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Anurich
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Model Details
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Anurich
Model Tree
Qwen/Qwen2.5-Coder-1.5B-Instruct
Input Modalities
Output Modalities
Supported Functionality
| Benchmark | EX | Notes |
|---|---|---|
| Un-tuned base | 17.4% | Qwen2.5-Coder-1.5B-Instruct baseline (BIRD dev) |
| BIRD dev | 36.9% | the hard benchmark (+19.5 over base) |
| Spider dev | 82.0% | generalization to unseen databases |
The high Spider score (on databases the model never trained on) confirms the model learned SQL generally rather than specializing to one benchmark — the point of the two-stage recipe. For reference, this Spider score is competitive with SLM-SQL-1.5B (~76.7%, which additionally used reinforcement learning).
Trained for ~$25–30 of rented GPU. This is a budget/reproducibility result, not frontier SOTA — see the full analysis in the project repo.
Key settings: LoRA rank 32 / alpha 64, all-linear target modules, LR 2e-4 (stage 1) / 5e-5 (stage 2), seq 5120, bf16, assistant-only loss, execution-filtered data, CoT-then-SQL targets, best-checkpoint selection on downstream EX.
python
from transformers import AutoModelForCausalLM, AutoTokenizerfrom peft import PeftModelbase = "Qwen/Qwen2.5-Coder-1.5B-Instruct"tok = AutoTokenizer.from_pretrained(base)model = AutoModelForCausalLM.from_pretrained(base, device_map="auto")model = PeftModel.from_pretrained(model, "Anurich/slmsql-qwen2.5-coder-1.5b")messages = [{"role": "system", "content": "You are an expert data analyst who translates ""questions into correct SQLite queries. Think step by step, then output the ""final query in a ```sql block. Wrap identifiers with spaces in backticks."},{"role": "user", "content": "Given the following database schema:\n\n""CREATE TABLE employees (id INTEGER, name TEXT, salary INTEGER);\n\n""Question: Who are the 3 highest-paid employees?\n\n""Write a single SQLite query that answers the question."},]prompt = tok.apply_chat_template(messages, add_generation_prompt=True, tokenize=False)out = model.generate(**tok(prompt, return_tensors="pt").to(model.device), max_new_tokens=512)print(tok.decode(out[0], skip_special_tokens=True))
See the repo's prompt.py for the exact training prompt format (schema +
optional evidence + optional matched DB values → reasoning → SQL).
Execution Accuracy: run the predicted SQL and the gold SQL against the real SQLite database, compare result sets (order-insensitive). BIRD dev on a stratified 149-example slice; Spider dev on 200 examples with real-data DBs. Standard single-model, single-inference greedy decoding.