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XINLI1997
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Model Details
Model Provider
XINLI1997
Model Tree
Qwen/Qwen3-1.7B
Input Modalities
Output Modalities
Supported Functionality
| Role | Student start that already makes the error (supervised fine-tuning) |
| What it is | Student fine-tuned (supervised) on Spider targets in which 3% of the training copies carry trailing text after the SQL query; the start of the arm whose student already makes the error. |
| Paper reference | Table 2 (SQL: student that starts with the error) |
| Task | Spider 1.0 text-to-SQL: given a SQLite schema and a question, output one SQL query on a single line and nothing else |
| Training data | sft_train.jsonl of XINLI1997/ErrOPD-Spider; trailing text is injected into 3% of the training copies at fine-tuning time |
| Base model | Qwen/Qwen3-1.7B |
| Weights | bf16 safetensors, 1 file, 3.44 GB (1.72 B stored parameters). |
python
# pip install "transformers>=4.57.0" accelerate datasets torchimport torchfrom datasets import load_datasetfrom transformers import AutoModelForCausalLM, AutoTokenizer repo = "XINLI1997/ErrOPD-Qwen3-1.7B-student-SQL-start3pct"tokenizer = AutoTokenizer.from_pretrained(repo)model = AutoModelForCausalLM.from_pretrained(repo, dtype=torch.bfloat16, device_map="auto") # one held-out prompt (system + user turns) from the evaluation splitexample = load_dataset("XINLI1997/ErrOPD-Spider", split="validation")[0]messages = [m for m in example["messages"] if m["role"] != "assistant"] inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt", return_dict=True).to(model.device)output = model.generate(**inputs, max_new_tokens=512, do_sample=False)print(tokenizer.decode(output[0, inputs["input_ids"].shape[1]:], skip_special_tokens=True))The expected output is one SQLite query on a single line (run it against the Spider database named in metadata.db_id to check it). Prompts, decoding settings and the scoring code used in the paper are in the code repository.
Notes:
Student fine-tuned (supervised) on Spider targets in which 3% of the training copies carry trailing text after the SQL query; the start of the arm whose student already makes the error. Full training configurations and scripts are in the code repository.
This checkpoint is reported in the paper, Table 2 (SQL: student that starts with the error); see that table for its scores.
Apache-2.0, inherited from the base model Qwen/Qwen3-1.7B (see LICENSE). The training data keep their own terms (see the dataset card of XINLI1997/ErrOPD-Spider).
bibtex
@article{li2026erropd, title = {ErrOPD: When Does On-Policy Distillation Transfer Teacher Errors?}, author = {Li, Xin and Jiang, Hao and Wang, Annan and Zhang, Yichi and Yuen, Chau}, journal = {arXiv preprint arXiv:2605.08737}, year = {2026}}# pip install "transformers>=4.57.0" accelerate datasets torchimport torchfrom datasets import load_datasetfrom transformers import AutoModelForCausalLM, AutoTokenizer repo = "XINLI1997/ErrOPD-Qwen3-1.7B-student-SQL-start3pct"tokenizer = AutoTokenizer.from_pretrained(repo)model = AutoModelForCausalLM.from_pretrained(repo, dtype=torch.bfloat16, device_map="auto") # one held-out prompt (system + user turns) from the evaluation splitexample = load_dataset("XINLI1997/ErrOPD-Spider", split="validation")[0]messages = [m for m in example["messages"] if m["role"] != "assistant"] inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt", return_dict=True).to(model.device)output = model.generate(**inputs, max_new_tokens=512, do_sample=False)print(tokenizer.decode(output[0, inputs["input_ids"].shape[1]:], skip_special_tokens=True))@article{li2026erropd, title = {ErrOPD: When Does On-Policy Distillation Transfer Teacher Errors?}, author = {Li, Xin and Jiang, Hao and Wang, Annan and Zhang, Yichi and Yuen, Chau}, journal = {arXiv preprint arXiv:2605.08737}, year = {2026}}