Instructions to use vngrs/VBART-XLarge-Title-Generation-from-Spot with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use vngrs/VBART-XLarge-Title-Generation-from-Spot with Transformers:

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

pipe = pipeline("text-generation", model="vngrs/VBART-XLarge-Title-Generation-from-Spot")

# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("vngrs/VBART-XLarge-Title-Generation-from-Spot")
model = AutoModelForSeq2SeqLM.from_pretrained("vngrs/VBART-XLarge-Title-Generation-from-Spot", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use vngrs/VBART-XLarge-Title-Generation-from-Spot with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/vngrs/VBART-XLarge-Title-Generation-from-Spot

SGLang

How to use vngrs/VBART-XLarge-Title-Generation-from-Spot 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 "vngrs/VBART-XLarge-Title-Generation-from-Spot" \
    --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": "vngrs/VBART-XLarge-Title-Generation-from-Spot",
		"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 "vngrs/VBART-XLarge-Title-Generation-from-Spot" \
        --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": "vngrs/VBART-XLarge-Title-Generation-from-Spot",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use vngrs/VBART-XLarge-Title-Generation-from-Spot with Docker Model Runner:
```
docker model run hf.co/vngrs/VBART-XLarge-Title-Generation-from-Spot
```

VBART-XLarge-Title-Generation-from-Spot / config.json

meliksahturker

Upload MBartForConditionalGeneration

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	{
	"_name_or_path": "tfhf_model_weights.15-2.5177-2.9868.hdf5",
	"activation_dropout": 0.0,
	"activation_function": "gelu",
	"architectures": [
	"MBartForConditionalGeneration"
	],
	"attention_dropout": 0.0,
	"bos_token_id": 2,
	"classifier_dropout": 0.0,
	"d_model": 1024,
	"decoder_attention_heads": 16,
	"decoder_ffn_dim": 4096,
	"decoder_layerdrop": 0.0,
	"decoder_layers": 24,
	"decoder_start_token_id": 2,
	"dropout": 0.1,
	"encoder_attention_heads": 16,
	"encoder_ffn_dim": 4096,
	"encoder_layerdrop": 0.0,
	"encoder_layers": 24,
	"eos_token_id": 3,
	"forced_eos_token_id": 3,
	"init_std": 0.02,
	"is_encoder_decoder": true,
	"max_position_embeddings": 1024,
	"model_type": "mbart",
	"num_hidden_layers": 24,
	"pad_token_id": 0,
	"scale_embedding": false,
	"torch_dtype": "float32",
	"transformers_version": "4.38.2",
	"use_cache": true,
	"vocab_size": 32000
	}