Instructions to use metoonhathung/music-generation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use metoonhathung/music-generation with Transformers:

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

pipe = pipeline("text-generation", model="metoonhathung/music-generation")

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

tokenizer = AutoTokenizer.from_pretrained("metoonhathung/music-generation")
model = AutoModelForCausalLM.from_pretrained("metoonhathung/music-generation", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use metoonhathung/music-generation with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/metoonhathung/music-generation

SGLang

How to use metoonhathung/music-generation 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 "metoonhathung/music-generation" \
    --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": "metoonhathung/music-generation",
		"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 "metoonhathung/music-generation" \
        --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": "metoonhathung/music-generation",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use metoonhathung/music-generation with Docker Model Runner:
```
docker model run hf.co/metoonhathung/music-generation
```

music-generation

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

Loss: 0.5312

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: 0.0001
train_batch_size: 16
eval_batch_size: 16
seed: 42
gradient_accumulation_steps: 16
total_train_batch_size: 256
optimizer: Use OptimizerNames.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: 100
num_epochs: 20
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss
3.7238	0.9217	100	2.8460
2.4643	1.8387	200	1.8829
1.8339	2.7558	300	1.4234
1.5013	3.6728	400	1.2203
1.3125	4.5899	500	1.0966
1.1899	5.5069	600	1.0028
1.0982	6.4240	700	0.9353
1.0302	7.3410	800	0.8779
0.9766	8.2581	900	0.8276
0.9243	9.1751	1000	0.7757
0.8825	10.0922	1100	0.7345
0.845	11.0092	1200	0.7000
0.8083	11.9309	1300	0.6624
0.7784	12.8479	1400	0.6328
0.7502	13.7650	1500	0.6052
0.7281	14.6820	1600	0.5816
0.7072	15.5991	1700	0.5622
0.6903	16.5161	1800	0.5486
0.6796	17.4332	1900	0.5386
0.6705	18.3502	2000	0.5335
0.6646	19.2673	2100	0.5312

Framework versions

Transformers 4.55.0
Pytorch 2.6.0+cu124
Datasets 4.0.0
Tokenizers 0.21.4

Downloads last month: 96

Safetensors

Model size

86.3M params

Tensor type

F32

Model tree for metoonhathung/music-generation

Base model

openai-community/gpt2

Finetuned

(2290)

this model