Instructions to use wvangils/CTRL-Beatles-Lyrics-finetuned-newlyrics with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use wvangils/CTRL-Beatles-Lyrics-finetuned-newlyrics with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="wvangils/CTRL-Beatles-Lyrics-finetuned-newlyrics")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("wvangils/CTRL-Beatles-Lyrics-finetuned-newlyrics")
model = AutoModelForCausalLM.from_pretrained("wvangils/CTRL-Beatles-Lyrics-finetuned-newlyrics", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use wvangils/CTRL-Beatles-Lyrics-finetuned-newlyrics with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "wvangils/CTRL-Beatles-Lyrics-finetuned-newlyrics"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "wvangils/CTRL-Beatles-Lyrics-finetuned-newlyrics",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/wvangils/CTRL-Beatles-Lyrics-finetuned-newlyrics

SGLang

How to use wvangils/CTRL-Beatles-Lyrics-finetuned-newlyrics 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 "wvangils/CTRL-Beatles-Lyrics-finetuned-newlyrics" \
    --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": "wvangils/CTRL-Beatles-Lyrics-finetuned-newlyrics",
		"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 "wvangils/CTRL-Beatles-Lyrics-finetuned-newlyrics" \
        --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": "wvangils/CTRL-Beatles-Lyrics-finetuned-newlyrics",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use wvangils/CTRL-Beatles-Lyrics-finetuned-newlyrics with Docker Model Runner:
```
docker model run hf.co/wvangils/CTRL-Beatles-Lyrics-finetuned-newlyrics
```

CTRL-Beatles-Lyrics-finetuned-newlyrics / README.md

wvangils

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metadata

tags:
  - generated_from_trainer
datasets: cmotions/Beatles_lyrics
model-index:
  - name: CTRL-Beatles-Lyrics-finetuned-newlyrics
    results: []

CTRL-Beatles-Lyrics-finetuned-newlyrics

This model is a fine-tuned version of sshleifer/tiny-ctrl on the Cmotions - Beatles lyrics dataset. It will complete an input prompt with Beatles-like text.

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 5e-05
train_batch_size: 4
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 100
num_epochs: 5

Training results

Training Loss	Epoch	Step	Validation Loss
12.361	1.0	35	12.3685
12.3529	2.0	70	12.3583
12.3374	3.0	105	12.3401
12.3158	4.0	140	12.3237
12.301	5.0	175	12.3180

Framework versions

Transformers 4.19.2
Pytorch 1.11.0+cu113
Datasets 2.2.2
Tokenizers 0.12.1