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Model: blackhao0426/pref-extractor-qwen3-0.6b-full-sft
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2026-04-23 23:35:05 +08:00
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---
base_model: Qwen/Qwen3-0.6B
datasets:
- blackhao0426/user-preference-564k
language:
- en
license: apache-2.0
pipeline_tag: text-generation
library_name: transformers
tags:
- preference-extraction
- personalization
- user-modeling
---
# Preference Extractor (Qwen3-0.6B Full SFT)
[![arXiv](https://img.shields.io/badge/arXiv-2603.20939-b31b1b.svg)](https://arxiv.org/abs/2603.20939)
[![Code](https://img.shields.io/badge/GitHub-VARS-black?logo=github)](https://github.com/YurenHao0426/VARS)
[![Dataset](https://img.shields.io/badge/🤗_Dataset-user--preference--564k-blue)](https://huggingface.co/datasets/blackhao0426/user-preference-564k)
A lightweight preference extraction model finetuned from [Qwen3-0.6B](https://huggingface.co/Qwen/Qwen3-0.6B). Given a dialogue window, it outputs structured JSON preference tuples describing user style preferences.
This model was introduced in the paper [User Preference Modeling for Conversational LLM Agents: Weak Rewards from Retrieval-Augmented Interaction](https://arxiv.org/abs/2603.20939). It is a core component of **VARS** (Vector-Adapted Retrieval Scoring), a framework for personalized LLM assistants.
## Task
Given recent conversation turns between a user and an AI assistant, extract user preferences as structured condition-action rules in JSON format:
```json
{
"preferences": [
{"condition": "when explaining math", "action": "show step-by-step derivation", "confidence": 0.9},
{"condition": "general", "action": "respond in Chinese", "confidence": 0.85}
]
}
```
Each preference is a `(condition, action, confidence)` tuple:
- **condition**: when this preference applies (e.g., "when writing code", "general", "when debugging")
- **action**: the preferred behavior (e.g., "use Python with type hints", "show step-by-step derivation")
- **confidence**: extraction confidence score (0.01.0)
## Performance
Evaluated on a held-out test set:
| Metric | Value |
|--------|-------|
| JSON validity | 99.7% |
| Recall | 97.5% |
| Precision | 37.7% |
| Eval loss | 0.1611 |
The model intentionally favors **high recall** over precision: it over-extracts candidate preferences, and the downstream reranker and user vector in the VARS pipeline filter irrelevant ones.
## Usage
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "blackhao0426/pref-extractor-qwen3-0.6b-full-sft"
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype="bfloat16", device_map="auto", trust_remote_code=True)
# system_prompt should describe the extraction task
system_prompt = "You are a preference extractor. Given the dialogue window, extract preferences into JSON format."
conversation = """User: Can you explain the quicksort algorithm? I prefer step-by-step breakdowns with Python code.
Assistant: Sure! Here's quicksort step by step...
User: Great, but can you add type hints to the code?"""
messages = [
{"role": "system", "content": system_prompt},
{"role": "user", "content": conversation},
]
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
with torch.no_grad():
outputs = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode only the newly generated tokens
result = tokenizer.decode(outputs[0][inputs.input_ids.shape[-1]:], skip_special_tokens=True)
# Parse JSON from result to get preference list
```
## Training
| Parameter | Value |
|-----------|-------|
| Base model | Qwen/Qwen3-0.6B |
| Training data | [blackhao0426/user-preference-564k](https://huggingface.co/datasets/blackhao0426/user-preference-564k) (564K examples) |
| Learning rate | 2e-05 |
| Batch size | 128 (32 per device x 4 GPUs) |
| Epochs | 1 |
| LR scheduler | Cosine with 5% warmup |
| Optimizer | AdamW (fused) |
| Framework | LLaMA-Factory |
## Citation
```bibtex
@misc{hao2026userpreferencemodelingconversational,
title={User Preference Modeling for Conversational LLM Agents: Weak Rewards from Retrieval-Augmented Interaction},
author={Yuren Hao and Shuhaib Mehri and ChengXiang Zhai and Dilek Hakkani-Tür},
year={2026},
eprint={2603.20939},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2603.20939},
}
```

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{%- if tools %}
{{- '<|im_start|>system\n' }}
{%- if messages[0].role == 'system' %}
{{- messages[0].content + '\n\n' }}
{%- endif %}
{{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
{%- for tool in tools %}
{{- "\n" }}
{{- tool | tojson }}
{%- endfor %}
{{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
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{{- '<|im_start|>' + message.role + '\n' + content }}
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{{- '<|im_start|>' + message.role + '\n' + content }}
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{%- if tool_call.arguments is string %}
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{%- if add_generation_prompt %}
{{- '<|im_start|>assistant\n' }}
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{{- '<think>\n\n</think>\n\n' }}
{%- endif %}
{%- endif %}

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