nandansarkar/qwen3_0-6B_adversarial_7
nandansarkar/qwen3_0-6B_adversarial_7 is a fine-tuned 0.8 billion parameter Qwen3.0 model, building upon a previous adversarial version. This model was trained on the adversarial_dataset_7 dataset, suggesting a specialization in handling or generating adversarial content. With a notable context length of 40960 tokens, it is likely optimized for tasks requiring extensive contextual understanding or generation in adversarial scenarios.
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Model Overview
nandansarkar/qwen3_0-6B_adversarial_7 is a fine-tuned iteration of a Qwen3.0-6B model, specifically developed by nandansarkar. This version is a direct successor to qwen3_0-6B_adversarial_6, indicating an ongoing development focused on adversarial training. The model has 0.8 billion parameters and supports a substantial context length of 40960 tokens, making it suitable for processing or generating long sequences of text.
Training Details
The model was fine-tuned on the adversarial_dataset_7 dataset. Key training hyperparameters include a learning rate of 1e-05, a train_batch_size of 2, and gradient_accumulation_steps of 8, resulting in a total_train_batch_size of 32. It utilized a cosine learning rate scheduler with a warmup ratio of 0.05 over 1 epoch. The training was conducted using a multi-GPU setup with 2 devices.
Potential Use Cases
Given its adversarial training, this model may be particularly suited for:
- Adversarial Text Generation: Creating text designed to challenge or test other language models.
- Robustness Testing: Evaluating the resilience of AI systems against adversarial inputs.
- Security Applications: Analyzing or generating content for cybersecurity-related tasks where understanding adversarial patterns is crucial.