Neeharika20
/

qwen3-4b-nl2sql

@@ -1,208 +1,149 @@
 ---
-base_model: unsloth/qwen3-4b-instruct-2507-unsloth-bnb-4bit
-library_name: peft
-pipeline_tag: text-generation
 tags:
-- base_model:adapter:unsloth/qwen3-4b-instruct-2507-unsloth-bnb-4bit
-- lora
-- transformers
 - unsloth
 ---
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
-## Model Details
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-### Framework versions
-- PEFT 0.18.0

 ---
+datasets:
+- xlangai/spider
+base_model:
+- Qwen/Qwen3-4B-Instruct-2507
 tags:
 - unsloth
+- text-to-sql
+- fine-tuning
+- trl
 ---
+# Qwen3-4B NL → SQL (Spider Fine-Tuned)
+## Model Overview
+- **Model name:** qwen3-4b-nl2sql
+- **Base model:** Qwen/Qwen3-4B-Instruct-2507
+- **Model type:** Decoder-only Transformer (Causal Language Model)
+- **Task:** Natural Language → SQL Query Generation
+- **Language:** English
+- **License:** Apache-2.0
+- **Author:** Neeharika
+> ✅ This repository contains **merged model weights** and can be used directly with
+> `AutoModelForCausalLM.from_pretrained`.
+This model is a fine-tuned version of Qwen3-4B, optimized to translate natural language questions into executable SQL queries using database schema context. It is designed for structured data querying and analytics use cases rather than open-ended conversation.
+---
+## Intended Use
+### Primary Use Cases
+- Natural language interfaces for SQL databases
+- Backend services that auto-generate SQL
+- Data analytics assistants
+- Research in semantic parsing and text-to-SQL
+### Out-of-Scope Uses
+- General-purpose chatbots
+- Code generation beyond SQL
+- Autonomous decision-making in high-risk domains (medical, legal, financial)
+---
+## Training Data
+### Dataset
+- **Name:** Spider Dataset
+- **Type:** Public benchmark for cross-domain text-to-SQL tasks
+- **Domains:** Multiple real-world databases with diverse schemas
+- **Size:** ~7k–10k question–SQL pairs (after preprocessing)
+### Data Characteristics
+- Multi-table joins
+- Nested and correlated subqueries
+- Aggregations (COUNT, AVG, SUM, GROUP BY, HAVING)
+- Schema-dependent reasoning
+### Preprocessing
+- Converted samples into instruction-style format
+- Injected full database schema into prompts
+- Cleaned malformed or ambiguous samples
+- Normalized SQL formatting
+---
+## Prompt Format
+The model was trained using schema-aware instruction prompts:
+```
+### Instruction:
+Convert the question into an SQL query.
+### Database Schema:
+<Table and column definitions>
+### Question:
+<User query>
+### SQL:
+```
+## Training Procedure
+### Frameworks & Libraries
+- Hugging Face Transformers
+- Hugging Face TRL (Supervised Fine-Tuning)
+- Unsloth (memory-efficient training)
+- PyTorch
+### Fine-Tuning Details
+- **Method:** Supervised Fine-Tuning (SFT)
+- **Quantization during training:** 4-bit (bnb)
+- **Precision:** Mixed precision
+- **Optimization:** LoRA adapters (merged post-training)
+### Key Hyperparameters
+- Max sequence length: 2048
+- Learning rate: 2e-4
+- Optimizer: AdamW
+- Padding: Right-padding
+- Gradient checkpointing: Enabled
+---
+## Example Usage
+```python
+from unsloth import FastLanguageModel
+import torch
+model, tokenizer = FastLanguageModel.from_pretrained(
+    model_name="Neeharika20/qwen3-4b-nl2sql",
+    max_seq_length=2048,
+    load_in_4bit=True,
+    device_map="auto",
+)
+model.eval()
+prompt = """### Instruction:
+Convert the question into an SQL query.
+### Database Schema:
+CREATE TABLE students(
+    id INT,
+    name TEXT,
+    age INT
+);
+### Question:
+List the names of all students.
+### SQL:
+"""
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+with torch.no_grad():
+    outputs = model.generate(
+        **inputs,
+        max_new_tokens=64,
+        temperature=0.2,
+        do_sample=False,
+    )
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```