Instructions to use adiitya0929/SmolLM2-135M-WikiSQL-baseline with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use adiitya0929/SmolLM2-135M-WikiSQL-baseline with Transformers:

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

pipe = pipeline("text-generation", model="adiitya0929/SmolLM2-135M-WikiSQL-baseline")

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

tokenizer = AutoTokenizer.from_pretrained("adiitya0929/SmolLM2-135M-WikiSQL-baseline")
model = AutoModelForCausalLM.from_pretrained("adiitya0929/SmolLM2-135M-WikiSQL-baseline", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use adiitya0929/SmolLM2-135M-WikiSQL-baseline with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "adiitya0929/SmolLM2-135M-WikiSQL-baseline"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "adiitya0929/SmolLM2-135M-WikiSQL-baseline",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/adiitya0929/SmolLM2-135M-WikiSQL-baseline

SGLang

How to use adiitya0929/SmolLM2-135M-WikiSQL-baseline 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 "adiitya0929/SmolLM2-135M-WikiSQL-baseline" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "adiitya0929/SmolLM2-135M-WikiSQL-baseline",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "adiitya0929/SmolLM2-135M-WikiSQL-baseline" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "adiitya0929/SmolLM2-135M-WikiSQL-baseline",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use adiitya0929/SmolLM2-135M-WikiSQL-baseline with Docker Model Runner:
```
docker model run hf.co/adiitya0929/SmolLM2-135M-WikiSQL-baseline
```

SmolLM2-135M-WikiSQL-baseline

SmolLM2-135M (by Hugging Face) fine-tuned on WikiSQL with exactly the recipe used for TinySQL-125M, as a baseline in the TinySQL paper. It answers the question "what does the same fine-tuning give on a model pretrained on about 2T tokens instead of 4.5B?". For the from-scratch model, see adiitya0929/TinySQL-125M-WikiSQL.

Paper: TinySQL: A 125M-Parameter Text-to-SQL Model Built and Pretrained From Scratch on 4.5B Tokens (Zenodo, 2026)
Code: github.com/aditya0929/tinysql (eval/baselines/finetune_hf.py)

Quick start

from transformers import AutoModelForCausalLM, AutoTokenizer

repo = "adiitya0929/SmolLM2-135M-WikiSQL-baseline"
tok = AutoTokenizer.from_pretrained(repo)
model = AutoModelForCausalLM.from_pretrained(repo)

prompt = ('<schema>CREATE TABLE t ("Player" TEXT, "Position" TEXT, "School/Club Team" TEXT);</schema>\n'
          '<question>What position does the player who played for butler cc (ks) play?</question>\n<sql>')
ids = tok(prompt, return_tensors="pt", add_special_tokens=False).input_ids
out = model.generate(ids, max_new_tokens=64, do_sample=False, stop_strings=["</sql>"], tokenizer=tok)
print(tok.decode(out[0, ids.shape[1]:], skip_special_tokens=True).split("</sql>")[0])
# SELECT "Position" FROM t WHERE "School/Club Team" = 'butler cc (ks)';

The prompt format is the same as TinySQL's. Here <schema>, <question> and <sql> are plain text, not special tokens.

Training

WikiSQL train (56,355 examples), loss on the SQL only, batch 32, AdamW (the repository's own), peak LR 1e-4 with warmup and cosine decay, 3 epochs, seed 0. The best epoch was picked by execution accuracy on the first 1,000 dev questions: 76.3 / 78.3 / 77.1%, so epoch 2.

Results (WikiSQL dev, 8,421 questions)

Decoding	Execution accuracy	Exact match
Greedy	82.72%	74.79%
Schema constraints	83.46%	75.05%
Schema + values constraints	85.19%	76.62%

The constrained decoder for Hugging Face models is eval/baselines/constrained_hf.py in the repository.

This is the dev-phase run. The paper's test-set numbers for SmolLM2-135M (82.57 ± 0.37% greedy, 85.22 ± 0.21% constrained) come from three later runs with the same recipe and LR (seeds 0, 1 and 2), whose weights were not kept.

Citation

@techreport{jha2026tinysql,
  title       = {{TinySQL}: A 125M-Parameter Text-to-SQL Model Built and Pretrained From Scratch on 4.5B Tokens},
  author      = {Jha, Aditya Narayan},
  year        = {2026},
  institution = {Zenodo},
  doi         = {10.5281/zenodo.23396770},
  url         = {https://doi.org/10.5281/zenodo.23396770}
}

SmolLM2 itself: Ben Allal et al., SmolLM2: When Smol Goes Big -- Data-Centric Training of a Small Language Model, 2025.