Instructions to use canbingol/gemma3_1B_base-tr-cpt-3epoch_15k_data with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use canbingol/gemma3_1B_base-tr-cpt-3epoch_15k_data with Transformers:

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

pipe = pipeline("text-generation", model="canbingol/gemma3_1B_base-tr-cpt-3epoch_15k_data")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("canbingol/gemma3_1B_base-tr-cpt-3epoch_15k_data")
model = AutoModelForCausalLM.from_pretrained("canbingol/gemma3_1B_base-tr-cpt-3epoch_15k_data", device_map="auto")

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use canbingol/gemma3_1B_base-tr-cpt-3epoch_15k_data with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "canbingol/gemma3_1B_base-tr-cpt-3epoch_15k_data"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "canbingol/gemma3_1B_base-tr-cpt-3epoch_15k_data",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/canbingol/gemma3_1B_base-tr-cpt-3epoch_15k_data

SGLang

How to use canbingol/gemma3_1B_base-tr-cpt-3epoch_15k_data 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_1B_base-tr-cpt-3epoch_15k_data" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "canbingol/gemma3_1B_base-tr-cpt-3epoch_15k_data",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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_1B_base-tr-cpt-3epoch_15k_data" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "canbingol/gemma3_1B_base-tr-cpt-3epoch_15k_data",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use canbingol/gemma3_1B_base-tr-cpt-3epoch_15k_data with Docker Model Runner:
```
docker model run hf.co/canbingol/gemma3_1B_base-tr-cpt-3epoch_15k_data
```

Model Card: Gemma3-1B Turkish CPT (15K Subset, 3 Epoch)

Overview

This model is a Turkish Continued Pretraining (CPT) variant of google/gemma-3-1b-pt.

The base model was further trained for 3 epochs on the first 15,000 samples of a Turkish web corpus to improve Turkish language modeling capability and domain familiarity.

This release is intended for research and experimental use.

Base Model

google/gemma-3-1b-pt

Training Setup

Dataset: canbingol/vngrs-web-corpus-200k
Subset Used: First 15,000 samples
Training Objective: Continued Pretraining (Causal LM / Next-Token Prediction)
Epochs: 3
Data Regime: Plain text (no instruction formatting)
Token Count (approximate): ~19.5M tokens

Citation

If you use this model, please cite the base model:

Base: google/gemma-3-1b-pt

Usage Example

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "canbingol/gemma3_1B_base-tr-cpt-3epoch_15k_data"

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 = "Benim adım"
inputs = tokenizer(prompt, return_tensors="pt").to(device)

outputs = model.generate(
    **inputs,
    max_new_tokens=50,
    do_sample=True,
    temperature=0.8,
    top_p=0.9
)

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