nandansarkar/qwen3_0-6B_adversarial_2
The nandansarkar/qwen3_0-6B_adversarial_2 model is a 0.8 billion parameter language model, fine-tuned from a previous adversarial version. It is based on the Qwen3.0 architecture and has a context length of 40960 tokens. This model is specifically trained on an adversarial dataset, suggesting its potential for robustness testing or generating challenging inputs.
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Model Overview
The nandansarkar/qwen3_0-6B_adversarial_2 is a 0.8 billion parameter language model, fine-tuned from an earlier adversarial checkpoint. It leverages the Qwen3.0 architecture and supports a substantial context length of 40960 tokens. The model's training involved a specific adversarial_dataset_2, indicating its development for tasks related to adversarial scenarios.
Training Details
The model was trained using the following key hyperparameters:
- Learning Rate: 1e-05
- Batch Size: A
train_batch_sizeof 2 withgradient_accumulation_stepsof 8 resulted in atotal_train_batch_sizeof 32. - Optimizer: AdamW with betas=(0.9, 0.95) and epsilon=1e-08.
- Scheduler: Cosine learning rate scheduler with a 0.05 warmup ratio.
- Epochs: Trained for 1 epoch.
Potential Use Cases
Given its fine-tuning on an adversarial dataset, this model is likely intended for:
- Adversarial Robustness Testing: Evaluating the resilience of other language models against adversarial attacks.
- Adversarial Example Generation: Creating challenging inputs to probe model weaknesses or biases.
- Security Research: Investigating vulnerabilities in AI systems.