antony-bryan-3D2Y
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phi2-sql-lora-lr5e4

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 ---
+base_model: microsoft/phi-2
+tags:
+  - sql
+  - text-to-sql
+  - lora
+  - qlora
+  - pytorch
+license: mit
+language:
+  - en
 ---
+# Phi-2 SQL LoRA (lr=5e-4)
+Fine-tuned [microsoft/phi-2](https://huggingface.co/microsoft/phi-2) on
+[b-mc2/sql-create-context](https://huggingface.co/datasets/b-mc2/sql-create-context)
+using QLoRA — achieving **70% exact match** on SQL generation, up from a 2% baseline.
+This is **Run 2** (lr=5e-4).
+See also: [phi2-sql-lora-lr2e4](https://huggingface.co/antony-bryan-3D2Y/phi2-sql-lora-lr2e4) (lr=2e-4, 76% EM — best run)
+## Results
+| Model | Exact Match | ROUGE-L | Δ vs Base |
+|---|---|---|---|
+| Phi-2 Base | 2.0% | 0.886 | — |
+| **This model (lr=5e-4)** | **70.0%** | **0.9825** | **+68pp** |
+Evaluated on 50 held-out samples from sql-create-context (seed=42).
+The lower learning rate (lr=2e-4) outperformed this run by 6 percentage points,
+consistent with the general finding that conservative learning rates are more
+stable for LoRA fine-tuning.
 ## Training Details
+| Parameter | Value |
+|---|---|
+| Method | QLoRA (4-bit NF4 + LoRA) |
+| LoRA rank | 16 |
+| LoRA alpha | 32 |
+| Target modules | q_proj, v_proj |
+| Dataset | 20,000 samples from sql-create-context |
+| Epochs | 2 |
+| Learning rate | 5e-4 |
+| Effective batch size | 16 |
+| Hardware | Kaggle T4 x2 |
+| Training time | ~7 hours |
+## Usage
+```python
+from peft import PeftModel
+from transformers import AutoModelForCausalLM, AutoTokenizer, AutoConfig
+import torch
+model_name = "microsoft/phi-2"
+tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
+tokenizer.pad_token = tokenizer.eos_token
+config = AutoConfig.from_pretrained(model_name, trust_remote_code=True)
+config.__dict__['pad_token_id'] = tokenizer.pad_token_id
+base = AutoModelForCausalLM.from_pretrained(
+    model_name, config=config,
+    dtype=torch.float16, device_map="auto", trust_remote_code=True
+)
+model = PeftModel.from_pretrained(base, "antony-bryan-3D2Y/phi2-sql-lora-lr5e4")
+model.eval()
+prompt = """### SQL Schema:
+CREATE TABLE employees (id INT, name VARCHAR, department VARCHAR, salary INT)
+### Question:
+What are the names of employees in the engineering department?
+### SQL Query:
+"""
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+with torch.no_grad():
+    output = model.generate(**inputs, max_new_tokens=100, do_sample=False,
+                            eos_token_id=tokenizer.eos_token_id)
+n = inputs['input_ids'].shape[1]
+result = tokenizer.decode(output[0][n:], skip_special_tokens=True)
+result = result.replace("</s>", "").replace("<|endoftext|>", "").split('\n')[0].strip()
+print(result)
+# → SELECT name FROM employees WHERE department = "engineering"
+```
+## Links
+- 📓 Training notebook: [llm-finetune-eval](https://github.com/antony-bryan/llm-finetune-eval)
+- 📊 W&B training runs: [phi2-sql-finetune](https://wandb.ai/antonybryan2-00-anthropic/phi2-sql-finetune)
+- 🔗 Run 1 (lr=2e-4): [phi2-sql-lora-lr2e4](https://huggingface.co/antony-bryan-3D2Y/phi2-sql-lora-lr2e4)