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Model: Shizu0n/phi3-mini-sql-generator-merged
Source: Original Platform

README.md

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+---
+base_model: microsoft/Phi-3-mini-4k-instruct
+library_name: transformers
+license: mit
+language:
+- en
+datasets:
+- b-mc2/sql-create-context
+tags:
+- sql
+- text-to-sql
+- code-generation
+- phi-3
+- fine-tuned
+- text-generation
+- phi3
+pipeline_tag: text-generation
+---
+
+# Phi-3 Mini SQL Generator — Merged Model
+
+Merged standalone version of [Shizu0n/phi3-mini-sql-generator](https://huggingface.co/Shizu0n/phi3-mini-sql-generator)
+— LoRA adapter weights fused into [Phi-3-mini-4k-instruct](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct).
+No PEFT dependency required for inference.
+
+## Evaluation — Base vs Fine-tuned
+
+Evaluated on 200 held-out examples from [b-mc2/sql-create-context](https://huggingface.co/datasets/b-mc2/sql-create-context).
+
+| Model | Exact Match |
+|---|---|
+| Phi-3-mini-4k-instruct (base) | 2.0% |
+| **This model (fine-tuned)** | **73.5%** |
+
+> Exact match: normalized SQL comparison (lowercase, strip whitespace/semicolons).
+
+## Why two versions?
+
+| Repo | Purpose |
+|---|---|
+| [`Shizu0n/phi3-mini-sql-generator`](https://huggingface.co/Shizu0n/phi3-mini-sql-generator) | QLoRA adapter — documents the training pipeline |
+| `Shizu0n/phi3-mini-sql-generator-merged` | Merged standalone — used for deployment and inference |
+
+## Training Details
+
+- **Dataset:** b-mc2/sql-create-context — 1,000 train / 200 validation examples
+- **Method:** QLoRA (4-bit NF4, LoRA rank 16, alpha 32, target modules: qkv_proj/o_proj/gate_up_proj/down_proj)
+- **Hardware:** NVIDIA T4 (Google Colab free tier)
+- **Training time:** ~21 min
+- **Final train loss:** 0.6526
+- **Best checkpoint:** step 250 (by eval loss)
+
+## Inference Example
+
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_id = "Shizu0n/phi3-mini-sql-generator-merged"
+
+tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype=torch.float16,
+    device_map="auto",
+    trust_remote_code=False,
+    attn_implementation="eager",
+)
+model.eval()
+
+prompt = (
+    "Given the following SQL table, write a SQL query.\n\n"
+    "Table: employees (id, name, department, salary)\n\n"
+    "Question: What is the average salary per department?\n\nSQL:"
+)
+
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+with torch.inference_mode():
+    outputs = model.generate(
+        **inputs,
+        max_new_tokens=80,
+        do_sample=False,
+        use_cache=False,
+        repetition_penalty=1.1,
+        pad_token_id=tokenizer.eos_token_id,
+    )
+prompt_len = inputs["input_ids"].shape[-1]
+print(tokenizer.decode(outputs[0][prompt_len:], skip_special_tokens=True))
+```
+
+Expected output:
+```sql
+SELECT AVG(salary), department FROM employees GROUP BY department
+```
+
+## Validation
+
+Merge accepted after three smoke tests:
+1. PEFT adapter loaded on base model
+2. Local merged directory after `merge_and_unload()` + `save_pretrained()`
+3. Downloaded from this repo with `force_download=True`
+
+## Limitations
+
+- Fine-tuned on 1,000 examples — best suited for simple to medium complexity SELECT queries
+- Not tested on dialect-specific SQL (PostgreSQL/MySQL-specific functions)
+- May struggle with multi-table JOINs and nested subqueries