Instructions to use thanhdath/FINER-SQL-0.5B-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use thanhdath/FINER-SQL-0.5B-v2 with Transformers:

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

pipe = pipeline("text-generation", model="thanhdath/FINER-SQL-0.5B-v2")
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("thanhdath/FINER-SQL-0.5B-v2")
model = AutoModelForCausalLM.from_pretrained("thanhdath/FINER-SQL-0.5B-v2", 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]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use thanhdath/FINER-SQL-0.5B-v2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "thanhdath/FINER-SQL-0.5B-v2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "thanhdath/FINER-SQL-0.5B-v2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/thanhdath/FINER-SQL-0.5B-v2

SGLang

How to use thanhdath/FINER-SQL-0.5B-v2 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 "thanhdath/FINER-SQL-0.5B-v2" \
    --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": "thanhdath/FINER-SQL-0.5B-v2",
		"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 "thanhdath/FINER-SQL-0.5B-v2" \
        --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": "thanhdath/FINER-SQL-0.5B-v2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use thanhdath/FINER-SQL-0.5B-v2 with Docker Model Runner:
```
docker model run hf.co/thanhdath/FINER-SQL-0.5B-v2
```

FINER-SQL-0.5B-v2 / README.md

thanhdath

Model card: link the code repository

c316913 verified 8 days ago

preview code

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---
license: apache-2.0
library_name: transformers
pipeline_tag: text-generation
tags: [text-to-sql, grpo, core-db]
---
# FINER-SQL-0.5B-v2

FINER-SQL 0.5B SQL-Writer continually trained with GRPO (format + execution reward) on **CoRE-DB**, the compact
counterexample-enriched database environment of *Small Language Models for Text-to-SQL against Spurious Correctness*.
Checkpoint step 6750 (dev-selected). Code: [github.com/thanhdath/core-db](https://github.com/thanhdath/core-db).

| BIRD dev EX | Spider dev EX | Spider dev TS | Spider test EX |
|---:|---:|---:|---:|
| 53.59 (822/1534) | 76.31 (789/1034) | 67.89 (702/1034) | 75.27 (1616/2147) |

k = 30 samples, T = 1.0, seed 42, at most 2048 new tokens, vote-after-verify selection, official evaluators (BIRD:
evaluation_bird_ex.py with SQLite 3.45.3 and `--meta_time_out 3600`; Spider: taoyds/test-suite-sql-eval @e97acc54,
default flags; TS on the distilled Spider dev test suites). Prompts: `thanhdath/core-db-evaluation-prompts`
(`bird_dev`, `spider_dev`; Spider test: `spider_test_full_schema`).

Provenance: these weights are byte-identical to `thanhdath/05b-core-noam-13k-ict` @ `cc5327057cc295782a4de3570b9096d0f17493b8`, sub-folder `checkpoints/step06750/checkpoint-6750` (training step 6750).
The saved predictions and the re-scoring script are in `scripts/rq2/` of the code repository.

	---
	license: apache-2.0
	library_name: transformers
	pipeline_tag: text-generation
	tags: [text-to-sql, grpo, core-db]
	---
	# FINER-SQL-0.5B-v2

	FINER-SQL 0.5B SQL-Writer continually trained with GRPO (format + execution reward) on CoRE-DB, the compact
	counterexample-enriched database environment of Small Language Models for Text-to-SQL against Spurious Correctness.
	Checkpoint step 6750 (dev-selected). Code: [github.com/thanhdath/core-db](https://github.com/thanhdath/core-db).

	\| BIRD dev EX \| Spider dev EX \| Spider dev TS \| Spider test EX \|
	\|---:\|---:\|---:\|---:\|
	\| 53.59 (822/1534) \| 76.31 (789/1034) \| 67.89 (702/1034) \| 75.27 (1616/2147) \|

	k = 30 samples, T = 1.0, seed 42, at most 2048 new tokens, vote-after-verify selection, official evaluators (BIRD:
	evaluation_bird_ex.py with SQLite 3.45.3 and `--meta_time_out 3600`; Spider: taoyds/test-suite-sql-eval @e97acc54,
	default flags; TS on the distilled Spider dev test suites). Prompts: `thanhdath/core-db-evaluation-prompts`
	(`bird_dev`, `spider_dev`; Spider test: `spider_test_full_schema`).

	Provenance: these weights are byte-identical to `thanhdath/05b-core-noam-13k-ict` @ `cc5327057cc295782a4de3570b9096d0f17493b8`, sub-folder `checkpoints/step06750/checkpoint-6750` (training step 6750).
	The saved predictions and the re-scoring script are in `scripts/rq2/` of the code repository.