Instructions to use DeividasM/gpt2_lithuanian_small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use DeividasM/gpt2_lithuanian_small with Transformers:

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

pipe = pipeline("text-generation", model="DeividasM/gpt2_lithuanian_small")

# Load model directly
from transformers import TFAutoModel
model = TFAutoModel.from_pretrained("DeividasM/gpt2_lithuanian_small", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use DeividasM/gpt2_lithuanian_small with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "DeividasM/gpt2_lithuanian_small"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "DeividasM/gpt2_lithuanian_small",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/DeividasM/gpt2_lithuanian_small

SGLang

How to use DeividasM/gpt2_lithuanian_small 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 "DeividasM/gpt2_lithuanian_small" \
    --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": "DeividasM/gpt2_lithuanian_small",
		"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 "DeividasM/gpt2_lithuanian_small" \
        --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": "DeividasM/gpt2_lithuanian_small",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use DeividasM/gpt2_lithuanian_small with Docker Model Runner:
```
docker model run hf.co/DeividasM/gpt2_lithuanian_small
```

Model description

GPT-2 model from Lithuania using Wikipedia corpus dataset based on GPT-2 small model.

This is only the first version of the model; over time model will be improved using a more extensive dataset and better data preparation.

Training data

This model was pre-trained with 180MB of Lithuanian Wikipedia. The texts are tokenized using a byte-level version of Byte Pair Encoding (BPE).

Training

The model was trained on wiki-corpus for 40 hours using NVIDIA Tesla P100 GPU.

How to use

Load model

from transformers import AutoTokenizer, TFAutoModelWithLMHead
import tensorflow as tf

tokenizer = AutoTokenizer.from_pretrained("DeividasM/gpt2_lithuanian_small")
model = TFAutoModelWithLMHead.from_pretrained("DeividasM/gpt2_lithuanian_small")

# Get sequence length max of 1024
tokenizer.model_max_length=1024 

model.eval()

Generate text

text = "tekstas "
inputs = tokenizer.encode(text, return_tensors="tf")


outputs = model.generate(inputs, eos_token_id=50256, pad_token_id=50256, 
                         do_sample=True,
                         max_length=40,
                         top_k=40)
                         
print(tokenizer.decode(outputs[0]))

Limitations and bias

The training data used for this model come from Lithuanian Wikipedia. We know it contains a lot of unfiltered content from the internet, which is far from neutral. As the OpenAI team themselves point out in their model card:

"Because large-scale language models like GPT-2 do not distinguish fact from fiction, we don’t support use-cases that require the generated text to be true. Additionally, language models like GPT-2 reflect the biases inherent to the systems they were trained on, so we do not recommend that they be deployed into systems that interact with humans > unless the deployers first carry out a study of biases relevant to the intended use-case. We found no statistically significant difference in gender, race, and religious bias probes between 774M and 1.5B, implying all versions of GPT-2 should be approached with similar levels of caution around use cases that are sensitive to biases around human attributes."

Author

Lithuanian GPT-2 small was trained and evaluated by Deividas Mataciunas (https://www.linkedin.com/in/deividasmataciunas/)

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