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

Libraries

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

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

pipe = pipeline("text-generation", model="thanhdath/FINER-SQL-0.5B-Spider")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("thanhdath/FINER-SQL-0.5B-Spider")
model = AutoModelForCausalLM.from_pretrained("thanhdath/FINER-SQL-0.5B-Spider")
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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

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

Use Docker

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

SGLang

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

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

thanhdath commited on 11 days ago

Commit

877376f

verified ·

1 Parent(s): a0263f4

Upload FINER-SQL-0.5B-Spider checkpoint (cp2000) with comparison README

Browse files

Files changed (12) hide show

.gitattributes +1 -0
README.md +134 -0
added_tokens.json +24 -0
chat_template.jinja +4 -0
config.json +54 -0
generation_config.json +13 -0
merges.txt +0 -0
model.safetensors +3 -0
special_tokens_map.json +31 -0
tokenizer.json +3 -0
tokenizer_config.json +212 -0
vocab.json +0 -0

.gitattributes CHANGED Viewed

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text

 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

README.md ADDED Viewed

	@@ -0,0 +1,134 @@

+---
+base_model:
+- griffith-bigdata/Qwen-2.5-Coder-0.5B-SQL-Writer
+license: apache-2.0
+language:
+- en
+tags:
+- text-to-sql
+- spider
+- grpo
+- finer-sql
+- code
+library_name: transformers
+pipeline_tag: text-generation
+---
+# FINER-SQL-0.5B-Spider
+A small but capable 0.5 B-parameter Text-to-SQL model fine-tuned from
+[`griffith-bigdata/Qwen-2.5-Coder-0.5B-SQL-Writer`](https://huggingface.co/griffith-bigdata/Qwen-2.5-Coder-0.5B-SQL-Writer)
+with GRPO + the FINER-SQL dense rewards (Memory + Atomic).
+✅ **75.0% Execution Accuracy on Spider Dev** (n=30, value-aware voting). Runs on a 4-8 GB GPU.
+📄 See other models: https://huggingface.co/collections/griffith-bigdata/finer-sql
+📄 GitHub: https://github.com/thanhdath/finer-sql/tree/main
+---
+## FINER-SQL Model Family — Comparison Across All Sizes
+| Model | Params | BIRD Dev (n=30, vav) | Spider Dev (n=30, vav, +agg_hint) |
+|-------|--------|---------------------|----------------------------------|
+| [FINER-SQL-3B-BIRD](https://huggingface.co/griffith-bigdata/FINER-SQL-3B-BIRD) | 3 B | **67.54%** ✅ | 83.8% |
+| [FINER-SQL-3B-Spider](https://huggingface.co/griffith-bigdata/FINER-SQL-3B-Spider) | 3 B | 63.04% | **85.10%** ✅ |
+| [FINER-SQL-0.5B-BIRD](https://huggingface.co/griffith-bigdata/FINER-SQL-0.5B-BIRD) | 0.5 B | **50.85%** ✅ | 68.6% |
+| **FINER-SQL-0.5B-Spider** *(this model)* | 0.5 B | TBD | **75.0%** ✅ |
+The 0.5 B Spider model is **6.4 pp better** than the 0.5 B BIRD model on Spider Dev — confirming dataset-specific specialisation matters even at small scales.
+---
+## Inference
+### Quick start (vLLM)
+```python
+from vllm import LLM, SamplingParams
+llm = LLM(
+    model="griffith-bigdata/FINER-SQL-0.5B-Spider",
+    dtype="bfloat16",
+    max_model_len=4096,
+    gpu_memory_utilization=0.7,
+)
+system_prompt = """You are a meticulous SQL expert. Generate a single, correct SQL query for the user question and the provided database schema.
+Follow this exact response format:
+Rules:
+- Output exactly one SQL statement.
+- The SQL must be executable on SQLite.
+- Do not include any explanatory text.
+- Output one SQL statement only. Do not include any extra text, tags, or code fences."""
+sampling = SamplingParams(n=30, temperature=1.0, max_tokens=2048)
+messages = [
+    {"role": "system", "content": system_prompt},
+    {"role": "user", "content": f"Database Schema:\n{schema}\n\nQuestion: {question}"},
+]
+output = llm.chat(messages, sampling)
+candidate_sqls = [c.text.split("</think>")[-1].strip() for c in output[0].outputs]
+# Apply majority voting (vav) — see GitHub repo
+```
+### Recommended evaluation pipeline
+1. Generate n=30 candidates with temperature=1.0
+2. Execute each candidate; group results
+3. Pick from the largest non-empty success group (value-aware voting, "vav")
+4. Score with the official Spider evaluator (`test_suite_sql_eval`)
+This pipeline gives **75.0% Spider Dev EX** (75.44% MV).
+---
+## Detailed Spider Dev results (n=30, vav)
+| Hardness | Count | Execution Accuracy |
+|----------|-------|--------------------|
+| Easy | 248 | 91.9% |
+| Medium | 446 | 82.5% |
+| Hard | 174 | 62.6% |
+| Extra Hard | 166 | 42.8% |
+| **All** | **1034** | **75.0%** |
+Recall@30: **85.11%** (any-correct rate among 30 candidates).
+---
+## Training
+| Parameter | Value |
+|-----------|-------|
+| Base model | `griffith-bigdata/Qwen-2.5-Coder-0.5B-SQL-Writer` |
+| Algorithm | GRPO |
+| Train data | Spider train (8,659 samples) |
+| Total steps | 2000 (this checkpoint = 2000) |
+| Learning rate | 8e-6 |
+| Num generations per prompt | 32 |
+| Gradient accumulation | 32 |
+| Max completion length | 2048 |
+| Max prompt length | 1500 |
+| Temperature (rollout) | 1.0 |
+| Selection during eval | vav (value-aware voting) |
+| Rewards | Execution + Atomic + Memory + Format |
+---
+## License
+Inherits the base model's license (Apache 2.0).
+---
+## Citation
+```bibtex
+@article{finer-sql-2026,
+  title  = {FINER-SQL: Fine-grained reasoning rewards for small Text-to-SQL models},
+  author = {Thanh Dat and others},
+  year   = {2026},
+}
+```

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chat_template.jinja ADDED Viewed

	@@ -0,0 +1,4 @@

+{% for message in messages %}{{'<|im_start|>' + message['role'] + '
+' + message['content'] + '<|im_end|>' + '
+'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant
+' }}{% endif %}

config.json ADDED Viewed

	@@ -0,0 +1,54 @@

+{
+  "architectures": [
+    "Qwen2ForCausalLM"
+  ],
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+  "dtype": "bfloat16",
+  "eos_token_id": 151645,
+  "hidden_act": "silu",
+  "hidden_size": 896,
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+  "max_window_layers": 24,
+  "model_type": "qwen2",
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+  "rope_theta": 1000000.0,
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+  "tie_word_embeddings": true,
+  "transformers_version": "4.57.0",
+  "use_cache": false,
+  "use_sliding_window": false,
+  "vocab_size": 151936
+}

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+  "top_p": 0.8,
+  "transformers_version": "4.57.0"
+}

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