Instructions to use thanhdath/FINER-SQL-3B-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use thanhdath/FINER-SQL-3B-v2 with Transformers:

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

pipe = pipeline("text-generation", model="thanhdath/FINER-SQL-3B-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-3B-v2")
model = AutoModelForCausalLM.from_pretrained("thanhdath/FINER-SQL-3B-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-3B-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-3B-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-3B-v2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/thanhdath/FINER-SQL-3B-v2

SGLang

How to use thanhdath/FINER-SQL-3B-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-3B-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-3B-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-3B-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-3B-v2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use thanhdath/FINER-SQL-3B-v2 with Docker Model Runner:
```
docker model run hf.co/thanhdath/FINER-SQL-3B-v2
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

FINER-SQL-3B-v2

FINER-SQL 3B 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 1000 (dev-selected). Code: github.com/thanhdath/core-db.

BIRD dev EX	Spider dev EX	Spider dev TS	Spider test EX
68.84 (1056/1534)	86.75 (897/1034)	81.53 (843/1034)	87.66 (1882/2147)

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/3b-core-noam-ict @ 0c74daf264c89d9e4bdf1378bcf065cb76b1ef0f, sub-folder checkpoints/step01000/checkpoint-1000 (training step 1000). The saved predictions and the re-scoring script are in scripts/rq2/ of the code repository.

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Safetensors

Model size

3B params

Tensor type

BF16

Model tree for thanhdath/FINER-SQL-3B-v2

Quantizations

1 model