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RealMati
/
t2sql_v6_structured

Text Generation
Transformers
Safetensors
t5
text2text-generation
sql
text-to-sql
wikisql
text-generation-inference
Model card Files Files and versions
xet
Community

Instructions to use RealMati/t2sql_v6_structured with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use RealMati/t2sql_v6_structured with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-generation", model="RealMati/t2sql_v6_structured")
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
    
    tokenizer = AutoTokenizer.from_pretrained("RealMati/t2sql_v6_structured")
    model = AutoModelForSeq2SeqLM.from_pretrained("RealMati/t2sql_v6_structured")
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps
  • vLLM

    How to use RealMati/t2sql_v6_structured with vLLM:

    Install from pip and serve model
    # Install vLLM from pip:
    pip install vllm
    # Start the vLLM server:
    vllm serve "RealMati/t2sql_v6_structured"
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:8000/v1/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "RealMati/t2sql_v6_structured",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
    Use Docker
    docker model run hf.co/RealMati/t2sql_v6_structured
  • SGLang

    How to use RealMati/t2sql_v6_structured 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 "RealMati/t2sql_v6_structured" \
        --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": "RealMati/t2sql_v6_structured",
    		"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 "RealMati/t2sql_v6_structured" \
            --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": "RealMati/t2sql_v6_structured",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
  • Docker Model Runner

    How to use RealMati/t2sql_v6_structured with Docker Model Runner:

    docker model run hf.co/RealMati/t2sql_v6_structured
t2sql_v6_structured
895 MB
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  • 1 contributor
History: 4 commits
RealMati's picture
RealMati
Fix pipeline_tag to text-generation
d7473da verified 3 months ago
  • .gitattributes
    1.52 kB
    initial commit 3 months ago
  • README.md
    668 Bytes
    Fix pipeline_tag to text-generation 3 months ago
  • config.json
    1.47 kB
    Upload folder using huggingface_hub 3 months ago
  • generation_config.json
    122 Bytes
    Upload folder using huggingface_hub 3 months ago
  • model.safetensors
    892 MB
    xet
    Upload folder using huggingface_hub 3 months ago
  • special_tokens_map.json
    2.2 kB
    Upload folder using huggingface_hub 3 months ago
  • spiece.model
    792 kB
    xet
    Upload folder using huggingface_hub 3 months ago
  • tokenizer.json
    2.42 MB
    Upload folder using huggingface_hub 3 months ago
  • tokenizer_config.json
    20.8 kB
    Upload folder using huggingface_hub 3 months ago
  • training_args.bin
    5.97 kB
    xet
    Upload folder using huggingface_hub 3 months ago