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vngrs-ai
/
VBART-Small-Base

Text Generation
Transformers
google-tensorflow TensorFlow
Safetensors
Turkish
mbart
text2text-generation
Model card Files Files and versions
xet
Community

Instructions to use vngrs-ai/VBART-Small-Base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use vngrs-ai/VBART-Small-Base with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-generation", model="vngrs-ai/VBART-Small-Base")
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
    
    tokenizer = AutoTokenizer.from_pretrained("vngrs-ai/VBART-Small-Base")
    model = AutoModelForSeq2SeqLM.from_pretrained("vngrs-ai/VBART-Small-Base")
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps
  • vLLM

    How to use vngrs-ai/VBART-Small-Base with vLLM:

    Install from pip and serve model
    # Install vLLM from pip:
    pip install vllm
    # Start the vLLM server:
    vllm serve "vngrs-ai/VBART-Small-Base"
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:8000/v1/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "vngrs-ai/VBART-Small-Base",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
    Use Docker
    docker model run hf.co/vngrs-ai/VBART-Small-Base
  • SGLang

    How to use vngrs-ai/VBART-Small-Base 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-ai/VBART-Small-Base" \
        --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-ai/VBART-Small-Base",
    		"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-ai/VBART-Small-Base" \
            --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-ai/VBART-Small-Base",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
  • Docker Model Runner

    How to use vngrs-ai/VBART-Small-Base with Docker Model Runner:

    docker model run hf.co/vngrs-ai/VBART-Small-Base
VBART-Small-Base
132 MB
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  • 1 contributor
History: 11 commits
meliksahturker's picture
meliksahturker
Update README.md
42fec30 verified about 2 years ago
  • .gitattributes
    1.52 kB
    initial commit about 2 years ago
  • README.md
    2.56 kB
    Update README.md about 2 years ago
  • config.json
    899 Bytes
    Upload MBartForConditionalGeneration about 2 years ago
  • generation_config.json
    216 Bytes
    Update generation_config.json about 2 years ago
  • model.safetensors
    64.5 MB
    xet
    Upload MBartForConditionalGeneration about 2 years ago
  • special_tokens_map.json
    124 Bytes
    Upload 3 files about 2 years ago
  • tf_model.h5
    64.7 MB
    xet
    Upload TFMBartForConditionalGeneration about 2 years ago
  • tokenizer.json
    2.44 MB
    Upload 3 files about 2 years ago
  • tokenizer_config.json
    298 Bytes
    Upload 3 files about 2 years ago