Instructions to use hakanbogan/gpt2-turkish-cased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use hakanbogan/gpt2-turkish-cased with Transformers:

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

pipe = pipeline("text-generation", model="hakanbogan/gpt2-turkish-cased")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("hakanbogan/gpt2-turkish-cased")
model = AutoModelForCausalLM.from_pretrained("hakanbogan/gpt2-turkish-cased", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use hakanbogan/gpt2-turkish-cased with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "hakanbogan/gpt2-turkish-cased"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "hakanbogan/gpt2-turkish-cased",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/hakanbogan/gpt2-turkish-cased

SGLang

How to use hakanbogan/gpt2-turkish-cased 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 "hakanbogan/gpt2-turkish-cased" \
    --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": "hakanbogan/gpt2-turkish-cased",
		"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 "hakanbogan/gpt2-turkish-cased" \
        --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": "hakanbogan/gpt2-turkish-cased",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use hakanbogan/gpt2-turkish-cased with Docker Model Runner:
```
docker model run hf.co/hakanbogan/gpt2-turkish-cased
```

🇹🇷 Turkish GPT-2 Model

In this repository I release GPT-2 model, that was trained on various texts for Turkish.

The model is meant to be an entry point for fine-tuning on other texts.

Training corpora

I used a Turkish corpora that is taken from oscar-corpus.

It was possible to create byte-level BPE with Tokenizers library of Huggingface.

With the Tokenizers library, I created a 52K byte-level BPE vocab based on the training corpora.

After creating the vocab, I could train the GPT-2 for Turkish on two 2080TI over the complete training corpus (five epochs).

Logs during training: https://tensorboard.dev/experiment/3AWKv8bBTaqcqZP5frtGkw/#scalars

Model weights

Both PyTorch and Tensorflow compatible weights are available.

Model	Downloads
`hakanbogan/gpt2-turkish-cased`	`config.json` • `merges.txt` • `pytorch_model.bin` • `special_tokens_map.json` • `tf_model.h5` • `tokenizer_config.json` • `traning_args.bin` • `vocab.json`

Using the model

The model itself can be used in this way:

from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("hakanbogan/gpt2-turkish-cased")
model = AutoModelWithLMHead.from_pretrained("hakanbogan/gpt2-turkish-cased")

Here's an example that shows how to use the great Transformers Pipelines for generating text:

from transformers import pipeline
pipe = pipeline('text-generation', model="hakanbogan/gpt2-turkish-cased",
                 tokenizer="hakanbogan/gpt2-turkish-cased", config={'max_length':800})   
text = pipe("Akşamüstü yolda ilerlerken, ")[0]["generated_text"]
print(text)

How to clone the model repo?

git lfs install
git clone https://huggingface.co/hakanbogan/gpt2-turkish-cased

Contact (Bugs, Feedback, Contribution and more)

For questions about the GPT2-Turkish model, just open an issue here 🤗

Downloads last month: 861

Safetensors

Model size

0.1B params

Tensor type

F32

Model tree for hakanbogan/gpt2-turkish-cased

Finetunes

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gpt2-turkish-cased