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mayiwen/
PaperAudit_Qwen3_14B_sft_rl

Name: PaperAudit_Qwen3_14B_sft_rl
Availability: InStock

safetensors

qwen3

mayiwen/PaperAudit_Dataset

Qwen/Qwen3-14B

finetune

apache-2.0

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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

python

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

python

# 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.

mayiwen/PaperAudit_Qwen3_14B_sft_rl