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