README.md

metadata

base_model: unsloth/Qwen2.5-Coder-7B-Instruct-bnb-4bit
library_name: transformers
pipeline_tag: text-generation
license: apache-2.0
language:
  - en
tags:
  - text-generation-inference
  - transformers
  - unsloth
  - qwen2
  - qlora
  - peft
  - sql
  - text-to-sql
  - code-generation
datasets:
  - DanielRegaladoCardoso/text-to-sql-mix-v2
model-index:
  - name: sql-generator-qwen25-coder-7b-lora
    results:
      - task:
          type: text-generation
          name: Text-to-SQL Generation
        dataset:
          name: text-to-sql-mix-v2
          type: DanielRegaladoCardoso/text-to-sql-mix-v2
        metrics:
          - type: train_loss
            value: 0.2658
            name: Final training loss

SQL Generator — Qwen2.5-Coder-7B (QLoRA)

Fine-tuned Qwen2.5-Coder-7B-Instruct for text-to-SQL generation. Given a SQL schema and a natural-language question, the model produces a syntactically correct SQL query.

Trained as part of the SQL Agent LLMOps project — a multi-model SQL agent with deployment on HuggingFace Spaces.

Model details

Base model	unsloth/Qwen2.5-Coder-7B-Instruct-bnb-4bit
Architecture	Qwen2 (7.6B params, 4-bit quantized base)
Fine-tuning method	QLoRA via Unsloth + TRL
LoRA rank	16
LoRA alpha	32
Target modules	q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
Trainable params	70 M (0.9% of base)
Language	English
License	Apache 2.0

Training data

DanielRegaladoCardoso/text-to-sql-mix-v2 — a curated mix of 5 public text-to-SQL datasets:

• b-mc2/sql-create-context
• gretelai/synthetic_text_to_sql
• knowrohit07/know_sql
• NumbersStation/NSText2SQL
• Clinton/Text-to-sql-v1

Final training set: 672,949 examples (after filtering sequences > 1024 tokens — kept 93.1% of original 723,097 rows). With sequence packing, this compressed to 154,462 effective sequences of length 1024.

Training configuration

Hyperparameter	Value
Hardware	1× NVIDIA L40S (48 GB)
Epochs	1
Batch size (per device)	16
Gradient accumulation	1
Effective batch size	16
Max sequence length	1024
Learning rate	1e-4
LR scheduler	Cosine
Warmup ratio	0.03
Optimizer	adamw_8bit
Precision	bf16
Sequence packing	Enabled
Total steps	9,654
Wall-clock time	13.5 hours
Final training loss	0.2658

Prompt format

The model expects a chat-style prompt with a system message defining the SQL-expert role and a user message containing the schema and question:

<|im_start|>system
You are a SQL expert. Given a SQL schema and a natural-language question, generate a correct SQL query answering the question. Return only the SQL.
<|im_end|>
<|im_start|>user
### Schema
CREATE TABLE players (id INT, name VARCHAR, hometown VARCHAR);

### Question
List all players from Tampa, Florida.
<|im_end|>
<|im_start|>assistant

Usage

Option A — Load merged 16-bit model (recommended)

from transformers import AutoModelForCausalLM, AutoTokenizer

REPO = "DanielRegaladoCardoso/sql-generator-qwen25-coder-7b-lora"

model = AutoModelForCausalLM.from_pretrained(REPO, torch_dtype="auto", device_map="auto")
tokenizer = AutoTokenizer.from_pretrained(REPO)

messages = [
    {"role": "system", "content": "You are a SQL expert. Given a SQL schema and a natural-language question, generate a correct SQL query answering the question. Return only the SQL."},
    {"role": "user", "content": "### Schema\nCREATE TABLE players (id INT, name VARCHAR, hometown VARCHAR);\n\n### Question\nList all players from Tampa, Florida."},
]

input_ids = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt").to(model.device)
out = model.generate(input_ids, max_new_tokens=256, do_sample=False)
print(tokenizer.decode(out[0][input_ids.shape[1]:], skip_special_tokens=True))
# → SELECT * FROM players WHERE hometown = 'Tampa, Florida'

Option B — Load LoRA adapter on top of base model

Useful if you want to keep the base model in 4-bit (lower VRAM footprint).

from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

base = AutoModelForCausalLM.from_pretrained(
    "Qwen/Qwen2.5-Coder-7B-Instruct",
    torch_dtype=torch.bfloat16,
    device_map="auto",
)
model = PeftModel.from_pretrained(base, "DanielRegaladoCardoso/sql-generator-qwen25-coder-7b-lora")
tokenizer = AutoTokenizer.from_pretrained("DanielRegaladoCardoso/sql-generator-qwen25-coder-7b-lora")

Option C — Inference with Unsloth (fastest)

from unsloth import FastLanguageModel

model, tokenizer = FastLanguageModel.from_pretrained(
    "DanielRegaladoCardoso/sql-generator-qwen25-coder-7b-lora",
    max_seq_length=1024,
    load_in_4bit=True,
)
FastLanguageModel.for_inference(model)

Files

File	Size	Purpose
adapter_model.safetensors	161 MB	LoRA adapter weights
adapter_config.json	1 KB	LoRA configuration
model-0000{1..4}-of-00004.safetensors	15.2 GB	Merged 16-bit model
tokenizer.json + tokenizer_config.json	11 MB	Tokenizer
chat_template.jinja	4 KB	Qwen chat template

Limitations

• English only — training data is English; performance on other languages is not validated.
• Sequence length cap — examples requiring > 1024 tokens (large schemas, complex multi-CTE queries) were filtered out during training. The model may underperform on inputs above this length.
• No execution validation — the model is trained to produce syntactically correct SQL, but generated queries are not guaranteed to execute or return correct results without manual review. Always sanity-check against your real database.
• Single dialect bias — training data mixes multiple SQL dialects (SQLite, ANSI, MySQL); the model may produce queries that lean toward one dialect over another.

Citation

If you use this model, please cite the SQL Agent LLMOps project.

@misc{regalado2026sqlagent,
  author       = {Daniel Regalado Cardoso},
  title        = {SQL Generator: Qwen2.5-Coder-7B fine-tuned for text-to-SQL},
  year         = {2026},
  howpublished = {\url{https://huggingface.co/DanielRegaladoCardoso/sql-generator-qwen25-coder-7b-lora}},
}

Acknowledgments

• Unsloth — 2× faster QLoRA training
• TRL — SFTTrainer
• Qwen team — Qwen2.5-Coder-7B base model
• All authors of the source datasets cited above