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AuricErgeson
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Model Details
Model Provider
AuricErgeson
Model Tree
microsoft/phi-2
Input Modalities
Output Modalities
Supported Functionality
| 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 |
python
from transformers import AutoModelForCausalLM, AutoTokenizerimport 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 departmenttransformerstext
### Task: Convert question to SQL. Use only what the question asks. Simple questions need simple SQL.### Database: {your_database_name}### Question: {your_question}### SQL: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())| 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 |
Auric Ergeson Nitonde
📧 auricergesonnitonde@gmail.com
🤗 HuggingFace Profile
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from transformers import AutoModelForCausalLM, AutoTokenizerimport 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### Task: Convert question to SQL. Use only what the question asks. Simple questions need simple SQL.### Database: {your_database_name}### Question: {your_question}### SQL: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())