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Model: bsq1989/qwen_4b_sql
Source: Original Platform

README.md

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+---
+base_model: Qwen/Qwen3-4B-Base
+library_name: transformers
+pipeline_tag: text-generation
+tags:
+  - text-to-sql
+  - sql
+  - qwen3
+  - llamafactory
+  - spider
+  - spider-test-suite
+---
+
+# qwen_4b_sql
+
+`qwen_4b_sql` is a `Qwen3-4B-Base` model finetuned for text-to-SQL generation with full SFT on a cleaned split of `PipableAI/pip-txt-to-sql-spider-bird-dataset`.
+
+This repository tracks the stronger 4B checkpoint from our H20 single-GPU training runs. In our internal comparison, this checkpoint outperformed the corresponding `Qwen3-1.7B-Base` baseline on Spider execution accuracy.
+
+## Base Model
+
+- Base model: [`Qwen/Qwen3-4B-Base`](https://huggingface.co/Qwen/Qwen3-4B-Base)
+- Finetuning framework: `LLaMA-Factory`
+- Training mode: `Full SFT`
+- Task: `schema + question -> SQL only`
+
+## Training Data
+
+- Primary dataset: [`PipableAI/pip-txt-to-sql-spider-bird-dataset`](https://huggingface.co/datasets/PipableAI/pip-txt-to-sql-spider-bird-dataset)
+- We used a cleaned local split derived from that dataset for train/validation
+
+## Training Setup
+
+- Hardware: single `NVIDIA H20 96GB`
+- Precision: `bf16`
+- Context length: `2048`
+- Per-device train batch size: `1`
+- Gradient accumulation steps: `8`
+- Effective batch size: `8`
+- Learning rate: `5e-6`
+- Scheduler: `cosine`
+- Warmup steps: `300`
+- Epochs: `4.0`
+- Template: `qwen3_nothink`
+- Best-checkpoint selection: `load_best_model_at_end = true`
+
+## Spider Benchmark
+
+The following numbers are from Spider dev using the official evaluation tooling:
+
+- Official `match` evaluation from `test-suite-sql-eval`
+- Official Spider `Test Suite` execution evaluation
+
+### Main Results
+
+| Metric | Score |
+| --- | ---: |
+| Spider official exact match | 35.0% |
+| Spider Test Suite execution accuracy | 67.6% |
+
+### Difficulty Breakdown
+
+| Difficulty | Exact Match | Test Suite Exec |
+| --- | ---: | ---: |
+| Easy | 64.9% | 87.5% |
+| Medium | 37.4% | 72.9% |
+| Hard | 16.1% | 50.0% |
+| Extra | 3.6% | 42.2% |
+
+## Notes
+
+- This model is stronger under execution-based Spider evaluation than our best `Qwen3-1.7B-Base` run.
+- In our experiments, exact-match metrics were often stricter than execution-based metrics because semantically valid SQL rewrites do not always match the Spider gold form exactly.
+- A later 4B rerun with altered training settings underperformed this checkpoint on Spider and is not the checkpoint published here.
+
+## Intended Use
+
+This model is intended for:
+
+- text-to-SQL research baselines
+- schema-conditioned SQL generation experiments
+- single-turn SQL generation from natural language plus schema text
+
+It is not validated for:
+
+- production-grade database access control
+- unrestricted execution over arbitrary enterprise schemas
+- multi-turn agent workflows without extra prompting / tooling
+
+## Example Usage
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+model_id = "bsq1989/qwen_4b_sql"
+
+tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype="auto",
+    device_map="auto",
+    trust_remote_code=True,
+)
+
+prompt = """Generate SQL from the given schema and question. Output SQL only.
+
+Schema:
+CREATE TABLE twitter (TweetID INTEGER, UserID INTEGER, LocationID INTEGER, Lang TEXT, ...);
+CREATE TABLE location (LocationID INTEGER, Country TEXT, City TEXT, ...);
+
+Question:
+How many tweets are in English?
+"""
+
+messages = [{"role": "user", "content": prompt}]
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True,
+)
+
+inputs = tokenizer(text, return_tensors="pt").to(model.device)
+outputs = model.generate(**inputs, max_new_tokens=256)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+
+## Limitations
+
+- Performance drops on more open-ended and heterogeneous SQL benchmarks than Spider.
+- The model can still produce invalid column references on out-of-distribution schemas.
+- Benchmark numbers here reflect our current internal setup and should be reproduced with the same evaluation pipeline for strict comparison.