Sid9797
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querycraft-phi3-sql

@@ -1,202 +1,142 @@
 ---
 base_model: microsoft/Phi-3-mini-4k-instruct
-library_name: peft
 ---
-# Model Card for Model ID
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-## Model Details
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-### Framework versions
-- PEFT 0.12.0

 ---
+language:
+- en
+license: mit
+tags:
+- text-to-sql
+- sql
+- nlp
+- fine-tuning
+- qlora
+- lora
+- phi-3
+- peft
 base_model: microsoft/Phi-3-mini-4k-instruct
+datasets:
+- b-mc2/sql-create-context
+metrics:
+- bleu
+pipeline_tag: text-generation
 ---
+# QueryCraft — Phi-3 Mini Fine-Tuned for Text-to-SQL
+Fine-tuned **Phi-3 Mini 3.8B** using **QLoRA** on 76,000 Text-to-SQL examples.
+Converts natural language questions into valid SQL queries.
+## Evaluation Results
+| Metric | Base Model | Fine-Tuned |
+|---|---|---|
+| Exact Match | 0.0% | 82.0% |
+| Execution Accuracy | 84.0% | 96.0% |
+| BLEU Score | 55.79 | 96.42 |
+Evaluated on 50 held-out validation examples not seen during training.
+## Model Details
+| Property | Value |
+|---|---|
+| Base model | microsoft/Phi-3-mini-4k-instruct (3.8B params) |
+| Fine-tuning method | QLoRA (4-bit NF4 + LoRA) |
+| LoRA rank | r=16, alpha=32 |
+| Trainable parameters | 8,912,896 (0.23%) |
+| Training examples | 76,577 |
+| Training hardware | NVIDIA RTX 5060 Ti 8GB |
+| Training time | 3 hours 2 minutes |
+| Final train loss | 0.5677 |
+| Max sequence length | 256 tokens |
+## How to Use
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
+from peft import PeftModel
+base_model = "microsoft/Phi-3-mini-4k-instruct"
+adapter    = "Sid9797/querycraft-phi3-sql"
+bnb_config = BitsAndBytesConfig(
+    load_in_4bit=True,
+    bnb_4bit_quant_type="nf4",
+    bnb_4bit_compute_dtype=torch.bfloat16,
+    bnb_4bit_use_double_quant=True,
+)
+tokenizer = AutoTokenizer.from_pretrained(base_model, trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained(
+    base_model,
+    quantization_config=bnb_config,
+    device_map="cuda:0",
+    trust_remote_code=True,
+    torch_dtype=torch.bfloat16,
+)
+model = PeftModel.from_pretrained(model, adapter)
+model.eval()
+prompt = '''### System:
+You are a SQL expert. Given a database schema and a natural language question, generate a valid SQL query that answers the question. Output only the SQL query with no explanation.
+### Schema:
+CREATE TABLE employees (id INTEGER, name VARCHAR, department VARCHAR, salary FLOAT)
+### Question:
+What is the average salary by department?
+### SQL:
+'''
+inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
+with torch.no_grad():
+    outputs = model.generate(**inputs, max_new_tokens=128, do_sample=False)
+sql = tokenizer.decode(outputs[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True)
+print(sql.strip().split("\n")[0])
+# SELECT AVG(salary) FROM employees GROUP BY department
+```
+## Prompt Format
+The model was trained on the Alpaca instruction format:
+System:
+You are a SQL expert. Given a database schema and a natural language question,
+generate a valid SQL query that answers the question.
+Output only the SQL query with no explanation.
+Schema:
+{CREATE TABLE statements}
+Question:
+{natural language question}
+SQL:
+{model generates SQL here}
 ## Training Details
+- **Dataset:** [b-mc2/sql-create-context](https://huggingface.co/datasets/b-mc2/sql-create-context)
+  — 78,577 examples with inline CREATE TABLE schemas
+- **Train/Val split:** 76,577 train / 2,000 validation (seeded shuffle)
+- **Quantization:** 4-bit NF4 with double quantization (bitsandbytes)
+- **LoRA target modules:** q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
+- **Optimizer:** AdamW with cosine LR schedule, warmup_ratio=0.05
+- **Effective batch size:** 16 (batch_size=4, gradient_accumulation=4)
+- **Packing:** Enabled — short examples concatenated to fill 256-token sequences
+## Why the Base Model Scored 0% Exact Match
+The base Phi-3 Mini, without fine-tuning, consistently wrapped SQL output
+in markdown code fences (`` ```sql ... ``` ``) and appended semicolons.
+This formatting breaks exact match evaluation even when the SQL logic is correct.
+Fine-tuning on consistently formatted examples eliminated this entirely.
+## Limitations
+- Optimised for single-table and simple multi-table queries
+- Schema must be provided as CREATE TABLE SQL statements
+- Best results on English-language questions
+- May struggle with highly complex nested subqueries
+## Links
+- **GitHub:** https://github.com/Siddhesh-Ai9797/querycraft
+- **Base Model:** https://huggingface.co/microsoft/Phi-3-mini-4k-instruct
+- **Training Dataset:** https://huggingface.co/datasets/b-mc2/sql-create-context