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")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("IThinkUPC/SQLGenerator-AI", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- 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
Commit ·
5124d8f
1
Parent(s): ce24db8
Upload model
Browse files- adapter_config.json +22 -0
- adapter_model.bin +3 -0
adapter_config.json
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{
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"base_model_name_or_path": "bigscience/bloom-1b7",
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"bias": "none",
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"enable_lora": [
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true,
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false,
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true
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],
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"fan_in_fan_out": true,
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"inference_mode": true,
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"init_lora_weights": true,
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"lora_alpha": 32,
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"lora_dropout": 0.05,
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"merge_weights": false,
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"modules_to_save": null,
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"peft_type": "LORA",
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"r": 16,
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"target_modules": [
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"query_key_value"
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],
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"task_type": "CAUSAL_LM"
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}
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adapter_model.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:22a9b6b3e33e7c46e102bc7097746198cc14f3aa2d931c1f65d10b679523615f
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size 12600833
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