Instructions to use lamrin8224/articles_gpt2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lamrin8224/articles_gpt2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="lamrin8224/articles_gpt2")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("lamrin8224/articles_gpt2") model = AutoModelForCausalLM.from_pretrained("lamrin8224/articles_gpt2") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use lamrin8224/articles_gpt2 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "lamrin8224/articles_gpt2" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "lamrin8224/articles_gpt2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/lamrin8224/articles_gpt2
- SGLang
How to use lamrin8224/articles_gpt2 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 "lamrin8224/articles_gpt2" \ --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": "lamrin8224/articles_gpt2", "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 "lamrin8224/articles_gpt2" \ --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": "lamrin8224/articles_gpt2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use lamrin8224/articles_gpt2 with Docker Model Runner:
docker model run hf.co/lamrin8224/articles_gpt2
| { | |
| "best_metric": null, | |
| "best_model_checkpoint": null, | |
| "epoch": 3.9840637450199203, | |
| "global_step": 2000, | |
| "is_hyper_param_search": false, | |
| "is_local_process_zero": true, | |
| "is_world_process_zero": true, | |
| "log_history": [ | |
| { | |
| "epoch": 1.0, | |
| "learning_rate": 4.00398406374502e-05, | |
| "loss": 3.6087, | |
| "step": 500 | |
| }, | |
| { | |
| "epoch": 1.99, | |
| "learning_rate": 3.00796812749004e-05, | |
| "loss": 3.242, | |
| "step": 1000 | |
| }, | |
| { | |
| "epoch": 2.99, | |
| "learning_rate": 2.01195219123506e-05, | |
| "loss": 3.0879, | |
| "step": 1500 | |
| }, | |
| { | |
| "epoch": 3.98, | |
| "learning_rate": 1.0159362549800798e-05, | |
| "loss": 2.9902, | |
| "step": 2000 | |
| } | |
| ], | |
| "max_steps": 2510, | |
| "num_train_epochs": 5, | |
| "total_flos": 3055939046866944.0, | |
| "trial_name": null, | |
| "trial_params": null | |
| } | |