Instructions to use build-small-hackathon/lfed-qwen2.5-coder-14b-sql-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use build-small-hackathon/lfed-qwen2.5-coder-14b-sql-lora with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("unsloth/qwen2.5-coder-14b-instruct-bnb-4bit")
model = PeftModel.from_pretrained(base_model, "build-small-hackathon/lfed-qwen2.5-coder-14b-sql-lora")

Transformers

How to use build-small-hackathon/lfed-qwen2.5-coder-14b-sql-lora with Transformers:

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

pipe = pipeline("text-generation", model="build-small-hackathon/lfed-qwen2.5-coder-14b-sql-lora")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("build-small-hackathon/lfed-qwen2.5-coder-14b-sql-lora", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use build-small-hackathon/lfed-qwen2.5-coder-14b-sql-lora with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "build-small-hackathon/lfed-qwen2.5-coder-14b-sql-lora"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "build-small-hackathon/lfed-qwen2.5-coder-14b-sql-lora",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/build-small-hackathon/lfed-qwen2.5-coder-14b-sql-lora

SGLang

How to use build-small-hackathon/lfed-qwen2.5-coder-14b-sql-lora 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 "build-small-hackathon/lfed-qwen2.5-coder-14b-sql-lora" \
    --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": "build-small-hackathon/lfed-qwen2.5-coder-14b-sql-lora",
		"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 "build-small-hackathon/lfed-qwen2.5-coder-14b-sql-lora" \
        --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": "build-small-hackathon/lfed-qwen2.5-coder-14b-sql-lora",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio

How to use build-small-hackathon/lfed-qwen2.5-coder-14b-sql-lora with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for build-small-hackathon/lfed-qwen2.5-coder-14b-sql-lora to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for build-small-hackathon/lfed-qwen2.5-coder-14b-sql-lora to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for build-small-hackathon/lfed-qwen2.5-coder-14b-sql-lora to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="build-small-hackathon/lfed-qwen2.5-coder-14b-sql-lora",
    max_seq_length=2048,
)

Docker Model Runner
How to use build-small-hackathon/lfed-qwen2.5-coder-14b-sql-lora with Docker Model Runner:
```
docker model run hf.co/build-small-hackathon/lfed-qwen2.5-coder-14b-sql-lora
```

LFED SQL Assistant — Qwen2.5-Coder-14B-LoRA

A LoRA adapter that turns plain-English school-data questions into read-only DuckDB SQL queries. Built for the Local First Education Data Framework (LFED), a local-first analytics assistant for school administrators.

Live demo: https://huggingface.co/spaces/build-small-hackathon/Kasualdad_LFED
GGUF (local/llama.cpp): https://huggingface.co/build-small-hackathon/lfed-qwen2.5-coder-14b-sql-gguf
Project: https://huggingface.co/spaces/build-small-hackathon/Kasualdad_LFED

Model Details

Developer: build-small-hackathon (HF Build Small Hackathon, Chapter One: Backyard AI)
Base model: unsloth/qwen2.5-coder-14b-instruct-bnb-4bit
Model type: Causal LM, text-to-SQL, instruction-tuned
Language: English (questions and SQL)
License: Apache 2.0
Fine-tuning method: Unsloth QLoRA (r=32, α=32, target modules q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj)
Training data: 27,859 synthetic NL→SQL pairs over a 5-table education schema
Adapter size: ~551 MB
Framework versions: PEFT 0.19.1, transformers, Unsloth, TRL

Intended Use

This adapter is designed for a single downstream task: converting natural-language questions about school data into safe, read-only SQL.

Direct use

Drop the adapter onto the base model for inference in the LFED Gradio app.
Run locally with the matching GGUF or with transformers + PEFT.

Suitable questions

"How many students were chronically absent at Lincoln Elementary in 2023-2024?"
"What is the suspension rate by race/ethnicity at Washington Middle?"
"Show the average GPA for English learners vs non-English learners."
"Which school has the highest enrollment growth since 2021?"

Out-of-scope use

Not a general chatbot or coding assistant.
Not trained on real student PII; the demo uses synthetic seed data only.
Not suitable for arbitrary SQL dialects beyond DuckDB-compatible queries.
Should not be used for write operations; the execution guard allows only SELECT statements.

Training Details

Training data

Source: synthetic data generated from hand-written templates, augmented with Gretel, and rephrased for natural-language variety.
Schema: 5 tables — students, enrollment, attendance, discipline, grades.
Coverage: single-table aggregations, joins, filtering by school/year/grade, subgroup comparisons, ranking, and simple rates/percentages.
Size: 27,859 question→SQL pairs.
Format: each example contains a question and a sql field.
Data generation scripts: modal_train/generate_synthetic_v2.py, modal_train/augment_gretel.py, modal_train/rephrase_pairs.py in the project repo.

Training procedure

Setting	Value
Optimizer	AdamW (Unsloth default)
Learning rate	1e-4
LR scheduler	cosine
Warmup steps	10
Batch size	4
Gradient accumulation	4
Epochs	2
LoRA r	32
LoRA α	32
LoRA dropout	0
Target modules	all linear layers
Quantization	4-bit (bnb NF4)
Max sequence length	2048
Trainer	SFTTrainer (TRL)
Packing	False
Hardware	Modal A10G

Training completed on 2026-06-10.

Outputs

Artifact	Location
This LoRA adapter	`build-small-hackathon/lfed-qwen2.5-coder-14b-sql-lora`
Merged GGUF Q4_K_M	`build-small-hackathon/lfed-qwen2.5-coder-14b-sql-gguf`
Training code	`modal_train/` in the LFED project repo

Evaluation

Approach

Evaluation is currently manual: a bank of 15 real-world-style queries spanning attendance, discipline, grades, enrollment, and equity comparisons is run through the LFED demo UI. Each query is scored on:

Correctness — does the answer match the expected aggregation/join?
SQL quality — is the generated SQL valid, safe, and readable?
UX — is the summary + table useful?
Latency — does the query complete within a reasonable time?

Known limitations

The model is fine-tuned on synthetic data; real-world schema variations require additional prompting or fine-tuning.
It occasionally needs explicit school name and school year in the question to produce the most reliable query.
Complex multi-step reasoning (e.g., "students who improved GPA across consecutive years") can be brittle.
Percentage/rate formatting is handled by the downstream app, not the model; the model may return either 0–1 proportions or already-scaled percentages.
No formal academic benchmark evaluation has been run.

How to Use

With transformers + PEFT (HF Space path)

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

base_id = "unsloth/qwen2.5-coder-14b-instruct-bnb-4bit"
adapter_id = "build-small-hackathon/lfed-qwen2.5-coder-14b-sql-lora"

tokenizer = AutoTokenizer.from_pretrained(base_id)
model = AutoModelForCausalLM.from_pretrained(
    base_id,
    torch_dtype=torch.float16,
    device_map="auto",
)
model = PeftModel.from_pretrained(model, adapter_id, torch_device="cpu")

prompt = """You are an assistant that converts school-data questions into DuckDB SQL.
Schema:
- students(student_id, school_name, grade_level, gender, race_ethnicity, english_learner, special_education, economically_disadvantaged)
- attendance(student_id, school_name, school_year, absence_count, is_chronically_absent)

Question: How many chronically absent students at Lincoln Elementary in 2023-2024?
SQL:"""

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=128, temperature=0.0)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

With llama.cpp (local-first path)

Use the matching GGUF:

llama-cli \
  -m lfed-qwen2.5-coder-14b-sql-gguf/ggml-model-q4_k_m.gguf \
  -p "Question: How many chronically absent students at Lincoln Elementary in 2023-2024?\nSQL:" \
  -n 128 --temp 0.0

Or run the full LFED app locally:

git checkout -b product local-llamacpp-v1
python3.12 -m venv .venv && source .venv/bin/activate
pip install -r requirements.txt
python app.py

Bias, Risks, and Limitations

Synthetic data: the training data is generated from templates and rephrased. Demographic patterns in the seed data do not represent any real population; they exist to exercise joins and filters.
No PII handling training: the model has no special safeguards around personally identifiable information because the schema uses anonymized student_ids only.
Read-only enforcement is app-level: the adapter itself will emit any SQL-like text; the downstream data_engine.py validator enforces SELECT-only and forbidden-token rules.
Hallucinated columns/tables: the model may occasionally reference a plausible-sounding column that does not exist. The execution guard catches these via schema-aware EXPLAIN validation.
Numeric accuracy: the model writes the SQL; percentage interpretation depends on the application layer. Users should verify rates and percentages against their own conventions.

Environmental Impact

Estimated training energy use on a Modal A10G for ~2 epochs:

Hardware type: NVIDIA A10G
Training time: approximately 1–2 hours
Cloud provider: Modal
Region: likely US-east (Modal default)
Carbon emitted: not precisely measured; rough estimate using ML CO2 Impact calculator is on the order of tens to low-hundreds of grams of CO2eq.

Citation

If you use this model, please cite the base model and the LFED project:

BibTeX:

@misc{lfed_sql_adapter,
  title={Local First Education Data Framework: A Qwen2.5-Coder-14B LoRA Adapter for School-Data Text-to-SQL},
  author={build-small-hackathon},
  year={2026},
  howpublished={\url{https://huggingface.co/build-small-hackathon/lfed-qwen2.5-coder-14b-sql-lora}}
}

APA: build-small-hackathon. (2026). Local First Education Data Framework: A Qwen2.5-Coder-14B LoRA adapter for school-data text-to-SQL. Hugging Face. https://huggingface.co/build-small-hackathon/lfed-qwen2.5-coder-14b-sql-lora

Framework versions

PEFT 0.19.1

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