初始化项目，由ModelHub XC社区提供模型

Model: oguzatas/mamba-tr-project-transformer
Source: Original Platform

README.md

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+---
+license: mit
+language:
+- tr
+pipeline_tag: text-generation
+tags:
+- transformer
+- gpt2
+- turkish
+- baseline
+links:
+  - label: "Experiment Paper"
+    url: "https://huggingface.co/oguzatas/mamba-tr-project-transformer/resolve/main/OguzhanAtasHW%20state-space-models%20on%20Morphologically%20Rich%20Languages.pdf"
+---
+---
+
+[![Paper](https://img.shields.io/badge/PDF-Technical_Paper-red?style=for-the-badge&logo=adobeacrobatreader&logoColor=white)](https://huggingface.co/oguzatas/mamba-tr-project-transformer/resolve/main/OguzhanAtasHW%20state-space-models%20on%20Morphologically%20Rich%20Languages.pdf)
+[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1UbkR-i3P6X2cFVWXlntySjki_WBdg0ac?usp=sharing)
+# Transformer Baseline (GPT-2 Style) - Turkish
+
+This model serves as the **baseline** for a comparative study between Transformer and Mamba architectures on agglutinative languages (specifically Turkish). It is a decoder-only Transformer model (~111M parameters) trained on the Turkish Wikipedia dataset.
+
+## Model Description
+- **Architecture:** GPT-2 (Small)
+- **Parameters:** ~111 Million
+- **Context Length:** 1024
+- **Training Data:** Turkish Wikipedia (Nov 2023)
+- **Purpose:** To provide a performance benchmark for the Mamba architecture.
+
+## Usage
+
+```python
+from transformers import GPT2LMHeadModel, PreTrainedTokenizerFast
+
+model_id = "oguzatas/mamba-tr-project-transformer"
+tokenizer_id = "oguzatas/mamba-tr-project-tokenizer"
+
+tokenizer = PreTrainedTokenizerFast.from_pretrained(tokenizer_id)
+model = GPT2LMHeadModel.from_pretrained(model_id)
+
+text = "Türkiye'nin başkenti"
+inputs = tokenizer(text, return_tensors="pt")
+outputs = model.generate(**inputs, max_new_tokens=20)
+
+print(tokenizer.decode(outputs[0]))