Instructions to use igorktech/t5-base-lyrics-explainer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use igorktech/t5-base-lyrics-explainer with Transformers:

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

pipe = pipeline("text-generation", model="igorktech/t5-base-lyrics-explainer")

# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("igorktech/t5-base-lyrics-explainer")
model = AutoModelForSeq2SeqLM.from_pretrained("igorktech/t5-base-lyrics-explainer")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use igorktech/t5-base-lyrics-explainer with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "igorktech/t5-base-lyrics-explainer"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "igorktech/t5-base-lyrics-explainer",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/igorktech/t5-base-lyrics-explainer

SGLang

How to use igorktech/t5-base-lyrics-explainer 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 "igorktech/t5-base-lyrics-explainer" \
    --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": "igorktech/t5-base-lyrics-explainer",
		"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 "igorktech/t5-base-lyrics-explainer" \
        --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": "igorktech/t5-base-lyrics-explainer",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use igorktech/t5-base-lyrics-explainer with Docker Model Runner:
```
docker model run hf.co/igorktech/t5-base-lyrics-explainer
```

t5-base-lyrics-explainer / config.json

igorktech

Upload T5ForConditionalGeneration

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	{
	"_name_or_path": "igorktech/t5-base-en",
	"architectures": [
	"T5ForConditionalGeneration"
	],
	"d_ff": 2048,
	"d_kv": 64,
	"d_model": 768,
	"decoder_start_token_id": 0,
	"dense_act_fn": "gelu_new",
	"dropout_rate": 0.1,
	"eos_token_id": 1,
	"feed_forward_proj": "gated-gelu",
	"initializer_factor": 1.0,
	"is_encoder_decoder": true,
	"is_gated_act": true,
	"layer_norm_epsilon": 1e-06,
	"model_type": "t5",
	"num_decoder_layers": 12,
	"num_heads": 12,
	"num_layers": 12,
	"output_past": true,
	"pad_token_id": 0,
	"relative_attention_max_distance": 128,
	"relative_attention_num_buckets": 32,
	"tie_word_embeddings": false,
	"tokenizer_class": "T5Tokenizer",
	"torch_dtype": "float32",
	"transformers_version": "4.27.2",
	"use_cache": true,
	"vocab_size": 20000
	}