Rubansendhur78409
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sql-llm-wikisql-lora

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 ---
 library_name: transformers
-tags: []
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 ---
 library_name: transformers
+base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
+tags:
+- text-to-sql
+- sql-generation
+- lora
+- qlora
+- wikisql
+- tinyllama
+- transformers
+license: apache-2.0
 ---
+SQL Assistant – TinyLlama Fine-Tuned on WikiSQL (QLoRA)
+Model Overview
+This model is a schema-aware Text-to-SQL generator built by fine-tuning TinyLlama-1.1B using QLoRA on the WikiSQL dataset.
+It converts natural language questions into structured SQL queries given a database schema.
+The model has been adapted specifically for:
+SQL generation
+Schema-conditioned reasoning
+Structured query formatting
+Reduced hallucination compared to base model
+Model Details
+Developed by: Ruben S
+Model type: Causal Language Model (LoRA fine-tuned adapter)
+Base model: TinyLlama-1.1B-Chat-v1.0
+Task: Text-to-SQL generation
+Language: English
+Training dataset: WikiSQL (10,000 samples subset)
+Fine-tuning method: QLoRA (4-bit quantization + LoRA adapters)
+Epochs: 3
+Final training loss: 0.52
+Hardware: Google Colab T4 GPU
+License: Apache 2.0 (inherits from base model)
+This repository contains only the LoRA adapter weights.
+The base model must be loaded separately.
+Intended Use
+Direct Use
+This model is intended for:
+Converting natural language queries into SQL
+Educational and research use
+SQL assistant systems
+Demonstrations of parameter-efficient fine-tuning
+Example use cases:
+"Find employees with salary greater than 50000"
+"What is the average price of products in Electronics category?"
+Downstream Use
+The model can be integrated into:
+Database query assistants
+Data analytics dashboards
+Backend services that translate user questions into SQL
+Chat-based data exploration tools
+Out-of-Scope Use
+This model is not suitable for:
+Production-grade database security systems
+Financial or safety-critical systems
+Complex multi-table join reasoning (not trained on Spider)
+SQL injection protection
+It was trained on single-table WikiSQL-style queries.
+Training Details
+Training Data
+Dataset: WikiSQL (publicly available dataset)
+10,000 training samples
+1,000 validation samples
+Single-table SQL queries
+Aggregations: MAX, MIN, COUNT, SUM, AVG
+WHERE clause conditions
+SQL queries were reconstructed from parsed format into full SQL strings before training.
+Training Procedure
+Base Model: TinyLlama-1.1B-Chat-v1.0
+Quantization: 4-bit (bitsandbytes)
+Fine-tuning: LoRA (Parameter Efficient Fine-Tuning)
+Trainable parameters: ~1% of total model parameters
+Objective: Causal Language Modeling (next-token prediction)
+Labels set equal to input_ids
+Training Hyperparameters
+Epochs: 3
+Batch size: 4
+Gradient accumulation steps: 4
+Learning rate: 1e-4
+Precision: FP16
+Optimizer: AdamW (default Trainer optimizer)
+Evaluation
+Qualitative Evaluation
+The fine-tuned model was compared against the base TinyLlama model.
+Improvements observed:
+Removal of chat-style explanations
+No markdown formatting
+Reduced hallucinated table names
+Improved aggregation selection (AVG, COUNT, etc.)
+Better multi-condition WHERE clauses
+Example:
+Input:
+Find the average price of products in Electronics category.
+Output:
+SELECT AVG(price) FROM table WHERE category = 'Electronics'
+Limitations
+Trained only on WikiSQL (single-table queries)
+Limited support for JOIN operations
+Numeric formatting inconsistencies may occur (e.g., quoting numbers)
+Sensitive to schema formatting structure
+How to Use
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from peft import PeftModel
+import torch
+base_model = AutoModelForCausalLM.from_pretrained(
+    "TinyLlama/TinyLlama-1.1B-Chat-v1.0",
+    torch_dtype=torch.float16,
+    device_map="auto"
+)
+model = PeftModel.from_pretrained(
+    base_model,
+    "YOUR_USERNAME/sql-assistant-tinyllama-wikisql-qlora"
+)
+tokenizer = AutoTokenizer.from_pretrained(
+    "TinyLlama/TinyLlama-1.1B-Chat-v1.0"
+)
+prompt = """### Instruction:
+Convert natural language to SQL using the given schema.
+### Schema:
+Table columns: product, price, category, rating
+### Question:
+Find the average price of products in Electronics category.
+### SQL:
+"""
+inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
+output = model.generate(
+    **inputs,
+    max_new_tokens=80,
+    temperature=0.1,
+    do_sample=True
+)
+print(tokenizer.decode(output[0], skip_special_tokens=True))
+Environmental Impact
+Hardware: NVIDIA T4 GPU
+Training Time: ~2 hours total
+Cloud Provider: Google Colab
+Precision: FP16 + 4-bit quantization
+Parameter-efficient fine-tuning significantly reduces compute and memory usage compared to full fine-tuning.
+Future Work
+Extend training to Spider dataset (multi-table joins)
+Add execution-based evaluation
+Improve numeric formatting consistency
+Add schema-aware table naming
+Citation
+If you use this model, please cite:
+TinyLlama-1.1B-Chat-v1.0
+WikiSQL Dataset
+Contact: rubansendhur78409@cit.edu.in