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lmqg
/
mt5-base-esquad-qag

Text Generation
Transformers
PyTorch
Spanish
mt5
text2text-generation
questions and answers generation
Eval Results (legacy)
Model card Files Files and versions
xet
Community
1

Instructions to use lmqg/mt5-base-esquad-qag with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use lmqg/mt5-base-esquad-qag with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-generation", model="lmqg/mt5-base-esquad-qag")
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
    
    tokenizer = AutoTokenizer.from_pretrained("lmqg/mt5-base-esquad-qag")
    model = AutoModelForSeq2SeqLM.from_pretrained("lmqg/mt5-base-esquad-qag")
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps
  • vLLM

    How to use lmqg/mt5-base-esquad-qag with vLLM:

    Install from pip and serve model
    # Install vLLM from pip:
    pip install vllm
    # Start the vLLM server:
    vllm serve "lmqg/mt5-base-esquad-qag"
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:8000/v1/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "lmqg/mt5-base-esquad-qag",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
    Use Docker
    docker model run hf.co/lmqg/mt5-base-esquad-qag
  • SGLang

    How to use lmqg/mt5-base-esquad-qag 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 "lmqg/mt5-base-esquad-qag" \
        --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": "lmqg/mt5-base-esquad-qag",
    		"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 "lmqg/mt5-base-esquad-qag" \
            --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": "lmqg/mt5-base-esquad-qag",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
  • Docker Model Runner

    How to use lmqg/mt5-base-esquad-qag with Docker Model Runner:

    docker model run hf.co/lmqg/mt5-base-esquad-qag
mt5-base-esquad-qag
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  • 1 contributor
History: 7 commits
asahi417's picture
asahi417
model update
e51a18b over 3 years ago
  • eval
    model update over 3 years ago
  • .gitattributes
    1.53 kB
    add tokenizer over 3 years ago
  • README.md
    5.27 kB
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  • added_tokens.json
    21 Bytes
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  • config.json
    825 Bytes
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  • pytorch_model.bin

    Detected Pickle imports (3)

    • "torch._utils._rebuild_tensor_v2",
    • "torch.FloatStorage",
    • "collections.OrderedDict"

    What is a pickle import?

    2.33 GB
    xet
    add model over 3 years ago
  • special_tokens_map.json
    123 Bytes
    add tokenizer over 3 years ago
  • spiece.model
    4.31 MB
    xet
    add tokenizer over 3 years ago
  • tokenizer.json
    16.3 MB
    xet
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  • tokenizer_config.json
    433 Bytes
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  • trainer_config.json
    357 Bytes
    model update over 3 years ago