Instructions to use laurabernardy/LuxGPT-basedEN with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use laurabernardy/LuxGPT-basedEN with Transformers:

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

pipe = pipeline("text-generation", model="laurabernardy/LuxGPT-basedEN")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("laurabernardy/LuxGPT-basedEN")
model = AutoModelForCausalLM.from_pretrained("laurabernardy/LuxGPT-basedEN")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use laurabernardy/LuxGPT-basedEN with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/laurabernardy/LuxGPT-basedEN

SGLang

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

Docker Model Runner
How to use laurabernardy/LuxGPT-basedEN with Docker Model Runner:
```
docker model run hf.co/laurabernardy/LuxGPT-basedEN
```

LuxGPT-2 based GER

GPT-2 model for Text Generation in luxembourgish language, trained on 711 MB of text data, consisting of RTL.lu news articles, comments, parlament speeches, the luxembourgish Wikipedia, Newscrawl, Webcrawl and subtitles. Created via transfer learning with an English base model, feature space mapping from LB on Base feature space and gradual layer freezing. The training took place on a 32 GB Nvidia Tesla V100

with One Cycle policy for the learning rate
with the help of fastai's LR finder
for 49.2 hours
for 18 epochs and 8 cycles
using the fastai library

Usage

from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("laurabernardy/LuxGPT2-basedEN")
model = AutoModelForCausalLM.from_pretrained("laurabernardy/LuxGPT2-basedEN")

Limitations and Biases

See the GPT2 model card for considerations on limitations and bias. See the GPT2 documentation for details on GPT2.

Downloads last month: 13

Evaluation results

accuracy on Luxembourgish Test Dataset
self-reported

0.35
perplexity on Luxembourgish Test Dataset
self-reported

45.08