Instructions to use Shizu0n/phi3-mini-sql-generator-merged with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Shizu0n/phi3-mini-sql-generator-merged with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Shizu0n/phi3-mini-sql-generator-merged", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Shizu0n/phi3-mini-sql-generator-merged", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("Shizu0n/phi3-mini-sql-generator-merged", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Shizu0n/phi3-mini-sql-generator-merged with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Shizu0n/phi3-mini-sql-generator-merged"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Shizu0n/phi3-mini-sql-generator-merged",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Shizu0n/phi3-mini-sql-generator-merged

SGLang

How to use Shizu0n/phi3-mini-sql-generator-merged with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Shizu0n/phi3-mini-sql-generator-merged" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Shizu0n/phi3-mini-sql-generator-merged",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Shizu0n/phi3-mini-sql-generator-merged" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Shizu0n/phi3-mini-sql-generator-merged",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Shizu0n/phi3-mini-sql-generator-merged with Docker Model Runner:
```
docker model run hf.co/Shizu0n/phi3-mini-sql-generator-merged
```

phi3-mini-sql-generator-merged / README.md

Shizu0n

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metadata

base_model: microsoft/Phi-3-mini-4k-instruct
library_name: transformers
license: mit
language:
  - en
datasets:
  - b-mc2/sql-create-context
tags:
  - sql
  - text-to-sql
  - code-generation
  - phi-3
  - fine-tuned
  - text-generation
  - phi3
pipeline_tag: text-generation

Phi-3 Mini SQL Generator — Merged Model

Merged standalone version of Shizu0n/phi3-mini-sql-generator — LoRA adapter weights fused into Phi-3-mini-4k-instruct. No PEFT dependency required for inference.

Evaluation — Base vs Fine-tuned

Evaluated on 200 held-out examples from b-mc2/sql-create-context.

Model	Exact Match
Phi-3-mini-4k-instruct (base)	2.0%
This model (fine-tuned)	73.5%

Exact match: normalized SQL comparison (lowercase, strip whitespace/semicolons).

Why two versions?

Repo	Purpose
`Shizu0n/phi3-mini-sql-generator`	QLoRA adapter — documents the training pipeline
`Shizu0n/phi3-mini-sql-generator-merged`	Merged standalone — used for deployment and inference

Training Details

Dataset: b-mc2/sql-create-context — 1,000 train / 200 validation examples
Method: QLoRA (4-bit NF4, LoRA rank 16, alpha 32, target modules: qkv_proj/o_proj/gate_up_proj/down_proj)
Hardware: NVIDIA T4 (Google Colab free tier)
Training time: ~21 min
Final train loss: 0.6526
Best checkpoint: step 250 (by eval loss)

Inference Example

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "Shizu0n/phi3-mini-sql-generator-merged"

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=False,
    attn_implementation="eager",
)
model.eval()

prompt = (
    "Given the following SQL table, write a SQL query.\n\n"
    "Table: employees (id, name, department, salary)\n\n"
    "Question: What is the average salary per department?\n\nSQL:"
)

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
with torch.inference_mode():
    outputs = model.generate(
        **inputs,
        max_new_tokens=80,
        do_sample=False,
        use_cache=False,
        repetition_penalty=1.1,
        pad_token_id=tokenizer.eos_token_id,
    )
prompt_len = inputs["input_ids"].shape[-1]
print(tokenizer.decode(outputs[0][prompt_len:], skip_special_tokens=True))

Expected output:

SELECT AVG(salary), department FROM employees GROUP BY department

Validation

Merge accepted after three smoke tests:

PEFT adapter loaded on base model
Local merged directory after merge_and_unload() + save_pretrained()
Downloaded from this repo with force_download=True

Limitations

Fine-tuned on 1,000 examples — best suited for simple to medium complexity SELECT queries
Not tested on dialect-specific SQL (PostgreSQL/MySQL-specific functions)
May struggle with multi-table JOINs and nested subqueries