Text Generation
Transformers
PyTorch
JAX
English
gpt2
huggingartists
lyrics
lm-head
causal-lm
text-generation-inference
Instructions to use huggingartists/25-17 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use huggingartists/25-17 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="huggingartists/25-17")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("huggingartists/25-17") model = AutoModelForCausalLM.from_pretrained("huggingartists/25-17", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use huggingartists/25-17 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "huggingartists/25-17" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "huggingartists/25-17", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/huggingartists/25-17
- SGLang
How to use huggingartists/25-17 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/25-17" \ --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/25-17", "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/25-17" \ --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/25-17", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use huggingartists/25-17 with Docker Model Runner:
docker model run hf.co/huggingartists/25-17
Download optimizer.pt from huggingartists/25-17: direct link, hf CLI and curl.
- Browser
- Download file 996 MB
-
https://huggingface.co/huggingartists/25-17/resolve/main/optimizer.pt
- Command line
-
hf download hf://huggingartists/25-17/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/huggingartists/25-17/resolve/main/optimizer.pt
996 MB
- Xet hash:
- 812a8e2cdd5768ea20230aa964a40680468ac0bfffbdbed09f9520660545147b
- Size of remote file:
- 996 MB
- SHA256:
- 43b7fdb4985b5042f3736c16d0dd1a10c208219dfb95c8d516960509f84117c5
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