PaperAudit_Qwen3_14B_sft_rl

PaperAudit Qwen3 14B (SFT + RL)

Model Overview

PaperAudit_Qwen3_14B_sft_rl is a large-scale high-performance model specifically trained for academic paper error detection and automated review tasks. This model is based on Qwen3 14B and has been optimized through Supervised Fine-Tuning (SFT) and Reinforcement Learning from Human Feedback (RLHF), providing the strongest performance for complex academic paper analysis tasks.

Model Information

• Base Model: Qwen3 14B
• Model Parameters: ~14 billion parameters
• Training Method: Supervised Fine-Tuning (SFT) + Reinforcement Learning (RLHF)
• Model Architecture: Qwen3ForCausalLM
• Context Length: 40,960 tokens
• Data Type: bfloat16

Model Features

• Top-Tier Performance: 14B parameter scale, providing optimal performance when handling complex paper analysis tasks
• Specialized Optimization: Specifically optimized for academic paper error detection and review tasks
• Reinforcement Learning: Aligned with human preferences through RLHF to improve review quality and error detection accuracy
• Long Context Support: Supports 40K tokens context length, suitable for processing complete academic papers
• Deep Understanding: Capable of understanding complex academic concepts and writing norms

Training Data

This model is trained on PaperAudit_Dataset. The dataset includes:

• Academic papers downloaded from OpenReview
• Structured paper content (processed via LlamaParse and LLM)
• Synthetic error data for training error detection models
• Human review feedback data

For more details about the dataset, please visit: https://huggingface.co/datasets/mayiwen/PaperAudit_Dataset

Usage

Install Dependencies

pip install transformers torch accelerate

Load Model

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_path = "./qwen3_14b_sft_rl"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

Inference Example

# Prepare input (paper error detection task)
prompt = """Please detect errors in the following academic paper paragraph:

[Paper content...]

Please identify errors and provide correction suggestions."""

# Encode input
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

# Generate response
with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=512,
        temperature=0.7,
        do_sample=True,
        pad_token_id=tokenizer.pad_token_id
    )

# Decode output
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)

Application Scenarios

• Academic paper error detection (highest accuracy)
• Automated paper review (professional-grade)
• Academic writing quality assessment (deep analysis)
• Paper content analysis and feedback generation (detailed feedback)
• Academic review assistant tools (expert-level advice)
• Complex academic concept understanding and analysis

Model Architecture Details

• Hidden Size: 5120
• Intermediate Size: 17408
• Number of Attention Heads: 40
• Number of Key-Value Heads: 8 (Grouped Query Attention)
• Number of Hidden Layers: 40
• Vocabulary Size: 151,936

Performance Advantages

Compared to the 8B and 3B models, the 14B model excels in the following aspects:

• Higher Accuracy: Capable of identifying more subtle and complex academic errors
• Deeper Analysis: Provides more detailed and professional review comments
• Better Understanding: Deeply understands academic writing norms, research methods, and theoretical frameworks
• Stronger Reasoning: Capable of complex logical reasoning and critical analysis
• More Comprehensive Feedback: Not only identifies errors but also provides constructive improvement suggestions

Suitable Scenarios

Most suitable for:

• Paper review for high-quality academic journals
• Quality assessment of theses and dissertations
• Paper review for academic conferences
• Scenarios requiring extremely high review quality

Notes

• This model is specifically optimized for academic paper review tasks and may require further fine-tuning for other domains
• It is recommended to use bfloat16 precision to save memory and improve inference speed
• For long document processing, appropriate context window management strategies are recommended
• Requires at least 28GB GPU memory for inference, multi-GPU inference or quantization techniques are recommended
• Inference speed is relatively slow, suitable for scenarios with high quality requirements and relatively lower speed requirements

System Requirements

• Recommended Memory: At least 28GB GPU memory
• Recommended Configuration: A100 40GB or higher configuration
• Quantization Options: 8-bit or 4-bit quantization can be used to reduce memory requirements

Related Resources

• Training Dataset: PaperAudit_Dataset
• PaperAudit Project: For more details, please refer to the PaperAudit project documentation

License

Please refer to the license terms of the base model Qwen3.