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, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("jerteh/gpt2-vrabac") model = AutoModelForMultimodalLM.from_pretrained("jerteh/gpt2-vrabac") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- 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
metadata
datasets:
- jerteh/cc100-sr-jerteh
- jerteh/SrpELTeC
- jerteh/SrpWiki
- srwac
language:
- sr
tags:
- Srpski
- Serbian
- GPT2
- generisanje
Mali generativni model za srpski jezik.
Generiše novi tekst, ili nastavlja započeti tekstualni unos.
Jednaka podrška unosa i na ćirilici i na latinici!
Upotreba
>>> from transformers import pipeline, set_seed
>>> generator = pipeline('text-generation', model='jerteh/gpt2-vrabac')
>>> set_seed(23)
>>> generator("", max_length=30, num_return_sequences=5)
[{'generated_text': 'Ja, međutim, ne idem na put da idem već da se vratim na aerodrom.'},
{'generated_text': 'Domaćinstvo se nalazilo na mestu zvanom Kutuzov kod Niša.'},
{'generated_text': 'Regionalne razlike:'},
{'generated_text': 'Od tada do sada smo u veoma teškoj situaciji“, poručio je on.'},
{'generated_text': 'Iz tog razloga, na ovaj način u potpunosti bi se izbegla dodatna mogućnost da se sa istim problemima suoči i Vlada.'}]
Pored navedenih, model je obučavan i na ostalim korpusima Društva za jezičke resurse i tehnologije, uključujući korpuse savremenog srpskog jezika: SrpKor2013 i SrpKor2021, kao i korpus PDRS 1.0 razvijen od strane Instituta za Srpski jezik SANU.
U slučaju potrebe za većim modelom, pogledajte gpt2-orao — najveći generativni model za srpski jezik.
Modeli su obučavani na Nacionalnoj platformi za veštačku inteligenciju Srbije (sistem koji se bazira na nVidia DGX sistemima).