я передумала.

FLORES-200 general translation benchmark

Evaluation was performed on FLORES-200 using MetricX-24 QE. Lower is better.

Model	Mean error ↓	Median ↓	P90 ↓
Gemma 4 12B QAT	1.8811	1.5391	3.5938
Translate Gemma 4 Sub E4B	2.0658	1.6523	4.0312
Gemma 4 E4B	2.0769	1.6875	4.1562
Translate Gemma 4 Sub E2B	2.2247	1.7930	4.4062
Gemma 4 E2B	2.2774	1.8359	4.4062

Despite being primarily optimized for contextual and conversational translation, Translate Gemma 4 Sub also slightly improved general translation quality over the corresponding base Gemma 4 models.

The following prompt was used for general translation:

TASK: Translate {source_language} into {target_language}.
Follow all demonstrations, glossary mappings, partial-translation constraints and formatting instructions in the user prompt.
Preserve meaning, names, numbers, terminology, register and document structure. Return only the requested final translation without commentary.

Usage with Transformers

import torch
from transformers import AutoModelForImageTextToText, AutoTokenizer

model_id = "17slever17/translate-gemma-4-sub-e2b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForImageTextToText.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

messages = [
    {
        "role": "system",
        "content": """TASK: Translate English subtitles into Russian.
STYLE: friendly.
Translate only CURRENT_SOURCE. PREVIOUS_SOURCE and PREVIOUS_TRANSLATION are context only. Preserve meaning, tone, slang, profanity, uncertainty, repetitions and incomplete speech. Return only the final translation without labels or commentary.""",
    },
    {
        "role": "user",
        "content": """[PREVIOUS_SOURCE]
I thought you said you weren't coming.

[PREVIOUS_TRANSLATION]
Я думала, ты сказала, что не придёшь.

[CURRENT_SOURCE]
Yeah, well... I changed my mind.""",
    },
]

inputs = tokenizer.apply_chat_template(
    messages,
    add_generation_prompt=True,
    return_tensors="pt",
    return_dict=True,
).to(model.device)

with torch.inference_mode():
    output = model.generate(
        **inputs,
        max_new_tokens=256,
        do_sample=False,
    )

generated_tokens = output[0, inputs["input_ids"].shape[1]:]
translation = tokenizer.decode(generated_tokens, skip_special_tokens=True)

print(translation)

Training overview

Translate Gemma 4 Sub E2B is based on google/gemma-4-E2B-it.

Training was performed with Unsloth using an optimized training run derived from the official Gemma 4 model.

Citation

@misc{17slever17_translate_gemma_4_sub_2026,
  author       = {17slever17},
  title        = {Translate Gemma 4 Sub E2B},
  year         = {2026},
  publisher    = {Hugging Face},
  howpublished = {\url{https://huggingface.co/17slever17/translate-gemma-4-sub-e2b}}
}

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Safetensors

Model size

5B params

Tensor type

BF16

Model tree for 17slever17/translate-gemma-4-sub-e2b

Base model

google/gemma-4-E2B

Finetuned

google/gemma-4-E2B-it

Finetuned

(305)

this model

Quantizations

1 model