Instructions to use ahmet1338/gpt-2-experimental with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ahmet1338/gpt-2-experimental with Transformers:

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

pipe = pipeline("text-generation", model="ahmet1338/gpt-2-experimental")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ahmet1338/gpt-2-experimental")
model = AutoModelForCausalLM.from_pretrained("ahmet1338/gpt-2-experimental")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use ahmet1338/gpt-2-experimental with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ahmet1338/gpt-2-experimental"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ahmet1338/gpt-2-experimental",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/ahmet1338/gpt-2-experimental

SGLang

How to use ahmet1338/gpt-2-experimental 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 "ahmet1338/gpt-2-experimental" \
    --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": "ahmet1338/gpt-2-experimental",
		"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 "ahmet1338/gpt-2-experimental" \
        --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": "ahmet1338/gpt-2-experimental",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use ahmet1338/gpt-2-experimental with Docker Model Runner:
```
docker model run hf.co/ahmet1338/gpt-2-experimental
```

Turkish GPT-2 Model (Experimental)

I've made available a GPT-2 model for Turkish that I trained on a variety of texts.

The model is intended to serve as a starting point for text-specific adjustments.

Training Source

I used a Turkish corpus that is taken from different written and oral sources.

I developed a LLM model with 50k vocabulary using the Custom Tokenizers library using the training resources.

I could train the GPT-2 for Turkish using the entire training corpus (ten epochs) after developing the vocabulary.

Using the model

The model itself can be used in this way:

from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("ahmet1338/gpt-2-experimental")
model = AutoModelWithLMHead.from_pretrained("ahmet1338/gpt-2-experimental")

To generating text, we can use these lines of code which is Transformers Pipelines:

from transformers import pipeline
pipe = pipeline('text-generation', model="ahmet1338/gpt-2-experimental",
                 tokenizer="ahmet1338/gpt-2-experimental", 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/ahmet1338/gpt-2-experimential

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Safetensors

Model size

0.1B params

Tensor type

F32