This repository provides a fine-tuned Qwen3 4B model for AI Taste experiments on social science research articles, with a current focus on Psychology, Multidisciplinary article evaluation.

Model Summary

Base model: Qwen/Qwen3-4B
Architecture: Qwen3ForCausalLM
Format: Hugging Face Transformers + Safetensors
Primary use: ranking or classifying research-article prompts into discrete quality levels

Intended Use

The model is intended for research and internal experimentation on structured article-evaluation prompts, including tasks such as:

research question quality assessment
article-level prompt scoring
social science journal tier benchmarking

It is not intended as a general-purpose factual assistant or as a substitute for expert peer review.

Files

This repository keeps only the files needed to load and run the model:

model weights in safetensors
tokenizer files
model config and generation config

Training-state artifacts such as trainer checkpoints and local run metadata are intentionally excluded.

Example

python

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "K1mG0ng/AI-Taste-Psychology-Multidisciplinary-4B"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)

Validation

Top-1 accuracy: 76.00% (200 samples)
Top-1+2 accuracy: 92.00%
Balanced accuracy: 76.00%
Macro F1: 75.53%

Notes

Subject: PSYCHOLOGY, MULTIDISCIPLINARY
Source checkpoint: /workspace/gongziqin/228/RQ/finetune/social_science_rqcontext_PSYCHOLOGY,_MULTIDISCIPLINARY_4B_/final_model
Output quality depends on prompt format and label schema used at inference time.

K1mG0ng/AI-taste-psychology-multidisciplinary-4B

AI-Taste-Psychology-Multidisciplinary-4B