haricharanhl22
/

ecommerce-distributed-sql

@@ -1,199 +1,123 @@
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 ---
+language:
+- en
+license: llama3.2
+base_model: unsloth/Llama-3.2-3B
+tags:
+- text-generation
+- sql
+- distributed-databases
+- qlora
+- peft
+- fine-tuned
+- e-commerce
+pipeline_tag: text-generation
 ---
+# Llama 3.2 3B — E-commerce Distributed SQL
+Fine-tuned version of Llama 3.2 3B that converts natural language questions
+into SQL queries for distributed e-commerce databases.
+## Example
+**Input:**
+```
+### Instruction:
+Convert to distributed SQL
+### Input:
+Find all customers who spent more than 1000 euros in Germany
+### Response:
+```
+**Output:**
+```sql
+SELECT * FROM customers
+WHERE country = 'Germany' AND amount > 1000;
+```
+## Model Details
+| Property | Value |
+|----------|-------|
+| Base model | Llama 3.2 3B |
+| Fine-tuning method | QLoRA (4-bit quantization + LoRA) |
+| LoRA rank | 16 |
+| Trainable parameters | 0.14% |
+| Training GPU | Google Colab T4 (free tier) |
+| Training time | ~20 minutes |
+| Dataset size | 25 examples |
+| Training epochs | 3 |
 ## Training Details
+Fine-tuned using QLoRA — 4-bit NF4 quantization with LoRA adapters on the
+attention layers (`q_proj`, `v_proj`). This reduced memory requirements enough
+to train on a free Colab T4 GPU (15GB VRAM) in under 20 minutes, while only
+updating 0.14% of parameters.
+**Libraries used:** HuggingFace Transformers, PEFT, TRL (SFTTrainer),
+bitsandbytes, datasets
+## Dataset
+25 natural language → SQL pairs covering distributed e-commerce scenarios:
+- Orders across regions and shards
+- Inventory across warehouses
+- Customer analytics and segmentation
+- Revenue aggregations
+- JOIN queries across fragmented tables
+**Prompt format used during training:**
+```
+### Instruction:
+Convert to distributed SQL
+### Input:
+{natural language question}
+### Response:
+{SQL query}
+```
+## How to Use
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
+from peft import PeftModel
+# Load base model + adapter
+base = AutoModelForCausalLM.from_pretrained("unsloth/Llama-3.2-3B")
+model = PeftModel.from_pretrained(base, "haricharanhl22/ecommerce-distributed-sql")
+tokenizer = AutoTokenizer.from_pretrained("haricharanhl22/ecommerce-distributed-sql")
+pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)
+query = """### Instruction:
+Convert to distributed SQL
+### Input:
+Find top 5 customers by total order value
+### Response:"""
+result = pipe(query, max_new_tokens=100, do_sample=False)
+print(result[0]["generated_text"])
+```
+## Limitations
+- Trained on a small dataset (25 examples) — works best for common query patterns
+- Optimized for e-commerce schemas (orders, customers, products, inventory)
+- May not generalize well to very complex multi-level nested subqueries
+- SQL dialect closest to standard SQL / SQLite
+## Author
+**Hari Charan Hosakote Lokesh**
+M.Sc. Digital Engineering — Otto-von-Guericke-Universität Magdeburg
+- GitHub: [haricharanhl22](https://github.com/haricharanhl22)
+- LinkedIn: [haricharanhl22](https://linkedin.com/in/haricharanhl22)
+- Live project: [ai-bewerbung-assistant.vercel.app](https://ai-bewerbung-assistant.vercel.app)