Instructions to use LarkAI/bart_large_nl2sql with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LarkAI/bart_large_nl2sql with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="LarkAI/bart_large_nl2sql")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("LarkAI/bart_large_nl2sql") model = AutoModel.from_pretrained("LarkAI/bart_large_nl2sql") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use LarkAI/bart_large_nl2sql with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "LarkAI/bart_large_nl2sql" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "LarkAI/bart_large_nl2sql", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/LarkAI/bart_large_nl2sql
- SGLang
How to use LarkAI/bart_large_nl2sql 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 "LarkAI/bart_large_nl2sql" \ --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": "LarkAI/bart_large_nl2sql", "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 "LarkAI/bart_large_nl2sql" \ --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": "LarkAI/bart_large_nl2sql", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use LarkAI/bart_large_nl2sql with Docker Model Runner:
docker model run hf.co/LarkAI/bart_large_nl2sql
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README.md
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pipeline_tag: text2text-generation
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tags:
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- nl2sql
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pipeline_tag: text2text-generation
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tags:
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- nl2sql
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---
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# How to Use
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```python
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from transformers import AutoTokenizer, BartForConditionalGeneration
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device = torch.device('cuda:0')
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tokenizer = AutoTokenizer.from_pretrained("LarkAI/bart_large_nl2sql")
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model = BartForConditionalGeneration.from_pretrained("LarkAI/bart_large_nl2sql").to(device)
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text = "question: which club was in toronto 2003-06 table: Player,No.,Nationality,Position,Years in Toronto,School/Club Team"
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inputs = tokenizer([text], max_length=1024, return_tensors="pt")
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output_ids = model.generate(inputs["input_ids"].to(self.device), num_beams=self.beams, max_length=128, min_length=8)
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response_text = tokenizer.batch_decode(output_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
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# select School/Club Team from product where Years in Toronto = 2003-06
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```
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reference: https://huggingface.co/juierror/flan-t5-text2sql-with-schema
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