gemma3_1B_it-tr-sft-1epoch

Model Card: Gemma3-1B 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-3-1b-it
• Dataset: merve/turkish_instructions

Usage

Below is a minimal example using 🤗 Transformers:

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "canbingol/gemma3_1B_it-tr-sft-1epoch"

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)