Instructions to use canbingol/gemma3-270M-tr-sft-2epoch with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use canbingol/gemma3-270M-tr-sft-2epoch with Transformers:

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

pipe = pipeline("text-generation", model="canbingol/gemma3-270M-tr-sft-2epoch")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("canbingol/gemma3-270M-tr-sft-2epoch")
model = AutoModelForCausalLM.from_pretrained("canbingol/gemma3-270M-tr-sft-2epoch", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.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(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use canbingol/gemma3-270M-tr-sft-2epoch with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "canbingol/gemma3-270M-tr-sft-2epoch"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "canbingol/gemma3-270M-tr-sft-2epoch",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/canbingol/gemma3-270M-tr-sft-2epoch

SGLang

How to use canbingol/gemma3-270M-tr-sft-2epoch 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 "canbingol/gemma3-270M-tr-sft-2epoch" \
    --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": "canbingol/gemma3-270M-tr-sft-2epoch",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "canbingol/gemma3-270M-tr-sft-2epoch" \
        --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": "canbingol/gemma3-270M-tr-sft-2epoch",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use canbingol/gemma3-270M-tr-sft-2epoch with Docker Model Runner:
```
docker model run hf.co/canbingol/gemma3-270M-tr-sft-2epoch
```

Model Card: Gemma3-270M Turkish SFT (Experimental)

Overview

This is an experimental Turkish instruction-tuned model based on google/gemma-3-270m.
It was fine-tuned for 1 epoch on the merve/turkish_instructions dataset.

This model was trained as part of ongoing experiments and is not intended as a production-ready release.

Base Model

google/gemma-3-270m

Training Data

Dataset: merve/turkish_instructions
Training setup: Supervised Fine-Tuning (SFT), 1 epoch

Intended Use

Research and experimentation with Turkish instruction following
Prototyping and evaluation workflows for Turkish LLMs

Limitations & Warnings

This model is experimental and may produce inaccurate, biased, or unsafe outputs.
For personal or commercial use, you should test and validate the model carefully for your specific scenario.
Benchmark performance and safety behavior are not guaranteed.

Evaluation

No official benchmark results are provided in this model card yet.

Citation

If you use this model in your work, consider citing the base model and the dataset:

Base: google/gemma-3-270m
Dataset: merve/turkish_instructions

Usage

Below is a minimal example using 🤗 Transformers:

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "canbingol/gemma3-270M-tr-sft-2epoch"

device = "cuda" if torch.cuda.is_available() else "cpu"

model = AutoModelForCausalLM.from_pretrained(model_id)
tokenizer = AutoTokenizer.from_pretrained(model_id)

model = model.to(device)

prompt = "nasıl yemek yaparım?"
inputs = tokenizer(prompt, return_tensors="pt").to(device)

outputs = model.generate(
    **inputs,
    max_new_tokens=50,
    do_sample=False
)

generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(generated_text)