Instructions to use MU-NLPC/CzeGPT-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MU-NLPC/CzeGPT-2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="MU-NLPC/CzeGPT-2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("MU-NLPC/CzeGPT-2") model = AutoModelForCausalLM.from_pretrained("MU-NLPC/CzeGPT-2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use MU-NLPC/CzeGPT-2 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "MU-NLPC/CzeGPT-2" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "MU-NLPC/CzeGPT-2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/MU-NLPC/CzeGPT-2
- SGLang
How to use MU-NLPC/CzeGPT-2 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 "MU-NLPC/CzeGPT-2" \ --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": "MU-NLPC/CzeGPT-2", "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 "MU-NLPC/CzeGPT-2" \ --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": "MU-NLPC/CzeGPT-2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use MU-NLPC/CzeGPT-2 with Docker Model Runner:
docker model run hf.co/MU-NLPC/CzeGPT-2
Commit History
-
Adding `safetensors` variant of this model (#4)
Verified signature- Adding `safetensors` variant of this model (ad10582bb249a066a8a2f7ec5e359582218fe4ed) Co-authored-by: Safetensors convertbot <SFconvertbot@users.noreply.huggingface.co>eb31559
-
Update README.md
Verified signaturefbd7d66
-
Update README.md
Verified signature74bd42c
-
Update README.md
Verified signaturefca9056 -
Update README.md
Verified signaturef868f72
-
Update README.md
abf5031 -
Update README.md
4a2dcdc
-
Update README.md
96033e2
-
Update README.md
84654be
-
Update generate_text.ipynb
35512db -
Upload generate_text.ipynb
Notebook for easy start with the model.9b4953e -
Update README.md
267d466
-
Update README.md
b559343 -
Update README.md
f0315d3
-
Update README.md
fc94b21
-
Update README.md
bbc8c58 -
Update README.md
d43f0cd -
Update README.md
ac29a50 -
Upload pytorch_model.bin with git-lfs
b7fd724 -
Upload vocab.json
d7cb9ab -
Upload merges.txt
f9414d0 -
Upload config.json
866bcc4 -
Delete config_summarizer.json
7684528 -
Upload config_summarizer.json
41d8a6b -
Update README.md
3a4978b -
initial commit
f712584