Text Generation
Transformers
Safetensors
Turkish
mt5
text2text-generation
Question Answering
Generated from Trainer
Instructions to use ucsahin/mT5-base-turkish-qa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ucsahin/mT5-base-turkish-qa with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="ucsahin/mT5-base-turkish-qa")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("ucsahin/mT5-base-turkish-qa") model = AutoModelForSeq2SeqLM.from_pretrained("ucsahin/mT5-base-turkish-qa", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use ucsahin/mT5-base-turkish-qa with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "ucsahin/mT5-base-turkish-qa" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ucsahin/mT5-base-turkish-qa", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/ucsahin/mT5-base-turkish-qa
- SGLang
How to use ucsahin/mT5-base-turkish-qa 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 "ucsahin/mT5-base-turkish-qa" \ --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": "ucsahin/mT5-base-turkish-qa", "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 "ucsahin/mT5-base-turkish-qa" \ --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": "ucsahin/mT5-base-turkish-qa", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use ucsahin/mT5-base-turkish-qa with Docker Model Runner:
docker model run hf.co/ucsahin/mT5-base-turkish-qa
Download training_args.bin from ucsahin/mT5-base-turkish-qa: direct link, hf CLI and curl.
- Browser
- Download file 4.86 kB
-
https://huggingface.co/ucsahin/mT5-base-turkish-qa/resolve/main/training_args.bin
- Command line
-
hf download hf://ucsahin/mT5-base-turkish-qa/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/ucsahin/mT5-base-turkish-qa/resolve/main/training_args.bin
4.86 kB
- Xet hash:
- 407cc1712e0e8b1a6d14142762b21a058d6b3522310653c7e67c972ce09e453e
- Size of remote file:
- 4.86 kB
- SHA256:
- 41391deedfb8c71ea7c4c32b0869c4b744abc076a17afd627b4dd50d6257ac9f
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