Instructions to use AhmetSemih/tr-gemma-128k-4b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AhmetSemih/tr-gemma-128k-4b with Transformers:

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

pipe = pipeline("image-text-to-text", model="AhmetSemih/tr-gemma-128k-4b")
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("AhmetSemih/tr-gemma-128k-4b")
model = AutoModelForImageTextToText.from_pretrained("AhmetSemih/tr-gemma-128k-4b")
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 AhmetSemih/tr-gemma-128k-4b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AhmetSemih/tr-gemma-128k-4b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AhmetSemih/tr-gemma-128k-4b",
		"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/AhmetSemih/tr-gemma-128k-4b

SGLang

How to use AhmetSemih/tr-gemma-128k-4b 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 "AhmetSemih/tr-gemma-128k-4b" \
    --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": "AhmetSemih/tr-gemma-128k-4b",
		"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 "AhmetSemih/tr-gemma-128k-4b" \
        --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": "AhmetSemih/tr-gemma-128k-4b",
		"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 AhmetSemih/tr-gemma-128k-4b with Docker Model Runner:
```
docker model run hf.co/AhmetSemih/tr-gemma-128k-4b
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Ahmet-Gemma3-4B

A Turkish-optimized version of Google's Gemma 3 4B model with a custom Turkish tokenizer.

Model Description

This model is based on google/gemma-3-4b-it with the embedding layer adapted to use a Turkish-optimized tokenizer (AhmetSemih/tr-gemma-128k-processor).

Key Features

Base Model: Google Gemma 3 4B Instruct
Tokenizer: Custom Turkish tokenizer with 128K vocabulary
Embedding Transfer: Mean pooling strategy for new tokens
Precision: bfloat16

How It Was Created

The model was created by:

Loading the original Gemma 3 4B model and tokenizer
Mapping token embeddings from the original vocabulary to the new Turkish vocabulary:
- Direct matches: Tokens existing in both vocabularies keep their original embeddings
- New tokens: Tokenized using the original tokenizer, then embeddings are averaged
- Unmapped tokens: Fall back to UNK token embedding
Resizing the embedding layer to match the new vocabulary size

Code

https://github.com/malibayram/embedding-trainer


## Training Recommendations

For best results, pretrain this model on Turkish text data.

Downloads last month: 3

Safetensors

Model size

4B params

Tensor type

BF16

Model tree for AhmetSemih/tr-gemma-128k-4b

Base model

google/gemma-3-4b-pt

Finetuned

google/gemma-3-4b-it

Finetuned

(679)

this model

Finetunes

1 model

Quantizations

1 model