Text Generation
Transformers
PyTorch
JAX
English
gpt2
huggingartists
lyrics
lm-head
causal-lm
text-generation-inference
Instructions to use huggingartists/bladee with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use huggingartists/bladee with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="huggingartists/bladee")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("huggingartists/bladee") model = AutoModelForCausalLM.from_pretrained("huggingartists/bladee", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use huggingartists/bladee with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "huggingartists/bladee" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "huggingartists/bladee", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/huggingartists/bladee
- SGLang
How to use huggingartists/bladee 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/bladee" \ --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/bladee", "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/bladee" \ --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/bladee", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use huggingartists/bladee with Docker Model Runner:
docker model run hf.co/huggingartists/bladee
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| "best_metric": 3.1062893867492676, | |
| "best_model_checkpoint": "output/bladee/checkpoint-46", | |
| "epoch": 1.0, | |
| "global_step": 46, | |
| "is_hyper_param_search": false, | |
| "is_local_process_zero": true, | |
| "is_world_process_zero": true, | |
| "log_history": [ | |
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| "step": 15 | |
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| "epoch": 0.43, | |
| "learning_rate": 8.255708249541069e-05, | |
| "loss": 2.6681, | |
| "step": 20 | |
| }, | |
| { | |
| "epoch": 0.54, | |
| "learning_rate": 5.9258967871997494e-05, | |
| "loss": 2.6912, | |
| "step": 25 | |
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| "epoch": 0.65, | |
| "learning_rate": 3.703953841164296e-05, | |
| "loss": 2.7339, | |
| "step": 30 | |
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| "epoch": 0.76, | |
| "learning_rate": 1.8464652850520685e-05, | |
| "loss": 2.6054, | |
| "step": 35 | |
| }, | |
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| "learning_rate": 5.679304716725937e-06, | |
| "loss": 2.6629, | |
| "step": 40 | |
| }, | |
| { | |
| "epoch": 0.98, | |
| "learning_rate": 1.5992243352900665e-07, | |
| "loss": 2.625, | |
| "step": 45 | |
| }, | |
| { | |
| "epoch": 1.0, | |
| "eval_loss": 3.1062893867492676, | |
| "eval_runtime": 0.8504, | |
| "eval_samples_per_second": 76.437, | |
| "eval_steps_per_second": 10.584, | |
| "step": 46 | |
| } | |
| ], | |
| "max_steps": 5888, | |
| "num_train_epochs": 128, | |
| "total_flos": 47947087872000.0, | |
| "trial_name": null, | |
| "trial_params": null | |
| } | |