Text Generation
Transformers
PyTorch
Safetensors
Luxembourgish
gpt2
luxembourgish
lëtzebuergesch
text generation
Eval Results (legacy)
text-generation-inference
Instructions to use laurabernardy/LuxGPT2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use laurabernardy/LuxGPT2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="laurabernardy/LuxGPT2")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("laurabernardy/LuxGPT2") model = AutoModelForCausalLM.from_pretrained("laurabernardy/LuxGPT2") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use laurabernardy/LuxGPT2 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "laurabernardy/LuxGPT2" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "laurabernardy/LuxGPT2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/laurabernardy/LuxGPT2
- SGLang
How to use laurabernardy/LuxGPT2 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 "laurabernardy/LuxGPT2" \ --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": "laurabernardy/LuxGPT2", "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 "laurabernardy/LuxGPT2" \ --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": "laurabernardy/LuxGPT2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use laurabernardy/LuxGPT2 with Docker Model Runner:
docker model run hf.co/laurabernardy/LuxGPT2
LuxGPT-2
The model with the best performance of this experiment is: laurabernardy/LuxGPT2-basedGER.
GPT-2 model for Text Generation in luxembourgish language, trained on 667 MB of text data, consisting of RTL.lu news articles, comments, parlament speeches, the luxembourgish Wikipedia, Newscrawl, Webcrawl and subtitles. The training took place on a 32 GB Nvidia Tesla V100
- with an initial learning rate of 5e-5
- with Batch size 4
- for 109 hours
- for 30 epochs
- using the transformers library
more detailed training information can be found in the "trainer_state.json".
Usage
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("laurabernardy/LuxGPT2")
model = AutoModelForCausalLM.from_pretrained("laurabernardy/LuxGPT2")
Limitations and Biases
See the GPT2 model card for considerations on limitations and bias. See the GPT2 documentation for details on GPT2.
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Evaluation results
- accuracy on Luxembourgish Test Datasetself-reported0.33
- perplexity on Luxembourgish Test Datasetself-reported46.69