lakshitha722
/

querymind-nl2sql

@@ -1,200 +1,101 @@
 ---
-library_name: transformers
 tags:
 - unsloth
 ---
-# Model Card for Model ID
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-## Model Details
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-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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 ---
+license: apache-2.0
+base_model: unsloth/Llama-3.2-3B-Instruct
 tags:
+- text-to-sql
+- nl2sql
 - unsloth
+- llama
+- lora
+- qlora
+datasets:
+- spider
+metrics:
+- exact_match
+- similarity
+model-index:
+- name: querymind-nl2sql
+  results: []
 ---
+# 🧠 QueryMind: Natural Language to SQL Engine
+QueryMind is a domain-specific, highly-optimized **NL-to-SQL engine** powered by a fine-tuned **LLaMA 3.2 3B Instruct** model. It has been fine-tuned using **QLoRA (4-bit)** via **Unsloth** on the **Spider NL2SQL dataset** to translate plain English queries into accurate, schema-valid SQL statements based on a provided database schema.
+---
+## 🎯 Model Details
+- **Developed by:** Lakshitha Nuwan
+- **Model type:** Causal Language Model (Fine-tuned LLM)
+- **Language(s) (NLP):** English
+- **License:** Apache 2.0
+- **Finetuned from model:** [unsloth/Llama-3.2-3B-Instruct](https://huggingface.co/unsloth/Llama-3.2-3B-Instruct)
+- **Training Framework:** Unsloth & PyTorch
+---
+## 🔗 Model Sources
+- **HuggingFace Repository:** [lakshitha722/querymind-nl2sql](https://huggingface.co/lakshitha722/querymind-nl2sql)
+- **Interactive Live Demo:** [HuggingFace Space Demo](https://huggingface.co/spaces/lakshitha722/querymind-nl2sql-demo)
+- **Experiment Tracking:** [Weights & Biases (W&B) Dashboard](https://wandb.ai/lakshithanuwan722-other/querymind-nl2sql)
+---
+## 💻 How to Get Started with the Model
+Use the code below to load the model and generate SQL queries using **Unsloth** (recommended for local GPUs) or standard HuggingFace **Transformers**.
+### Inference with Unsloth (Recommended)
+```python
+from unsloth import FastLanguageModel
+import torch
+MODEL_NAME = "lakshitha722/querymind-nl2sql"
+model, tokenizer = FastLanguageModel.from_pretrained(
+    model_name     = MODEL_NAME,
+    max_seq_length = 1024,
+    load_in_4bit   = True,
+    dtype          = None,
+)
+FastLanguageModel.for_inference(model)
+# 1. Define Prompt Template
+PROMPT_TEMPLATE = """Below is an instruction that describes a task. Write a response that appropriately completes the request.
+### Instruction:
+Convert the following natural language question to a SQL query based on the given database schema. Return ONLY the SQL query, nothing else.
+### Schema:
+{schema}
+### Question:
+{question}
+### Response:
+"""
+# 2. Prepare Inputs
+schema = "Database: company\nTables: employees (id, name, department, salary, hire_date)"
+question = "What is the average salary by department?"
+prompt = PROMPT_TEMPLATE.format(schema=schema, question=question)
+inputs = tokenizer([prompt], return_tensors="pt").to("cuda")
+# 3. Generate
+with torch.no_grad():
+    outputs = model.generate(
+        **inputs,
+        max_new_tokens = 150,
+        temperature    = 0.1,
+        do_sample      = False,
+        pad_token_id   = tokenizer.eos_token_id,
+    )
+# 4. Decode Output
+input_length = inputs['input_ids'].shape[1]
+sql = tokenizer.decode(outputs[0][input_length:], skip_special_tokens=True).strip()
+print("Generated SQL:", sql)