Instructions to use niclasfw/schlager-bot-004 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use niclasfw/schlager-bot-004 with Transformers:

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

pipe = pipeline("text-generation", model="niclasfw/schlager-bot-004", trust_remote_code=True)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("niclasfw/schlager-bot-004", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("niclasfw/schlager-bot-004", trust_remote_code=True)

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use niclasfw/schlager-bot-004 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "niclasfw/schlager-bot-004"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "niclasfw/schlager-bot-004",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/niclasfw/schlager-bot-004

SGLang

How to use niclasfw/schlager-bot-004 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 "niclasfw/schlager-bot-004" \
    --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": "niclasfw/schlager-bot-004",
		"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 "niclasfw/schlager-bot-004" \
        --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": "niclasfw/schlager-bot-004",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use niclasfw/schlager-bot-004 with Docker Model Runner:
```
docker model run hf.co/niclasfw/schlager-bot-004
```

schlager-bot-004

This model is a fine-tuned version of LeoLM/leo-hessianai-7b on a dataset of 1048 schlager song lyrics. Schlager songs form a genre of German music and, therefore, the input should be in German as well to ensure best results.

Model description

The model takes a verse (in German) and uses it to generate the text to a schlager song.

Intended uses & limitations

This model is not intended for commercial use of any kind as the dataset used for fine-tuning contains propietary information.

Training and evaluation data

The training and evaluation data was extracted from Spotify via an API.

Training procedure

The model was fine-tuned using a Google colab notebook with a V100 GPU and High RAM. For details, please see the Github repository (https://github.com/NiclasFenton-Wiegleb/schlager-lyrics-bot/tree/main)

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.0005
train_batch_size: 4
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 8
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: constant
lr_scheduler_warmup_ratio: 0.03
num_epochs: 10

Framework versions

Transformers 4.34.0
Pytorch 2.1.0+cu118
Datasets 2.13.0
Tokenizers 0.14.1

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