Instructions to use procesaur/gpt2-srlat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use procesaur/gpt2-srlat with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="procesaur/gpt2-srlat")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("procesaur/gpt2-srlat") model = AutoModelForCausalLM.from_pretrained("procesaur/gpt2-srlat") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use procesaur/gpt2-srlat with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "procesaur/gpt2-srlat" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "procesaur/gpt2-srlat", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/procesaur/gpt2-srlat
- SGLang
How to use procesaur/gpt2-srlat 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 "procesaur/gpt2-srlat" \ --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": "procesaur/gpt2-srlat", "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 "procesaur/gpt2-srlat" \ --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": "procesaur/gpt2-srlat", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use procesaur/gpt2-srlat with Docker Model Runner:
docker model run hf.co/procesaur/gpt2-srlat
| license: agpl-3.0 | |
| language: | |
| - sr | |
| Model is developed in support of the University of Belgrade doctoral dissertation "Composite pseudogrammars based on parallel language models of Serbian" by Mihailo Škorić. | |
| This small gpt-2 model was trained on several corpora for Serbian, including ["The corpus of Contemporary Serbian"](https://drive.google.com/file/d/1wRgoWer6YULGCXR0zWOl1fVA6VIe1DOR), [SrpELTeC](https://drive.google.com/file/d/1RtBXyw5Cdh6y_cqbJoMlYhSwNFydBRUv) and WikiKorpus by [JeRTeh – Society for Language Resources and Technologies](https://jerteh.rs/). | |
| <b style="color:red">This model is purely experimental! For actual models for Serbian see <a href="https://huggingface.co/jerteh/gpt2-orao" style="color:blue;font-weight:bold">GPT2-ORAO</a> and <a style="color:blue;font-weight:bold" href="https://huggingface.co/jerteh/gpt2-orao">GPT2-VRABAC</a></b> | |
| <br/><b>If you use this model for your reseach please cite: [https://doi.org/10.3390/math11224660](https://doi.org/10.3390/math11224660)</b> |