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XINLI1997
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Model Details
Model Provider
XINLI1997
Model Tree
Qwen/Qwen3.5-2B-Base
Input Modalities
Output Modalities
Supported Functionality
| Method | SFT initialisation on Reddit-Music |
| Data | Reddit-Music from XINLI1997/ReMaRk-data: requests and responders from r/musicsuggestions, item mentions extracted by GPT-5.6 and grounded to MusicBrainz artists; SFT targets are GPT-5.6 recommendation lists grounded to the same catalog |
| Base model | Qwen/Qwen3.5-2B-Base |
| Initialised from | Qwen/Qwen3.5-2B-Base |
| Selected step | 1500 (SFT steps) |
| Paper reference | Table 2 / Table 17 (Music SFT, 2B) |
| Weights | bf16 safetensors, 1 file, 5.44 GB (2.72 B stored parameters, including the unused vision encoder). The file also stores lm_head.weight, a bit-identical copy of the tied input embeddings. |
python
# pip install "transformers>=5.3.0" accelerate torchimport torchfrom transformers import AutoModelForCausalLM, AutoTokenizer repo = "XINLI1997/ReMaRk-Qwen3.5-2B-RedditMusic-SFT"tokenizer = AutoTokenizer.from_pretrained(repo)model = AutoModelForCausalLM.from_pretrained(repo, dtype=torch.bfloat16, device_map="auto") messages = [{"role": "user", "content": "I've been listening to Radiohead, Portishead and Massive Attack a lot. Which artists should I try next? List 20."}]inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, enable_thinking=False, 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 model answers with a recommendation list, one title (year) (or name (year) for music) per line. The exact instruction header
wrapped around each Reddit request in training and evaluation, the decoding settings and the evaluation scripts are in the
code repository; the request above is only an illustration.
Notes:
transformers>=5.3.0. They keep the Qwen3.5 multimodal layout (Qwen3_5ForConditionalGeneration); AutoModelForCausalLM loads the language model only (vLLM loads the folder directly; preprocessor_config.json and video_preprocessor_config.json are included for that). When training with TRL, pass the text tokenizer as processing_class.enable_thinking=False).trainer_state.json (training curves of the run) is included; no optimizer state.Supervised fine-tuning of the base model on the SFT targets of Reddit-Music. Full configurations and scripts are in the code repository.
This checkpoint is reported in the paper, Table 2 / Table 17 (Music SFT, 2B); see the paper for its scores.
Apache-2.0, inherited from the base model Qwen/Qwen3.5-2B-Base (see LICENSE). Third-party data keep their own terms (Reddit; UCSD Book Graph: academic use; MusicBrainz: CC0); see the dataset card of XINLI1997/ReMaRk-data.
bibtex
@misc{li2026remark, title = {Many Right Answers, One Training Example: Removing Responder Noise from GRPO for LLM Recommenders}, author = {Li, Xin and Jiang, Hao and Guo, Jinghao and Lin, Weisi and Yuen, Chau}, year = {2026}, note = {Preprint; arXiv link coming soon}}# pip install "transformers>=5.3.0" accelerate torchimport torchfrom transformers import AutoModelForCausalLM, AutoTokenizer repo = "XINLI1997/ReMaRk-Qwen3.5-2B-RedditMusic-SFT"tokenizer = AutoTokenizer.from_pretrained(repo)model = AutoModelForCausalLM.from_pretrained(repo, dtype=torch.bfloat16, device_map="auto") messages = [{"role": "user", "content": "I've been listening to Radiohead, Portishead and Massive Attack a lot. Which artists should I try next? List 20."}]inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, enable_thinking=False, 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))@misc{li2026remark, title = {Many Right Answers, One Training Example: Removing Responder Noise from GRPO for LLM Recommenders}, author = {Li, Xin and Jiang, Hao and Guo, Jinghao and Lin, Weisi and Yuen, Chau}, year = {2026}, note = {Preprint; arXiv link coming soon}}