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XINLI1997
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Model Details
Model Provider
XINLI1997
Model Tree
Qwen/Qwen3.5-4B
Input Modalities
Output Modalities
Supported Functionality
| Role | Student start (supervised fine-tuning) |
| What it is | Student fine-tuned (supervised) on clean Fashion targets; the starting point of on-policy distillation. |
| Paper reference | Table 1 (Qwen3.5 4B / Fashion: student start) |
| Task | Amazon Fashion review ranking: given a product title and 8 of its reviews, score each review's helpfulness for a buyer (0-10) and return a strict JSON list of 8 {"review_id", "score"} objects in prompt order |
| Training data | train.jsonl of XINLI1997/ErrOPD-Fashion (1,795 lists, clean targets) |
| Base model | Qwen/Qwen3.5-4B |
| Weights | bf16 safetensors, 1 file, 9.08 GB (4.54 B stored parameters, including the unused vision encoder). |
python
# pip install "transformers>=5.3.0" accelerate datasets torchimport torchfrom datasets import load_datasetfrom transformers import AutoModelForCausalLM, AutoTokenizer repo = "XINLI1997/ErrOPD-Qwen3.5-4B-student-Fashion"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-Fashion", 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 a JSON list of 8 objects {"review_id": ..., "score": ...} in the order of the reviews in the prompt. Prompts, decoding settings and the scoring code used in the paper are in the code repository.
Notes:
transformers>=5.3.0. They keep the Qwen3.5 multimodal layout (Qwen3_5ForConditionalGeneration); AutoModelForCausalLM loads the language model only, which is all these text tasks use (vLLM loads the folder directly).enable_thinking have no effect.Student fine-tuned (supervised) on clean Fashion targets; the starting point of on-policy distillation. Full training configurations and scripts are in the code repository.
This checkpoint is reported in the paper, Table 1 (Qwen3.5 4B / Fashion: student start); see that table for its scores.
Apache-2.0, inherited from the base model Qwen/Qwen3.5-4B (see LICENSE). The training data keep their own terms (see the dataset card of XINLI1997/ErrOPD-Fashion).
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>=5.3.0" accelerate datasets torchimport torchfrom datasets import load_datasetfrom transformers import AutoModelForCausalLM, AutoTokenizer repo = "XINLI1997/ErrOPD-Qwen3.5-4B-student-Fashion"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-Fashion", 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}}