Instructions to use tokhey/question_generation_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use tokhey/question_generation_model with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct") model = PeftModel.from_pretrained(base_model, "tokhey/question_generation_model") - Transformers
How to use tokhey/question_generation_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="tokhey/question_generation_model") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("tokhey/question_generation_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use tokhey/question_generation_model with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "tokhey/question_generation_model" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "tokhey/question_generation_model", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/tokhey/question_generation_model
- SGLang
How to use tokhey/question_generation_model 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 "tokhey/question_generation_model" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "tokhey/question_generation_model", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'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 "tokhey/question_generation_model" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "tokhey/question_generation_model", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use tokhey/question_generation_model with Docker Model Runner:
docker model run hf.co/tokhey/question_generation_model
Download training_args.bin from tokhey/question_generation_model: direct link, hf CLI and curl.
- Browser
- Download file 6.42 kB
-
https://huggingface.co/tokhey/question_generation_model/resolve/main/training_args.bin
- Command line
-
hf download hf://tokhey/question_generation_model/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/tokhey/question_generation_model/resolve/main/training_args.bin
6.42 kB
- Xet hash:
- 5d82560328d9fe47d1bec0c220d9ad695aa3142efb6ce8fae4c4f1bb6b4cfdb5
- Size of remote file:
- 6.42 kB
- SHA256:
- 0bd190a5ed39dce6520c6416f429ce6f302771dd53405a5136a1b0b09b4a4f32
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