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distil-labs
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Model Details
Model Provider
distil-labs
Model Tree
Qwen/Qwen3-4B
Input Modalities
Output Modalities
Supported Functionality
| Metric | DeepSeek-V3 (Teacher) | Qwen3-4B (Base) | This Model |
|---|---|---|---|
| LLM-as-a-Judge | 80% | 62% | 80% |
| Exact Match | 48% | 16% | 60% |
| ROUGE | 87.6% | 84.2% | 89.5% |
| METEOR | 85.1% | 87.3% | 86.1% |
The fine-tuned model matches the 685B parameter teacher on LLM-as-a-Judge accuracy and exceeds it on exact match and ROUGE scores.
python
from transformers import AutoModelForCausalLM, AutoTokenizer model = AutoModelForCausalLM.from_pretrained("distil-labs/distil-qwen3-4b-text2sql")tokenizer = AutoTokenizer.from_pretrained("distil-labs/distil-qwen3-4b-text2sql") schema = """CREATE TABLE employees ( id INTEGER PRIMARY KEY, name TEXT NOT NULL, department TEXT, salary INTEGER);""" question = "How many employees earn more than 50000?" messages = [ { "role": "system", "content": """You are a problem solving model working on task_description XML block:<task_description>You are given a database schema and a natural language question. Generate the SQL query that answers the question. Input:- Schema: One or two table definitions in SQL DDL format- Question: Natural language question about the data Output:- A single SQL query that answers the question- No explanations, comments, or additional text Rules:- Use only tables and columns from the provided schema- Use uppercase SQL keywords (SELECT, FROM, WHERE, etc.)- Use SQLite-compatible syntax</task_description>You will be given a single task in the question XML blockSolve only the task in question block.Generate only the answer, do not generate anything else""" }, { "role": "user", "content": f"""Now for the real task, solve the task in question block.Generate only the solution, do not generate anything else<question>Schema:{schema} Question: {question}</question>""" }] text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)inputs = tokenizer(text, return_tensors="pt")outputs = model.generate(**inputs, max_new_tokens=256, temperature=0)print(tokenizer.decode(outputs[0], skip_special_tokens=True))For local inference, use the quantized GGUF versions:
bash
# Download and create Ollama modelollama create distil-qwen3-4b-text2sql -f Modelfile # Run inferenceollama run distil-qwen3-4b-text2sql| Property | Value |
|---|---|
| Base Model | Qwen/Qwen3-4B |
| Parameters | 4 billion |
| Architecture | Qwen3ForCausalLM |
| Context Length | 262,144 tokens |
| Precision | bfloat16 |
| Training Data | ~10,000 synthetic examples |
| Teacher Model | DeepSeek-V3 |
This model was trained using the Distil Labs platform:
text
Schema:CREATE TABLE table_name ( column_name DATA_TYPE [CONSTRAINTS], ...); Question: Natural language question about the dataA single SQL query with:
This model is released under the Apache 2.0 license.
bibtex
@misc{distil-qwen3-4b-text2sql, author = {Distil Labs}, title = {Distil-Qwen3-4B-Text2SQL: A Fine-tuned Model for Natural Language to SQL}, year = {2025}, publisher = {Hugging Face}, url = {https://huggingface.co/distil-labs/distil-qwen3-4b-text2sql}}from transformers import AutoModelForCausalLM, AutoTokenizer model = AutoModelForCausalLM.from_pretrained("distil-labs/distil-qwen3-4b-text2sql")tokenizer = AutoTokenizer.from_pretrained("distil-labs/distil-qwen3-4b-text2sql") schema = """CREATE TABLE employees ( id INTEGER PRIMARY KEY, name TEXT NOT NULL, department TEXT, salary INTEGER);""" question = "How many employees earn more than 50000?" messages = [ { "role": "system", "content": """You are a problem solving model working on task_description XML block:<task_description>You are given a database schema and a natural language question. Generate the SQL query that answers the question. Input:- Schema: One or two table definitions in SQL DDL format- Question: Natural language question about the data Output:- A single SQL query that answers the question- No explanations, comments, or additional text Rules:- Use only tables and columns from the provided schema- Use uppercase SQL keywords (SELECT, FROM, WHERE, etc.)- Use SQLite-compatible syntax</task_description>You will be given a single task in the question XML blockSolve only the task in question block.Generate only the answer, do not generate anything else""" }, { "role": "user", "content": f"""Now for the real task, solve the task in question block.Generate only the solution, do not generate anything else<question>Schema:{schema} Question: {question}</question>""" }] text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)inputs = tokenizer(text, return_tensors="pt")outputs = model.generate(**inputs, max_new_tokens=256, temperature=0)print(tokenizer.decode(outputs[0], skip_special_tokens=True))# Download and create Ollama modelollama create distil-qwen3-4b-text2sql -f Modelfile # Run inferenceollama run distil-qwen3-4b-text2sqlSchema:CREATE TABLE table_name ( column_name DATA_TYPE [CONSTRAINTS], ...);Question: Natural language question about the data@misc{distil-qwen3-4b-text2sql, author = {Distil Labs}, title = {Distil-Qwen3-4B-Text2SQL: A Fine-tuned Model for Natural Language to SQL}, year = {2025}, publisher = {Hugging Face}, url = {https://huggingface.co/distil-labs/distil-qwen3-4b-text2sql}}