Replace README with minimal version (GitHub link + citation)

README.md

@@ -1,134 +1,13 @@
----
-base_model:
-- griffith-bigdata/Qwen-2.5-Coder-0.5B-SQL-Writer
-license: apache-2.0
-language:
-- en
-tags:
-- text-to-sql
-- spider
-- grpo
-- finer-sql
-- code
-library_name: transformers
-pipeline_tag: text-generation
----
-
-# FINER-SQL-0.5B-Spider
-
-A small but capable 0.5 B-parameter Text-to-SQL model fine-tuned from
-[`griffith-bigdata/Qwen-2.5-Coder-0.5B-SQL-Writer`](https://huggingface.co/griffith-bigdata/Qwen-2.5-Coder-0.5B-SQL-Writer)
-with GRPO + the FINER-SQL dense rewards (Memory + Atomic).
-
-✅ **75.0% Execution Accuracy on Spider Dev** (n=30, value-aware voting). Runs on a 4-8 GB GPU.
-
-📄 See other models: https://huggingface.co/collections/griffith-bigdata/finer-sql
-📄 GitHub: https://github.com/thanhdath/finer-sql/tree/main
-
----
-
-## FINER-SQL Model Family — Comparison Across All Sizes
-
-| Model | Params | BIRD Dev (n=30, vav) | Spider Dev (n=30, vav, +agg_hint) |
-|-------|--------|---------------------|----------------------------------|
-| [FINER-SQL-3B-BIRD](https://huggingface.co/griffith-bigdata/FINER-SQL-3B-BIRD) | 3 B | **67.54%** ✅ | 83.8% |
-| [FINER-SQL-3B-Spider](https://huggingface.co/griffith-bigdata/FINER-SQL-3B-Spider) | 3 B | 63.04% | **85.10%** ✅ |
-| [FINER-SQL-0.5B-BIRD](https://huggingface.co/griffith-bigdata/FINER-SQL-0.5B-BIRD) | 0.5 B | **50.85%** ✅ | 68.6% |
-| **FINER-SQL-0.5B-Spider** *(this model)* | 0.5 B | TBD | **75.0%** ✅ |
-
-The 0.5 B Spider model is **6.4 pp better** than the 0.5 B BIRD model on Spider Dev — confirming dataset-specific specialisation matters even at small scales.
-
----
-
-## Inference
-
-### Quick start (vLLM)
-
-```python
-from vllm import LLM, SamplingParams
-
-llm = LLM(
-    model="griffith-bigdata/FINER-SQL-0.5B-Spider",
-    dtype="bfloat16",
-    max_model_len=4096,
-    gpu_memory_utilization=0.7,
-)
-
-system_prompt = """You are a meticulous SQL expert. Generate a single, correct SQL query for the user question and the provided database schema.
-Follow this exact response format:
-
-Rules:
-- Output exactly one SQL statement.
-- The SQL must be executable on SQLite.
-- Do not include any explanatory text.
-- Output one SQL statement only. Do not include any extra text, tags, or code fences."""
-
-sampling = SamplingParams(n=30, temperature=1.0, max_tokens=2048)
-messages = [
-    {"role": "system", "content": system_prompt},
-    {"role": "user", "content": f"Database Schema:\n{schema}\n\nQuestion: {question}"},
-]
-output = llm.chat(messages, sampling)
-candidate_sqls = [c.text.split("</think>")[-1].strip() for c in output[0].outputs]
-# Apply majority voting (vav) — see GitHub repo
-```
-
-### Recommended evaluation pipeline
-
-1. Generate n=30 candidates with temperature=1.0
-2. Execute each candidate; group results
-3. Pick from the largest non-empty success group (value-aware voting, "vav")
-4. Score with the official Spider evaluator (`test_suite_sql_eval`)
-
-This pipeline gives **75.0% Spider Dev EX** (75.44% MV).
-
----
-
-## Detailed Spider Dev results (n=30, vav)
-
-| Hardness | Count | Execution Accuracy |
-|----------|-------|--------------------|
-| Easy | 248 | 91.9% |
-| Medium | 446 | 82.5% |
-| Hard | 174 | 62.6% |
-| Extra Hard | 166 | 42.8% |
-| **All** | **1034** | **75.0%** |
-
-Recall@30: **85.11%** (any-correct rate among 30 candidates).
-
----
-
-## Training
-
-| Parameter | Value |
-|-----------|-------|
-| Base model | `griffith-bigdata/Qwen-2.5-Coder-0.5B-SQL-Writer` |
-| Algorithm | GRPO |
-| Train data | Spider train (8,659 samples) |
-| Total steps | 2000 (this checkpoint = 2000) |
-| Learning rate | 8e-6 |
-| Num generations per prompt | 32 |
-| Gradient accumulation | 32 |
-| Max completion length | 2048 |
-| Max prompt length | 1500 |
-| Temperature (rollout) | 1.0 |
-| Selection during eval | vav (value-aware voting) |
-| Rewards | Execution + Atomic + Memory + Format |
-
----
-
-## License
-
-Inherits the base model's license (Apache 2.0).
-
----
+📄 GitHub: https://github.com/thanhdath/finer-sql
 
 ## Citation
 
 ```bibtex
-@article{finer-sql-2026,
-  title  = {FINER-SQL: Fine-grained reasoning rewards for small Text-to-SQL models},
-  author = {Thanh Dat and others},
-  year   = {2026},
+@inproceedings{finersql,
+  author       = {Thanh Dat Hoang and Thanh Trung Huynh and Matthias Weidlich and Thanh Tam Nguyen and Tong Chen and Hongzhi Yin and Quoc Viet Hung Nguyen},
+  title        = {Boosting Small Language Models for Text-to-SQL with Fine-Grained Execution Feedback and Cost-Efficient Rewards},
+  booktitle    = {ICDE},
+  publisher    = {IEEE},
+  year         = {2026},
 }
 ```