library_name, tags, language, license, base_model, datasets, pipeline_tag

library_name	tags	language	license	base_model	datasets	pipeline_tag
transformers	text-to-sql sql natural-language-processing peft qlora phi-2 sqlite database code-generation question-answering	en	mit	microsoft/phi-2	spider	text-generation

🦌 Antelope Text-to-SQL

Convert plain English questions into SQL queries instantly.
Lightweight, fast, and runs on CPU. No database expertise needed.

🚀 Try the live demo →

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What it does

Question	Database	Output
How many employees are there?	company	SELECT COUNT(*) FROM employees
List all customers from Germany	store	SELECT * FROM customers WHERE country = 'Germany'
What is the average salary by department?	hr	SELECT department, AVG(salary) FROM employees GROUP BY department

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Quick Start

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "AuricErgeson/Antelope-textTosql"

tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    trust_remote_code=True
)

def generate_sql(question, db=""):
    prompt = (
        f"### Task: Convert question to SQL. Use only what the question asks. Simple questions need simple SQL.\n"
        f"### Database: {db}\n"
        f"### Question: {question}\n"
        f"### SQL:"
    )
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    with torch.no_grad():
        out = model.generate(
            **inputs,
            max_new_tokens=128,
            temperature=0.1,
            do_sample=True,
            pad_token_id=tokenizer.eos_token_id
        )
    decoded = tokenizer.decode(out[0], skip_special_tokens=True)
    return decoded.split("### SQL:")[-1].strip().split("\n")[0]

print(generate_sql("How many employees are in each department?", db="company"))
# → SELECT department, COUNT(*) FROM employees GROUP BY department

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Why use this model?

• ✅ Small — 2.7B params, runs on modest hardware
• ✅ Fast — CPU inference possible, GPU recommended
• ✅ Open — MIT license, use anywhere
• ✅ No setup — works out of the box with transformers
• ✅ Cross-domain — trained on 200+ database schemas

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Prompt Format

### Task: Convert question to SQL. Use only what the question asks. Simple questions need simple SQL.
### Database: {your_database_name}
### Question: {your_question}
### SQL:

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Via HuggingFace Inference API

import requests

API_URL = "https://api-inference.huggingface.co/models/AuricErgeson/Antelope-textTosql"
headers = {"Authorization": "Bearer YOUR_HF_TOKEN"}

response = requests.post(API_URL, headers=headers, json={
    "inputs": "### Task: Convert question to SQL. Use only what the question asks. Simple questions need simple SQL.\n### Database: company\n### Question: How many employees?\n### SQL:"
})
print(response.json())

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Training Details

Property	Value
Base model	microsoft/phi-2 (2.7B)
Method	QLoRA (4-bit + LoRA)
Dataset	Spider (7,000+ examples, 200+ databases)
LoRA rank	16
LoRA alpha	32
Target modules	q_proj, v_proj
Learning rate	2e-4
Epochs	3
Hardware	NVIDIA A100 (Google Colab)
Training time	~1.5 hours
Adapter size	~21MB
Merged model size	~5.56GB

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Limitations

• Not recommended for production databases without output validation
• Complex multi-join queries may be inaccurate
• Does not infer table/column names — provide your database name for best results

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Roadmap

• Text-to-SQL (this model)
• Text-to-Regex (coming soon)
• Text-to-Shell (planned)

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Author

Auric Ergeson Nitonde
📧 auricergesonnitonde@gmail.com
🤗 HuggingFace Profile

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