Text Generation
Transformers
PyTorch
Safetensors
Serbian
gpt2
Srpski
Serbian
GPT2
generisanje
text-generation-inference
Instructions to use jerteh/gpt2-vrabac with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jerteh/gpt2-vrabac with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="jerteh/gpt2-vrabac")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("jerteh/gpt2-vrabac") model = AutoModelForCausalLM.from_pretrained("jerteh/gpt2-vrabac") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use jerteh/gpt2-vrabac with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "jerteh/gpt2-vrabac" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "jerteh/gpt2-vrabac", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/jerteh/gpt2-vrabac
- SGLang
How to use jerteh/gpt2-vrabac 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 "jerteh/gpt2-vrabac" \ --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": "jerteh/gpt2-vrabac", "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 "jerteh/gpt2-vrabac" \ --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": "jerteh/gpt2-vrabac", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use jerteh/gpt2-vrabac with Docker Model Runner:
docker model run hf.co/jerteh/gpt2-vrabac
- Xet hash:
- 964f765eba66e976ad98e60654c775bd8ce539b49a7a84abcb1a6181d6de3003
- Size of remote file:
- 507 MB
- SHA256:
- a1fe14fe52d1930b2d07212b8813878fc89283c9c994096eecf515c056be701a
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.