Instructions to use emonHasan01/qwen2.5-3b-text2sql with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use emonHasan01/qwen2.5-3b-text2sql with Transformers:

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

pipe = pipeline("text-generation", model="emonHasan01/qwen2.5-3b-text2sql")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("emonHasan01/qwen2.5-3b-text2sql")
model = AutoModelForCausalLM.from_pretrained("emonHasan01/qwen2.5-3b-text2sql", device_map="auto")
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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

PEFT
How to use emonHasan01/qwen2.5-3b-text2sql with PEFT:
```
Task type is invalid.
```
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use emonHasan01/qwen2.5-3b-text2sql with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "emonHasan01/qwen2.5-3b-text2sql"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "emonHasan01/qwen2.5-3b-text2sql",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/emonHasan01/qwen2.5-3b-text2sql

SGLang

How to use emonHasan01/qwen2.5-3b-text2sql 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 "emonHasan01/qwen2.5-3b-text2sql" \
    --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": "emonHasan01/qwen2.5-3b-text2sql",
		"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 "emonHasan01/qwen2.5-3b-text2sql" \
        --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": "emonHasan01/qwen2.5-3b-text2sql",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Desktop
Docker Model Runner
How to use emonHasan01/qwen2.5-3b-text2sql with Docker Model Runner:
```
docker model run hf.co/emonHasan01/qwen2.5-3b-text2sql
```

qwen2.5-3b-text2sql / README.md

emonHasan01

Update README.md

6a1accd verified 11 days ago

preview code

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---
language:
  - en
license: apache-2.0
library_name: transformers
pipeline_tag: text-generation
base_model: unsloth/Qwen2.5-3B-Instruct
base_model_relation: finetune
tags:
  - text-to-sql
  - sql
  - unsloth
  - lora
  - peft
  - qwen2.5
  - code-generation
datasets:
  - b-mc2/sql-create-context
metrics:
  - exact_match
model-index:
  - name: qwen2.5-3b-text2sql
    results:
      - task:
          type: text-generation
          name: Text to SQL
        dataset:
          name: sql-create-context
          type: b-mc2/sql-create-context
          split: test
        metrics:
          - type: exact_match
            name: Exact match (AST-normalised, 300 queries)
            value: 74.3
---
<img src="assets/text2sql_light.png" alt="emonHasan01/qwen2.5-3b-text2sql" width="100%">

<div align="center">

[Hugging Face](https://huggingface.co/emonHasan01) | [GitHub](https://github.com/Md-Emon-Hasan) | [LinkedIn](https://www.linkedin.com/in/md-emon-hasan-695483237/) | [WhatsApp](https://wa.me/8801834363533) | [Portfolio](https://emonlabs-ai.hitechparks.com/)

**Author:** [Md. Emon Hasan](https://huggingface.co/emonHasan01)

</div>

# qwen2.5-3b-text2sql

**Qwen2.5-3B-Instruct turned into a SQL writer.** A 16-bit LoRA fine-tune trained with
[Unsloth](https://github.com/unslothai/unsloth) on
[`b-mc2/sql-create-context`](https://huggingface.co/datasets/b-mc2/sql-create-context).

Exact match rises from **7.7% to 74.3%** on 300 held-out queries,
a gain of **+66.6 points**. Trained on a free Colab T4 in 16 minutes.

## Results

![Metrics](assets/metrics.png)

| | exact match | valid SQL | token F1 | mean output chars |
|---|---|---|---|---|
| `unsloth/Qwen2.5-3B-Instruct` | 7.7% | 98.7% | 83.1 | 75 |
| **this model** | **74.3%** | **100.0%** | **97.5** | **75** |

**Exact match is computed on normalised ASTs, not strings.** Both queries are parsed with
[`sqlglot`](https://github.com/tobymao/sqlglot) and re-emitted in a canonical form, so
differences in case, whitespace and quoting do not count as errors.

**The same output cleaning was applied to both models** — markdown fences stripped, leading
prose removed, first statement taken. Without that the comparison would mostly measure
whether a model wraps its answer in backticks. The gain above is about the SQL being right.

![Progress](assets/progress.png)

Measured at three points: untrained, step 150, and step 500.

### Per-query, not just the aggregate

![Per-query](assets/per-query.png)

| | queries |
|---|---|
| both correct | 22 |
| only this model correct | 201 |
| only the base correct | 1 |
| neither correct | 76 |

### Samples

| Question | Reference SQL | Base correct | This model correct |
|---|---|---|---|
| How many Attendances have a Score of 4 – 5? Question 4 | `SELECT MAX(attendance) FROM table_name_88 WHERE score = "4 –` | no | no |
| When did woking compete? | `SELECT date FROM table_name_92 WHERE opponent = "woking"` | no | no |
| What is the local name given to the city of Canberra? | `SELECT capital___endonym__ FROM table_1008653_1 WHERE capita` | no | yes |
| What is the Version before 2001 with a Length of 7:42? | `SELECT version FROM table_name_64 WHERE year < 2001 AND leng` | no | yes |

![Loss](assets/loss.png)

## Usage

```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
tok = AutoTokenizer.from_pretrained("emonHasan01/qwen2.5-3b-text2sql")
model = AutoModelForCausalLM.from_pretrained("emonHasan01/qwen2.5-3b-text2sql", torch_dtype=torch.float16,
                                             device_map="auto")
schema = "CREATE TABLE head (age INTEGER, head_id VARCHAR)"
question = "How many heads of the departments are older than 56?"
messages = [
    {"role": "system", "content": "You convert questions into a single SQLite query. Reply with the query only: no explanation, no markdown, no commentary."},
    {"role": "user", "content": f"Schema:\n{schema}\n\nQuestion: {question}"},
]
prompt = tok.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tok(prompt, return_tensors="pt", add_special_tokens=False).to(model.device)
out = model.generate(**inputs, max_new_tokens=128, do_sample=False)   # greedy
print(tok.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))
```

**Use greedy decoding.** Sampling introduces column and table names that are not in the
schema. The system prompt and user template are in
[`prompt_format.json`](prompt_format.json) and were present in every training example.

## Training

| | |
|---|---|
| Base model | `unsloth/Qwen2.5-3B-Instruct` (3.1B parameters) |
| Method | LoRA at 16-bit via Unsloth |
| Trainable | 29,933,568 parameters (0.97%) |
| Rank / alpha / dropout | 16 / 16 / 0 |
| Target modules | `q,k,v,o,gate,up,down` |
| Learning rate | 0.0002, linear, `adamw_8bit` |
| Batch | 2 × 4 = 8 effective |
| Steps | 500 |
| Max sequence | 512 tokens |
| Peak VRAM | 6.9 GB of 15.0 |
| Hardware | Tesla T4 |
| Training time | ~16 minutes |

### Why this task worked when a previous attempt did not

An earlier experiment distilled reasoning traces into Llama-3.2-3B and GSM8K accuracy
**fell** from 75.0% to 65.0%. That model had already been post-trained on maths by its
authors, leaving little headroom and plenty to disturb.

Fine-tuning pays off where the base model's output does not match the target **form**.
Asked for SQL, an instruct model returns a markdown fence, a preamble and an explanation,
and it reaches for columns the schema does not contain. Those are form errors, and they are
exactly what supervised fine-tuning fixes — which is why the gain here is +66.6 points
rather than a regression.

This model was only published because a gate in the training notebook required at least a
+5 point improvement over the baseline before uploading.

## Limitations

- **Single-table, synthetic schemas.** `sql-create-context` pairs one `CREATE TABLE` with
  one question. Multi-table joins, real production schemas, CTEs and window functions are
  out of distribution.
- **Exact match is strict and also too lenient.** It misses semantically equivalent queries
  written differently, and it credits queries that parse identically but would error against
  a real database. Nothing here was executed against actual data.
- **Evaluated on 300 queries**, so differences under a couple of points are noise.
- **No SQL injection or safety review.** Do not route model output straight into a database
  without validation and a read-only connection.
- **Greedy only.** Sampled decoding was not evaluated and hallucinates schema elements.
- A 3B model will not match a frontier model on hard SQL.

## Reproducing

One Colab notebook on a Tesla T4 produced the baseline, both training stages, all four charts
and this card. Raw numbers in [`results.json`](results.json).

	---
	language:
	- en
	license: apache-2.0
	library_name: transformers
	pipeline_tag: text-generation
	base_model: unsloth/Qwen2.5-3B-Instruct
	base_model_relation: finetune
	tags:
	- text-to-sql
	- sql
	- unsloth
	- lora
	- peft
	- qwen2.5
	- code-generation
	datasets:
	- b-mc2/sql-create-context
	metrics:
	- exact_match
	model-index:
	- name: qwen2.5-3b-text2sql
	results:
	- task:
	type: text-generation
	name: Text to SQL
	dataset:
	name: sql-create-context
	type: b-mc2/sql-create-context
	split: test
	metrics:
	- type: exact_match
	name: Exact match (AST-normalised, 300 queries)
	value: 74.3
	---

	<img src="assets/text2sql_light.png" alt="emonHasan01/qwen2.5-3b-text2sql" width="100%">

	<div align="center">

	[Hugging Face](https://huggingface.co/emonHasan01) \| [GitHub](https://github.com/Md-Emon-Hasan) \| [LinkedIn](https://www.linkedin.com/in/md-emon-hasan-695483237/) \| [WhatsApp](https://wa.me/8801834363533) \| [Portfolio](https://emonlabs-ai.hitechparks.com/)

	Author: [Md. Emon Hasan](https://huggingface.co/emonHasan01)

	</div>

	# qwen2.5-3b-text2sql

	Qwen2.5-3B-Instruct turned into a SQL writer. A 16-bit LoRA fine-tune trained with
	[Unsloth](https://github.com/unslothai/unsloth) on
	[`b-mc2/sql-create-context`](https://huggingface.co/datasets/b-mc2/sql-create-context).

	Exact match rises from 7.7% to 74.3% on 300 held-out queries,
	a gain of +66.6 points. Trained on a free Colab T4 in 16 minutes.

	## Results

	![Metrics](assets/metrics.png)

	\| \| exact match \| valid SQL \| token F1 \| mean output chars \|
	\|---\|---\|---\|---\|---\|
	\| `unsloth/Qwen2.5-3B-Instruct` \| 7.7% \| 98.7% \| 83.1 \| 75 \|
	\| this model \| 74.3% \| 100.0% \| 97.5 \| 75 \|

	Exact match is computed on normalised ASTs, not strings. Both queries are parsed with
	[`sqlglot`](https://github.com/tobymao/sqlglot) and re-emitted in a canonical form, so
	differences in case, whitespace and quoting do not count as errors.

	The same output cleaning was applied to both models — markdown fences stripped, leading
	prose removed, first statement taken. Without that the comparison would mostly measure
	whether a model wraps its answer in backticks. The gain above is about the SQL being right.

	![Progress](assets/progress.png)

	Measured at three points: untrained, step 150, and step 500.

	### Per-query, not just the aggregate

	![Per-query](assets/per-query.png)

	\| \| queries \|
	\|---\|---\|
	\| both correct \| 22 \|
	\| only this model correct \| 201 \|
	\| only the base correct \| 1 \|
	\| neither correct \| 76 \|

	### Samples

	\| Question \| Reference SQL \| Base correct \| This model correct \|
	\|---\|---\|---\|---\|
	\| How many Attendances have a Score of 4 – 5? Question 4 \| `SELECT MAX(attendance) FROM table_name_88 WHERE score = "4 –` \| no \| no \|
	\| When did woking compete? \| `SELECT date FROM table_name_92 WHERE opponent = "woking"` \| no \| no \|
	\| What is the local name given to the city of Canberra? \| `SELECT capital___endonym__ FROM table_1008653_1 WHERE capita` \| no \| yes \|
	\| What is the Version before 2001 with a Length of 7:42? \| `SELECT version FROM table_name_64 WHERE year < 2001 AND leng` \| no \| yes \|

	![Loss](assets/loss.png)

	## Usage

	```python
	from transformers import AutoTokenizer, AutoModelForCausalLM
	import torch

	tok = AutoTokenizer.from_pretrained("emonHasan01/qwen2.5-3b-text2sql")
	model = AutoModelForCausalLM.from_pretrained("emonHasan01/qwen2.5-3b-text2sql", torch_dtype=torch.float16,
	device_map="auto")

	schema = "CREATE TABLE head (age INTEGER, head_id VARCHAR)"
	question = "How many heads of the departments are older than 56?"

	messages = [
	{"role": "system", "content": "You convert questions into a single SQLite query. Reply with the query only: no explanation, no markdown, no commentary."},
	{"role": "user", "content": f"Schema:\n{schema}\n\nQuestion: {question}"},
	]
	prompt = tok.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
	inputs = tok(prompt, return_tensors="pt", add_special_tokens=False).to(model.device)
	out = model.generate(**inputs, max_new_tokens=128, do_sample=False) # greedy
	print(tok.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))
	```

	Use greedy decoding. Sampling introduces column and table names that are not in the
	schema. The system prompt and user template are in
	[`prompt_format.json`](prompt_format.json) and were present in every training example.

	## Training

	\| \| \|
	\|---\|---\|
	\| Base model \| `unsloth/Qwen2.5-3B-Instruct` (3.1B parameters) \|
	\| Method \| LoRA at 16-bit via Unsloth \|
	\| Trainable \| 29,933,568 parameters (0.97%) \|
	\| Rank / alpha / dropout \| 16 / 16 / 0 \|
	\| Target modules \| `q,k,v,o,gate,up,down` \|
	\| Learning rate \| 0.0002, linear, `adamw_8bit` \|
	\| Batch \| 2 × 4 = 8 effective \|
	\| Steps \| 500 \|
	\| Max sequence \| 512 tokens \|
	\| Peak VRAM \| 6.9 GB of 15.0 \|
	\| Hardware \| Tesla T4 \|
	\| Training time \| ~16 minutes \|

	### Why this task worked when a previous attempt did not

	An earlier experiment distilled reasoning traces into Llama-3.2-3B and GSM8K accuracy
	fell from 75.0% to 65.0%. That model had already been post-trained on maths by its
	authors, leaving little headroom and plenty to disturb.

	Fine-tuning pays off where the base model's output does not match the target form.
	Asked for SQL, an instruct model returns a markdown fence, a preamble and an explanation,
	and it reaches for columns the schema does not contain. Those are form errors, and they are
	exactly what supervised fine-tuning fixes — which is why the gain here is +66.6 points
	rather than a regression.

	This model was only published because a gate in the training notebook required at least a
	+5 point improvement over the baseline before uploading.

	## Limitations

	- Single-table, synthetic schemas. `sql-create-context` pairs one `CREATE TABLE` with
	one question. Multi-table joins, real production schemas, CTEs and window functions are
	out of distribution.
	- Exact match is strict and also too lenient. It misses semantically equivalent queries
	written differently, and it credits queries that parse identically but would error against
	a real database. Nothing here was executed against actual data.
	- Evaluated on 300 queries, so differences under a couple of points are noise.
	- No SQL injection or safety review. Do not route model output straight into a database
	without validation and a read-only connection.
	- Greedy only. Sampled decoding was not evaluated and hallucinates schema elements.
	- A 3B model will not match a frontier model on hard SQL.

	## Reproducing

	One Colab notebook on a Tesla T4 produced the baseline, both training stages, all four charts
	and this card. Raw numbers in [`results.json`](results.json).