Instructions to use Jonathanmann/lyrics-main with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Jonathanmann/lyrics-main with Transformers:

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

pipe = pipeline("text-generation", model="Jonathanmann/lyrics-main")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Jonathanmann/lyrics-main")
model = AutoModelForCausalLM.from_pretrained("Jonathanmann/lyrics-main", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Jonathanmann/lyrics-main with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Jonathanmann/lyrics-main

SGLang

How to use Jonathanmann/lyrics-main 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 "Jonathanmann/lyrics-main" \
    --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": "Jonathanmann/lyrics-main",
		"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 "Jonathanmann/lyrics-main" \
        --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": "Jonathanmann/lyrics-main",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Jonathanmann/lyrics-main with Docker Model Runner:
```
docker model run hf.co/Jonathanmann/lyrics-main
```

lyrics-main

This model is a fine-tuned version of gpt2-medium on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 3.0601

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: 8
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 16
optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 500
num_epochs: 3
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss
3.7651	0.1550	50	3.4887
3.6031	0.3101	100	3.3498
3.4463	0.4651	150	3.2707
3.4016	0.6202	200	3.2325
3.3733	0.7752	250	3.2004
3.3827	0.9302	300	3.1764
3.1805	1.0853	350	3.1566
3.1353	1.2403	400	3.1435
3.1613	1.3953	450	3.1296
3.2172	1.5504	500	3.1210
3.1419	1.7054	550	3.1074
3.0979	1.8605	600	3.0954
3.1357	2.0155	650	3.0860
2.9513	2.1705	700	3.0827
2.9206	2.3256	750	3.0738
2.9016	2.4806	800	3.0678
2.9043	2.6357	850	3.0620
3.0086	2.7907	900	3.0606
2.9851	2.9457	950	3.0601

Framework versions

Transformers 4.46.2
Pytorch 2.5.1+cu121
Tokenizers 0.20.3

Downloads last month: 4

Safetensors

Model size

0.4B params

Tensor type

F32

Model tree for Jonathanmann/lyrics-main

Base model

openai-community/gpt2-medium

Finetuned

(197)

this model