Instructions to use fav-kky/gpt2-small-cs with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fav-kky/gpt2-small-cs with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="fav-kky/gpt2-small-cs")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("fav-kky/gpt2-small-cs") model = AutoModelForCausalLM.from_pretrained("fav-kky/gpt2-small-cs") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use fav-kky/gpt2-small-cs with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "fav-kky/gpt2-small-cs" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "fav-kky/gpt2-small-cs", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/fav-kky/gpt2-small-cs
- SGLang
How to use fav-kky/gpt2-small-cs 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 "fav-kky/gpt2-small-cs" \ --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": "fav-kky/gpt2-small-cs", "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 "fav-kky/gpt2-small-cs" \ --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": "fav-kky/gpt2-small-cs", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use fav-kky/gpt2-small-cs with Docker Model Runner:
docker model run hf.co/fav-kky/gpt2-small-cs
gpt2-small-cs
Small version of GPT2 model (4 layers, 8 attention heads, hid. size 512) pretrained from 115 GB of cleaned Czech text (mainly from Common Crawl project).
Usage
from transformers import pipeline, set_seed
generator = pipeline('text-generation', model='fav-kky/gpt2-small-cs')
set_seed(42)
generator("Lidstvo bude brzy", max_length=30, num_return_sequences=2)
[{'generated_text': 'Lidstvo bude brzy seskupeno do osmi skupin, které budou rozděleny do čtyř skupin. V čele budou čtyři prezidenti, mezi nimi například prezidenti'},
{'generated_text': 'Lidstvo bude brzy potřebovat čas na budování infrastruktury, řekl na tiskové konferenci ředitel Národní síťové informační agentury Jiří Drahoš. "Napojení, které bude'}]
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