Text Generation
Transformers
PyTorch
JAX
English
gpt2
huggingartists
lyrics
lm-head
causal-lm
text-generation-inference
Instructions to use huggingartists/100-gecs with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use huggingartists/100-gecs with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="huggingartists/100-gecs")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("huggingartists/100-gecs") model = AutoModelForCausalLM.from_pretrained("huggingartists/100-gecs", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use huggingartists/100-gecs with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "huggingartists/100-gecs" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "huggingartists/100-gecs", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/huggingartists/100-gecs
- SGLang
How to use huggingartists/100-gecs 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 "huggingartists/100-gecs" \ --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": "huggingartists/100-gecs", "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 "huggingartists/100-gecs" \ --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": "huggingartists/100-gecs", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use huggingartists/100-gecs with Docker Model Runner:
docker model run hf.co/huggingartists/100-gecs
| { | |
| "best_metric": 2.6175992488861084, | |
| "best_model_checkpoint": "output/100-gecs/checkpoint-18", | |
| "epoch": 1.0, | |
| "global_step": 18, | |
| "is_hyper_param_search": false, | |
| "is_local_process_zero": true, | |
| "is_world_process_zero": true, | |
| "log_history": [ | |
| { | |
| "epoch": 0.28, | |
| "learning_rate": 0.00011269523002449659, | |
| "loss": 2.8358, | |
| "step": 5 | |
| }, | |
| { | |
| "epoch": 0.56, | |
| "learning_rate": 5.668773501204858e-05, | |
| "loss": 2.6935, | |
| "step": 10 | |
| }, | |
| { | |
| "epoch": 0.83, | |
| "learning_rate": 9.190657300387505e-06, | |
| "loss": 2.5145, | |
| "step": 15 | |
| }, | |
| { | |
| "epoch": 1.0, | |
| "eval_loss": 2.6175992488861084, | |
| "eval_runtime": 1.2504, | |
| "eval_samples_per_second": 20.794, | |
| "eval_steps_per_second": 3.199, | |
| "step": 18 | |
| } | |
| ], | |
| "max_steps": 18, | |
| "num_train_epochs": 1, | |
| "total_flos": 18682380288000.0, | |
| "trial_name": null, | |
| "trial_params": null | |
| } | |