| | --- |
| | language: |
| | - en |
| | license: apache-2.0 |
| | library_name: peft |
| | tags: |
| | - text-generation |
| | - sql |
| | - chat |
| | - peft |
| | - lora |
| | - transformers |
| | - phi-3 |
| | - instruction-tuning |
| | base_model: unsloth/Phi-3-mini-4k-instruct-bnb-4bit |
| | pipeline_tag: text-generation |
| | inference: false |
| | --- |
| | |
| | # 🧠 SQL Chat – Phi-3-mini SQL Assistant |
| |
|
| | **Model ID:** `saadkhi/SQL_Chat_finetuned_model` |
| | **Base model:** `unsloth/Phi-3-mini-4k-instruct-bnb-4bit` |
| | **Model type:** LoRA (merged) |
| | **Task:** Natural Language → SQL query generation + conversational SQL assistance |
| | **Language:** English |
| | **License:** Apache 2.0 |
| |
|
| | This model is a fine-tuned version of **Phi-3-mini-4k-instruct** (4-bit quantized) specialized in understanding natural language questions about databases and generating correct, clean SQL queries. |
| |
|
| | ## ✨ Key Features |
| |
|
| | - Very good balance between size, speed and SQL generation quality |
| | - Works well with common database dialects (PostgreSQL, MySQL, SQLite, SQL Server, etc.) |
| | - Can explain queries, suggest improvements and handle follow-up questions |
| | - Fast inference even on consumer hardware (especially with 4-bit quantization) |
| |
|
| | ## 🎯 Intended Use & Capabilities |
| |
|
| | **Best for:** |
| | - Converting natural language questions → SQL queries |
| | - Helping beginners learn SQL through explanations |
| | - Quick prototyping of SQL queries in development |
| | - Building SQL chat interfaces / tools / assistants |
| | - Educational purposes |
| |
|
| | **Limitations / Not recommended for:** |
| | - Extremely complex analytical/business intelligence queries |
| | - Real-time query optimization advice |
| | - Very database-specific or proprietary SQL extensions |
| | - Production systems without human review (always validate generated SQL!) |
| |
|
| | ## 🛠️ Quick Start (merged LoRA version) |
| |
|
| | ```python |
| | from transformers import AutoTokenizer, AutoModelForCausalLM |
| | import torch |
| | |
| | model_id = "saadkhi/SQL_Chat_finetuned_model" |
| | |
| | tokenizer = AutoTokenizer.from_pretrained(model_id) |
| | model = AutoModelForCausalLM.from_pretrained( |
| | model_id, |
| | torch_dtype=torch.bfloat16, |
| | device_map="auto", |
| | trust_remote_code=True |
| | ) |
| | |
| | # Simple prompt style (chat template is recommended) |
| | prompt = """Show all customers who placed more than 5 orders in 2024""" |
| | |
| | messages = [{"role": "user", "content": prompt}] |
| | |
| | inputs = tokenizer.apply_chat_template( |
| | messages, |
| | tokenize=True, |
| | add_generation_prompt=True, |
| | return_tensors="pt" |
| | ).to(model.device) |
| | |
| | outputs = model.generate( |
| | inputs, |
| | max_new_tokens=180, |
| | do_sample=False, |
| | temperature=0.0, |
| | pad_token_id=tokenizer.eos_token_id |
| | ) |
| | |
| | print(tokenizer.decode(outputs[0], skip_special_tokens=True)) |
