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README.md

@@ -1,21 +1,119 @@
----
-base_model: unsloth/qwen2.5-coder-7b-instruct-bnb-4bit
-tags:
-- text-generation-inference
-- transformers
-- unsloth
-- qwen2
-license: apache-2.0
-language:
-- en
----
-
-# Uploaded finetuned  model
-
-- **Developed by:** junmingg
-- **License:** apache-2.0
-- **Finetuned from model :** unsloth/qwen2.5-coder-7b-instruct-bnb-4bit
-
-This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
-
-[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
+---
+license: apache-2.0
+base_model: Qwen/Qwen2.5-Coder-7B-Instruct
+datasets:
+  - b-mc2/sql-create-context
+language:
+  - en
+library_name: transformers
+pipeline_tag: text-generation
+tags:
+  - text-to-sql
+  - text2sql
+  - sql
+  - qlora
+  - peft
+  - unsloth
+  - qwen2.5
+---
+
+# Qwen2.5-Coder-7B Text-to-SQL (QLoRA)
+
+A QLoRA fine-tune of [`Qwen/Qwen2.5-Coder-7B-Instruct`](https://huggingface.co/Qwen/Qwen2.5-Coder-7B-Instruct)
+that turns a **SQL schema + a natural-language question** into **a single SQL query**. Trained on a single
+RTX 3090 with [Unsloth](https://github.com/unslothai/unsloth) + TRL, completion-only loss.
+
+> Trained and evaluated by `junmingg`. Code + reproducible eval harness:
+> https://github.com/junmingg/Unsloth-Qwen2.5-Coder-7b-Text-to-SQL-SFT
+
+## Results (held-out 500 examples)
+
+| Model | Exact match | Semantic equiv. | SQL validity |
+|---|---|---|---|
+| Base `Qwen2.5-Coder-7B-Instruct` (zero-shot) | 3.8% | 67.0% | 100.0% |
+| **+ QLoRA (this model)** | **78.8%** | **86.2%** | **99.2%** |
+
+![benchmark](benchmark.png)
+
+- **Exact match** = canonicalized string match (via `sqlglot`); a strict *lower bound* — different-but-correct
+  SQL fails it.
+- **Semantic equivalence** = an independent LLM judge (GLM-5-Turbo) decides whether the predicted SQL is
+  equivalent to the gold for the question. This is the headline correctness metric.
+- **SQL validity** = fraction of predictions that parse under `sqlglot`.
+
+Fine-tuning lifted exact-match by **+75.0 points** and semantic-equivalence by **+19.2 points** on prompts the
+model never saw during training (0/500 test prompts appear in the training data). The large exact-match jump
+is the model learning the dataset's SQL conventions (quoting, value formatting); the semantic jump is genuine
+correctness beyond formatting.
+
+### Label-noise ablation
+
+A variant trained with malformed gold answers filtered out of the training set
+(`junmingg/qwen2.5-coder-7b-text2sql-filtered`) reaches the same accuracy with slightly higher validity
+(100% on valid-reference test rows). Cleaning 0.32% of train labels moved validity, not accuracy — details in
+the [repo README](https://github.com/junmingg/Unsloth-Qwen2.5-Coder-7b-Text-to-SQL-SFT).
+
+## Usage
+
+The model was trained with a specific system prompt and ChatML format — **use the same formatting** for best
+results.
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+REPO = "junmingg/qwen2.5-coder-7b-text2sql"
+model = AutoModelForCausalLM.from_pretrained(REPO, torch_dtype=torch.bfloat16, device_map="auto")
+tok = AutoTokenizer.from_pretrained(REPO)
+
+SYSTEM = ("You are a precise text-to-SQL engine. Given a SQL schema and a natural-language "
+          "question, respond with a single valid SQL query and nothing else.")
+
+schema = "CREATE TABLE head (age INTEGER, name TEXT)"
+question = "How many heads of the departments are older than 56?"
+
+messages = [
+    {"role": "system", "content": SYSTEM},
+    {"role": "user", "content": f"Schema:\n{schema}\n\nQuestion: {question}"},
+]
+inputs = tok.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
+out = model.generate(inputs, max_new_tokens=256, do_sample=False)
+print(tok.decode(out[0][inputs.shape[1]:], skip_special_tokens=True))
+# -> SELECT COUNT(*) FROM head WHERE age > 56
+```
+
+LoRA adapters only (smaller): `junmingg/qwen2.5-coder-7b-text2sql-lora`.
+GGUF quants (`q4_k_m`, `q8_0`) for llama.cpp / Ollama: `junmingg/qwen2.5-coder-7b-text2sql-GGUF`.
+
+## Training
+
+| | |
+|---|---|
+| Base | `Qwen/Qwen2.5-Coder-7B-Instruct` (4-bit QLoRA) |
+| Data | [`b-mc2/sql-create-context`](https://huggingface.co/datasets/b-mc2/sql-create-context), 25k train / 500 held-out test (seed 42) |
+| LoRA | r=16, α=16, dropout=0, all linear layers |
+| Schedule | 1 epoch (~1,557 steps), effective batch 16, lr 2e-4 cosine, warmup 0.03 |
+| Precision / optim | bf16, `adamw_8bit`, `max_seq_length=2048` |
+| Loss | completion-only (prompt masked; loss on the SQL answer only) |
+| Hardware / time | 1× RTX 3090, ~59 min |
+
+## Evaluation methodology
+
+Both base and fine-tuned models are evaluated with **identical** prompting and greedy decoding on the **same**
+held-out 500-example test set (disjoint from train; verified 0 prompt overlap). Semantic equivalence is scored
+by an independent LLM judge (GLM-5-Turbo). Reporting both exact-match and semantic-equivalence is deliberate:
+exact-match is a lower bound, semantic-equivalence is the fairer correctness signal. The full harness is in the
+linked repo (`python -m src.eval`, `python -m src.judge`).
+
+## Limitations
+
+- Trained on synthetic, largely single-table schemas (`sql-create-context`, derived from WikiSQL + Spider);
+  not evaluated for SQL-injection safety or complex multi-join queries.
+- The dataset contains a small amount of label noise (~0.3–0.6% of gold answers are themselves unparseable —
+  WikiSQL artifacts); the model's rare "invalid" outputs are predominantly faithful reproductions of those
+  malformed references rather than novel errors.
+- Outputs should be validated/parameterized before execution against a real database.
+
+## License & attribution
+
+Apache-2.0 (inherited from the base model). Training data: `b-mc2/sql-create-context` (CC-BY-4.0).