procesaur/znanje
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How to use jerteh/gpt2-orao with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-generation", model="jerteh/gpt2-orao") # Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("jerteh/gpt2-orao")
model = AutoModelForCausalLM.from_pretrained("jerteh/gpt2-orao")How to use jerteh/gpt2-orao with vLLM:
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "jerteh/gpt2-orao"
# 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-orao",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'docker model run hf.co/jerteh/gpt2-orao
How to use jerteh/gpt2-orao with SGLang:
# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
--model-path "jerteh/gpt2-orao" \
--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-orao",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'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-orao" \
--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-orao",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'How to use jerteh/gpt2-orao with Docker Model Runner:
docker model run hf.co/jerteh/gpt2-orao
gpt2-orao — Najveći generativni model za srpski jezik.
>>> from transformers import pipeline, set_seed
>>> generator = pipeline('text-generation', model='jerteh/gpt2-orao')
>>> set_seed(23)
>>> generator("", max_length=30, num_return_sequences=5)
[{'generated_text': 'Ja, međutim, ne idem na Adu - kaže Miodrag.'},
{'generated_text': 'Domaćinstvo se nalazilo na mestu zvanom Kulina (ranije Kulina Vakuf) i bilo je jedno od najvećih i naj'},
{'generated_text': 'Regionalne razlike se uglavnom odnose na geografski položaj, geografsko-geografski položaj i ekonomsku razvijenost.'},
{'generated_text': 'Od tada do danas Srbija ne stoji na nogama'},
{'generated_text': 'Iz tog razloga, na ovaj način se postiže bolja efikasnost rada, odnosno smanjuje se vreme potrebno za sprovođenje simulacije.'}]
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.
@article{skoric24modeli,
author = {Mihailo \vSkori\'c},
title = {Novi jezi\vcki modeli za srpski jezik},
journal = {Infoteka},
volume = {24},
issue = {1},
year = {2024},
publisher = {Zajednica biblioteka univerziteta u Srbiji, Beograd},
url = {https://arxiv.org/abs/2402.14379}
}