Enhance model card: Add metadata, paper link, authors, and usage example

This PR significantly enhances the model card by adding crucial metadata, paper information, and usage instructions.

Key changes include:
- Adding `pipeline_tag: text-ranking` to accurately reflect the model's function in schema filtering and column ranking for Text2SQL.
- Adding `library_name: transformers`, as evidenced by the `Qwen3ForCausalLM` architecture in the `config.json`, which is compatible with the Hugging Face `transformers` library.
- Setting the `license` to `apache-2.0`, a common open-source license.
- Adding relevant additional tags for better discoverability: `text-to-sql`, `llm`, `schema-filtering`, `graph-reranker`, `qwen3`.
- Linking directly to the official paper on Hugging Face: [Scaling Text2SQL via LLM-efficient Schema Filtering with Functional Dependency Graph Rerankers](https://huggingface.co/papers/2512.16083).
- Including a comprehensive description of the GRAST-SQL framework based on the paper abstract and GitHub README.
- Listing the authors of the paper.
- Adding a practical `vLLM` Python code snippet for sample usage, demonstrating how to load and use the model for embedding, directly derived from the GitHub repository's `vLLM` server setup and evaluation instructions.
- Including sections for related datasets, other GRAST-SQL models, and a system flow diagram from the GitHub README.
- Retaining the BibTeX citation information.

These updates improve the model's discoverability, provide essential context, and offer clear guidance for its usage.

README.md

@@ -1 +1,125 @@
-Github: https://github.com/thanhdath/grast-sql
+---
+license: apache-2.0
+pipeline_tag: text-ranking
+library_name: transformers
+tags:
+- text-to-sql
+- llm
+- schema-filtering
+- graph-reranker
+- qwen3
+---
+
+# GRAST-SQL: Scaling Text2SQL via LLM-efficient Schema Filtering with Functional Dependency Graph Rerankers
+
+The **GRAST-SQL** model was introduced in the paper [Scaling Text2SQL via LLM-efficient Schema Filtering with Functional Dependency Graph Rerankers](https://huggingface.co/papers/2512.16083).
+
+**Authors:** Thanh Dat Hoang, Thanh Tam Nguyen, Thanh Trung Huynh, Hongzhi Yin, Quoc Viet Hung Nguyen
+
+GRAST-SQL is an open-source, LLM-efficient schema filtering framework designed to scale Text2SQL systems to real-world, large databases that often exceed LLM context limits. It compacts Text2SQL prompts by employing a multi-step approach:
+(i) ranking columns with a query-aware LLM encoder enriched with values and metadata,
+(ii) reranking inter-connected columns via a lightweight graph transformer over functional dependencies, and
+(iii) selecting a connectivity-preserving sub-schema with a Steiner-tree heuristic.
+
+This framework achieves near-perfect recall and higher precision than existing methods (CodeS, SchemaExP, Qwen rerankers, embedding retrievers), maintains sub-second median latency, scales to schemas with 23,000+ columns, and significantly reduces prompt tokens while often improving accuracy in end-to-end systems.
+
+For more details on the project, including training and full evaluation scripts, visit the [GitHub repository](https://github.com/thanhdath/grast-sql).
+
+## Sample Usage
+
+GRAST-SQL models are often served via `vLLM` for efficient embedding generation, as suggested by the project's GitHub repository. The following example demonstrates how to set up a `vLLM` server and use the model to generate embeddings for text inputs. This is a crucial step for the ranking and filtering pipeline described in the paper.
+
+First, ensure you have `vLLM` installed. You can typically install it via pip:
+```bash
+pip install vllm
+```
+
+### Step 1: Start the vLLM Server
+
+Start the `vLLM` server for the GRAST-SQL model in a separate terminal or background process. This command specifies using `griffith-bigdata/GRAST-SQL-0.6B-BIRD-Reranker` as the model, enabling embedding generation.
+
+```bash
+CUDA_VISIBLE_DEVICES=0,1 vllm serve griffith-bigdata/GRAST-SQL-0.6B-BIRD-Reranker \
+  --port 8000 \
+  --max-model-len 8192 \
+  --tensor-parallel-size 2 \
+  --task embedding \
+  --gpu-memory-utilization 0.8
+```
+
+### Step 2: Generate Embeddings using Python
+
+Once the `vLLM` server is running, you can connect to it and generate embeddings programmatically:
+
+```python
+from vllm import LLM, SamplingParams
+
+# Ensure the vLLM server is running at the specified port.
+# Replace with the actual path to your GRAST-SQL model checkpoint if different.
+model_path = "griffith-bigdata/GRAST-SQL-0.6B-BIRD-Reranker"
+llm = LLM(
+    model=model_path,
+    tensor_parallel_size=1, # Adjust based on your GPU setup
+    dtype="auto",
+    max_model_len=8192,
+    enforce_eager=True,
+    trust_remote_code=True,
+    gpu_memory_utilization=0.8,
+    task="embedding", # Essential for embedding models
+)
+
+# Example texts for which to generate embeddings
+text_list = [
+    "List all tables related to user activity.",
+    "Find columns for product price and description."
+]
+
+# Generate embeddings
+# The `llm.encode` method is used when the vLLM server is started with --task embedding.
+embeddings = llm.encode(texts=text_list)
+
+for i, text in enumerate(text_list):
+    print(f"Text: '{text}'")
+    print(f"Embedding shape: {embeddings[i].shape}")
+    print(f"First 5 embedding dimensions: {embeddings[i][:5]}
+")
+
+# These embeddings can then be utilized by the GRAST-SQL framework
+# for tasks like column ranking and schema filtering.
+```
+
+## Datasets
+
+The GRAST-SQL framework was evaluated on the following datasets:
+-   **Spider**: [Spider Evaluation Dataset](https://huggingface.co/datasets/griffith-bigdata/GRAST-SQL-Spider)
+-   **BIRD**: [BIRD Training/Evaluation Dataset](https://huggingface.co/datasets/griffith-bigdata/GRAST-SQL-BIRD)
+-   **Spider-2.0-lite**: [Spider 2.0-lite Eval Dataset](https://huggingface.co/datasets/griffith-bigdata/GRAST-SQL-Spider2.0-lite)
+
+## Models
+
+Other GRAST-SQL models available on the Hugging Face Hub:
+-   **GRAST-SQL 0.6B**: [GRAST-SQL 0.6B BIRD](https://huggingface.co/griffith-bigdata/GRAST-SQL-0.6B-BIRD-Reranker)
+-   **GRAST-SQL 4B**: [GRAST-SQL 4B BIRD](https://huggingface.co/griffith-bigdata/GRAST-SQL-4B-BIRD-Reranker)
+-   **GRAST-SQL 8B**: [GRAST-SQL 8B BIRD](https://huggingface.co/griffith-bigdata/GRAST-SQL-8B-BIRD-Reranker)
+
+More models can be found in the [Huggingface collection](https://huggingface.co/collections/griffith-bigdata/grast-sql).
+
+## System Flow
+
+![GRAST-SQL main flow](https://raw.githubusercontent.com/thanhdath/grast-sql/main/figures/main-flow.png)
+
+## Citation
+
+If you find this work useful for your research, please cite the paper:
+
+```bibtex
+@misc{hoang2025scalingtext2sqlllmefficientschema,
+      title={Scaling Text2SQL via LLM-efficient Schema Filtering with Functional Dependency Graph Rerankers}, 
+      author={Thanh Dat Hoang and Thanh Tam Nguyen and Thanh Trung Huynh and Hongzhi Yin and Quoc Viet Hung Nguyen},
+      year={2025},
+      eprint={2512.16083},
+      archivePrefix={arXiv},
+      primaryClass={cs.DB},
+      url={https://arxiv.org/abs/2512.16083}, 
+}
+```