Model Overview

The nandansarkar/qwen3_0-6B_adversarial_3 model is a 0.8 billion parameter language model, representing a fine-tuned iteration of a prior adversarial version of Qwen3.0-6B. This model has undergone specific training on an adversarial_dataset_3, indicating an optimization for handling or generating adversarial content.

Key Training Details

• Base Model: Fine-tuned from /home/nsarkar/orcd/pool/GPTeacher/model_checkpoints/qwen3_0-6B_adversarial_2.
• Dataset: Trained on adversarial_dataset_3.
• Hyperparameters:
  • Learning Rate: 1e-05
  • Optimizer: adamw_torch
  • Epochs: 1
  • Gradient Accumulation Steps: 8
• Frameworks: Utilizes Transformers 4.52.4, Pytorch 2.7.0+cu126, Datasets 3.6.0, and Tokenizers 0.21.1.

Potential Use Cases

Given its adversarial training, this model could be particularly useful for:

• Adversarial Robustness Research: Investigating model behavior and resilience against adversarial attacks.
• Security Applications: Developing systems that need to detect or withstand adversarial inputs.
• Challenging Text Generation: Creating content designed to test other models or systems.