from transformers import AutoModelForCausalLM, AutoTokenizer

tok = AutoTokenizer.from_pretrained("Vicen-te/qwen3.5-2b-sql")
model = AutoModelForCausalLM.from_pretrained("Vicen-te/qwen3.5-2b-sql", dtype="auto", device_map="auto")

Usage with vLLM

vllm serve Vicen-te/qwen3.5-2b-sql --max-model-len 4096 --served-model-name sql-ft

Training

Base model: Qwen/Qwen3.5-2B
Method: LoRA (rank=16, α=32) → merged via peft.merge_and_unload()
Dataset: Vicen-te/sql-create-context-mini — 300 train / 200 eval
Recipe: 3 epochs, bf16, effective batch 16, cosine LR 2e-4

Evaluation

Compared against the base model on a held-out 200-example split. See the project repo for the full report (executable accuracy, exact match, BLEU, latency, 4-bit quantization trade-off).

License

Apache 2.0, inherited from the base model.