oscar-corpus/oscar
Updated • 670 • 207
How to use malteos/gpt2-uk with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-generation", model="malteos/gpt2-uk") # Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("malteos/gpt2-uk")
model = AutoModelForCausalLM.from_pretrained("malteos/gpt2-uk")How to use malteos/gpt2-uk with vLLM:
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "malteos/gpt2-uk"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "malteos/gpt2-uk",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'docker model run hf.co/malteos/gpt2-uk
How to use malteos/gpt2-uk with SGLang:
# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
--model-path "malteos/gpt2-uk" \
--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": "malteos/gpt2-uk",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'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 "malteos/gpt2-uk" \
--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": "malteos/gpt2-uk",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'How to use malteos/gpt2-uk with Docker Model Runner:
docker model run hf.co/malteos/gpt2-uk
A generative language model for the Ukrainian language follows the GPT-2 architecture (124M parameters).
MIT