gemma3_1B_base-tr-cpt-3epoch_15k_data/README.md

library_name, tags, datasets, language, base_model

library_name	tags	datasets	language	base_model
transformers	trl cpt	canbingol/vngrs-web-corpus-200k	tr en	google/gemma-3-1b-pt

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.

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Base Model

• google/gemma-3-1b-pt

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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

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Citation

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

• Base: google/gemma-3-1b-pt

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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)