79 lines
1.6 KiB
Markdown
79 lines
1.6 KiB
Markdown
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---
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library_name: transformers
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tags:
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- trl
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- cpt
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datasets:
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- canbingol/vngrs-web-corpus-200k
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language:
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- tr
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- en
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base_model:
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- google/gemma-3-1b-pt
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---
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# Model Card: Gemma3-1B Turkish CPT (15K Subset, 3 Epoch)
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## Overview
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This model is a Turkish Continued Pretraining (CPT) variant of `google/gemma-3-1b-pt`.
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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.
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This release is intended for **research and experimental use**.
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---
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## Base Model
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- `google/gemma-3-1b-pt`
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---
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## Training Setup
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- Dataset: `canbingol/vngrs-web-corpus-200k`
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- Subset Used: First 15,000 samples
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- Training Objective: Continued Pretraining (Causal LM / Next-Token Prediction)
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- Epochs: 3
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- Data Regime: Plain text (no instruction formatting)
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- Token Count (approximate): ~19.5M tokens
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---
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## Citation
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If you use this model, please cite the base model:
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- Base: `google/gemma-3-1b-pt`
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---
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## Usage Example
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```python
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_id = "canbingol/gemma3_1B_base-tr-cpt-3epoch_15k_data"
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device = "cuda" if torch.cuda.is_available() else "cpu"
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model = AutoModelForCausalLM.from_pretrained(model_id)
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = model.to(device)
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prompt = "Benim adım"
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inputs = tokenizer(prompt, return_tensors="pt").to(device)
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outputs = model.generate(
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**inputs,
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max_new_tokens=50,
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do_sample=True,
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temperature=0.8,
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top_p=0.9
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)
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generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
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print(generated_text)
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