Instructions to use IThinkUPC/SQLGenerator-AI with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use IThinkUPC/SQLGenerator-AI with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="IThinkUPC/SQLGenerator-AI")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("IThinkUPC/SQLGenerator-AI", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use IThinkUPC/SQLGenerator-AI with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "IThinkUPC/SQLGenerator-AI" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "IThinkUPC/SQLGenerator-AI", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/IThinkUPC/SQLGenerator-AI
- SGLang
How to use IThinkUPC/SQLGenerator-AI 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 "IThinkUPC/SQLGenerator-AI" \ --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": "IThinkUPC/SQLGenerator-AI", "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 "IThinkUPC/SQLGenerator-AI" \ --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": "IThinkUPC/SQLGenerator-AI", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use IThinkUPC/SQLGenerator-AI with Docker Model Runner:
docker model run hf.co/IThinkUPC/SQLGenerator-AI
Download adapter_model.bin from IThinkUPC/SQLGenerator-AI: direct link, hf CLI and curl.
- Browser
- Download file 12.6 MB
-
https://huggingface.co/IThinkUPC/SQLGenerator-AI/resolve/main/adapter_model.bin
- Command line
-
hf download hf://IThinkUPC/SQLGenerator-AI/adapter_model.bin
-
curl -L -o adapter_model.bin https://huggingface.co/IThinkUPC/SQLGenerator-AI/resolve/main/adapter_model.bin
12.6 MB
- Xet hash:
- 73d9ae46c1ef7a11673cbaea3d1a15551f2b56a3318603af242af680232dd676
- Size of remote file:
- 12.6 MB
- SHA256:
- 22a9b6b3e33e7c46e102bc7097746198cc14f3aa2d931c1f65d10b679523615f
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