Instructions to use IBB-University/ghadeer_question_answer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use IBB-University/ghadeer_question_answer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="IBB-University/ghadeer_question_answer")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("IBB-University/ghadeer_question_answer") model = AutoModelForCausalLM.from_pretrained("IBB-University/ghadeer_question_answer", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use IBB-University/ghadeer_question_answer with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "IBB-University/ghadeer_question_answer" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "IBB-University/ghadeer_question_answer", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/IBB-University/ghadeer_question_answer
- SGLang
How to use IBB-University/ghadeer_question_answer 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 "IBB-University/ghadeer_question_answer" \ --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": "IBB-University/ghadeer_question_answer", "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 "IBB-University/ghadeer_question_answer" \ --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": "IBB-University/ghadeer_question_answer", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use IBB-University/ghadeer_question_answer with Docker Model Runner:
docker model run hf.co/IBB-University/ghadeer_question_answer
Commit ·
abbae5c
1
Parent(s): aed883b
Upload 6 files
Browse files- merges.txt +0 -0
- special_tokens_map.json +6 -0
- tokenizer.json +0 -0
- tokenizer_config.json +10 -0
- training_args.bin +3 -0
- vocab.json +0 -0
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"name_or_path": "aubmindlab/aragpt2-base",
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"special_tokens_map_file": null,
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"tokenizer_class": "GPT2Tokenizer",
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version https://git-lfs.github.com/spec/v1
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oid sha256:69e0a5527567096e6c5c6085ea21fab26696b5810f628b140beb91786dcff408
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size 3375
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