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Model: AuricErgeson/Antelope-textTosql
Source: Original Platform

README.md

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+---
+library_name: transformers
+tags:
+- text-to-sql
+- sql
+- natural-language-processing
+- peft
+- qlora
+- phi-2
+- sqlite
+- database
+- code-generation
+- question-answering
+language:
+- en
+license: mit
+base_model: microsoft/phi-2
+datasets:
+- spider
+pipeline_tag: 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 →](https://huggingface.co/spaces/AuricErgeson/Antelope-textTosql-demo)**
+
+---
+
+## 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` |
+
+---
+
+## Quick Start
+
+```python
+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
+```
+
+---
+
+## 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
+
+---
+
+## 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:
+```
+
+---
+
+## Via HuggingFace Inference API
+
+```python
+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())
+```
+
+---
+
+## 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 |
+
+---
+
+## 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
+
+---
+
+## Roadmap
+
+- [x] Text-to-SQL (this model)
+- [ ] Text-to-Regex (coming soon)
+- [ ] Text-to-Shell (planned)
+
+---
+
+## Author
+
+**Auric Ergeson Nitonde**  
+📧 auricergesonnitonde@gmail.com  
+🤗 [HuggingFace Profile](https://huggingface.co/AuricErgeson)
+
+*If this model helped you, consider leaving a like ⭐ — it helps others find it.*