Text Generation
Transformers
Safetensors
English
gpt2
gpt-2
education
student-advisor
suggestions
casual-lm
text-generation-inference
Instructions to use LyubomirT/gpt2-student-suggester with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LyubomirT/gpt2-student-suggester with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="LyubomirT/gpt2-student-suggester")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("LyubomirT/gpt2-student-suggester") model = AutoModelForCausalLM.from_pretrained("LyubomirT/gpt2-student-suggester", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use LyubomirT/gpt2-student-suggester with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "LyubomirT/gpt2-student-suggester" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "LyubomirT/gpt2-student-suggester", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/LyubomirT/gpt2-student-suggester
- SGLang
How to use LyubomirT/gpt2-student-suggester 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 "LyubomirT/gpt2-student-suggester" \ --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": "LyubomirT/gpt2-student-suggester", "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 "LyubomirT/gpt2-student-suggester" \ --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": "LyubomirT/gpt2-student-suggester", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use LyubomirT/gpt2-student-suggester with Docker Model Runner:
docker model run hf.co/LyubomirT/gpt2-student-suggester
Download model.safetensors from LyubomirT/gpt2-student-suggester: direct link, hf CLI and curl.
- Browser
- Download file 498 MB
-
https://huggingface.co/LyubomirT/gpt2-student-suggester/resolve/main/model.safetensors
- Command line
-
hf download hf://LyubomirT/gpt2-student-suggester/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/LyubomirT/gpt2-student-suggester/resolve/main/model.safetensors
498 MB
- Xet hash:
- 63c0ed1e3199e01f6d0400d982789f8f1443508caee1d12c59fbf6cb989675ae
- Size of remote file:
- 498 MB
- SHA256:
- af39c6f70c09cc400a65ca97070cb06ec8279ee7c1346648ef89d725f41677f1
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