Text Generation
Transformers
PyTorch
English
t5
text2text-generation
nl2sql
text-generation-inference
Instructions to use LarkAI/codet5p-770m_nl2sql_oig with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LarkAI/codet5p-770m_nl2sql_oig with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="LarkAI/codet5p-770m_nl2sql_oig")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("LarkAI/codet5p-770m_nl2sql_oig") model = AutoModelForSeq2SeqLM.from_pretrained("LarkAI/codet5p-770m_nl2sql_oig", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use LarkAI/codet5p-770m_nl2sql_oig with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "LarkAI/codet5p-770m_nl2sql_oig" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "LarkAI/codet5p-770m_nl2sql_oig", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/LarkAI/codet5p-770m_nl2sql_oig
- SGLang
How to use LarkAI/codet5p-770m_nl2sql_oig 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/codet5p-770m_nl2sql_oig" \ --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/codet5p-770m_nl2sql_oig", "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/codet5p-770m_nl2sql_oig" \ --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/codet5p-770m_nl2sql_oig", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use LarkAI/codet5p-770m_nl2sql_oig with Docker Model Runner:
docker model run hf.co/LarkAI/codet5p-770m_nl2sql_oig
Commit ·
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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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```
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import torch
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from transformers import T5ForConditionalGeneration, AutoTokenizer
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device = torch.device("cuda:0")
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tokenizer = AutoTokenizer.from_pretrained("LarkAI/codet5p-770m_nl2sql_oig")
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model = T5ForConditionalGeneration.from_pretrained("LarkAI/codet5p-770m_nl2sql_oig").to(device)
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text = "Given the following schema:\nroad (road_name, state_name)\nstate (state_name, capital, population, area, country_name, density)\nhighlow (state_name, highest_point, highest_elevation, lowest_point, lowest_elevation)\nlake (lake_name, area, state_name, country_name)\nriver (river_name, length, traverse, country_name)\nborder_info (state_name, border)\nmountain (mountain_name, mountain_altitude, state_name, country_name)\ncity (city_name, state_name, population, country_name)\nWrite a SQL query to what states does the mississippi river run throug"
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inputs = tokenizer.encode(text, return_tensors="pt").to(device)
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output_ids = model.generate(inputs, max_length=512)
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response_text = self.tokenizer.decode(outputs[0], skip_special_tokens=True)
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# SELECT traverse FROM river WHERE river_name = \"mississippi\" ;
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```
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