Instructions to use baarish/property-query-parser with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use baarish/property-query-parser with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="baarish/property-query-parser")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("baarish/property-query-parser", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use baarish/property-query-parser with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "baarish/property-query-parser" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "baarish/property-query-parser", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/baarish/property-query-parser
- SGLang
How to use baarish/property-query-parser 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 "baarish/property-query-parser" \ --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": "baarish/property-query-parser", "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 "baarish/property-query-parser" \ --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": "baarish/property-query-parser", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use baarish/property-query-parser with Docker Model Runner:
docker model run hf.co/baarish/property-query-parser
| language: "en" | |
| library_name: "transformers" | |
| pipeline_tag: "text2text-generation" | |
| base_model: "google/flan-t5-base" | |
| license: "apache-2.0" | |
| tags: | |
| - text2text | |
| - property | |
| - query-parser | |
| # 🏠 Property Query Parser | |
| This model helps extract structured information (like BHK, location, and possession date) from natural language property queries. | |
| Example usage: | |
| ```python | |
| from transformers import pipeline | |
| pipe = pipeline("text2text-generation", model="baarish/property-query-parser") | |
| query = "Show me 2BHK flats in Pune with possession in 2025" | |
| result = pipe(query) | |
| print(result[0]['generated_text']) | |