base_model, library_name, license, language, datasets, tags, pipeline_tag

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

1. PEFT adapter loaded on base model
2. Local merged directory after merge_and_unload() + save_pretrained()
3. 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