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-qwen2.5-coder-14b-sql-lora / README.md

Kasualdad

docs: fix model card formatting

3c6b90a verified 20 days ago

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	---
	base_model: unsloth/qwen2.5-coder-14b-instruct-bnb-4bit
	library_name: peft
	pipeline_tag: text-generation
	tags:
	- base_model:adapter:unsloth/qwen2.5-coder-14b-instruct-bnb-4bit
	- lora
	- sft
	- transformers
	- trl
	- unsloth
	- text-to-sql
	- education
	- local-first
	---

	# 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`](https://huggingface.co/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:

	1. Correctness — does the answer match the expected aggregation/join?
	2. SQL quality — is the generated SQL valid, safe, and readable?
	3. UX — is the summary + table useful?
	4. 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)

	```python
	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:

	```bash
	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:

	```bash
	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_id`s 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](https://mlco2.github.io/impact#compute) 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:
	```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