nandansarkar/qwen3_0-6B_adversarial_3
The nandansarkar/qwen3_0-6B_adversarial_3 model is a 0.8 billion parameter language model, fine-tuned from a previous adversarial version of Qwen3.0-6B. This model is specifically trained on an adversarial dataset, suggesting a focus on robustness or handling challenging inputs. Its primary application is likely in scenarios requiring a model with enhanced resilience to adversarial examples or for research into adversarial training techniques.
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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
- Learning Rate:
- 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.