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Model: canbingol/gemma3_1B_base-tr-cpt-3epoch_15k_data
Source: Original Platform

README.md

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+---
+library_name: transformers
+tags:
+- trl
+- cpt
+datasets:
+- canbingol/vngrs-web-corpus-200k
+language:
+- tr
+- en
+base_model:
+- 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**.
+
+---
+
+## 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
+
+```python
+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)