nandansarkar/qwen3_0-6B_adversarial_5
nandansarkar/qwen3_0-6B_adversarial_5 is a 0.8 billion parameter language model, fine-tuned from a Qwen3_0-6B_adversarial_4 base model. This model has a context length of 40960 tokens and was specifically trained on the adversarial_dataset_5, suggesting an optimization for handling or generating adversarial content. Its primary application is likely in research or development contexts requiring models with specific adversarial training characteristics.
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Model Overview
This model, nandansarkar/qwen3_0-6B_adversarial_5, is a fine-tuned variant of the qwen3_0-6B_adversarial_4 base model. With 0.8 billion parameters and a substantial context length of 40960 tokens, it is designed for specific applications. The model's training involved a single epoch on the adversarial_dataset_5 dataset, indicating a focus on adversarial learning or content generation.
Training Details
The fine-tuning process utilized a learning rate of 1e-05, a train_batch_size of 2, and a gradient_accumulation_steps of 8, resulting in a total_train_batch_size of 32. The optimizer used was adamw_torch with default betas and epsilon, and a cosine learning rate scheduler with a warmup ratio of 0.05. The training was conducted across 2 devices in a multi-GPU setup.
Key Characteristics
- Base Model: Fine-tuned from
qwen3_0-6B_adversarial_4. - Parameter Count: 0.8 billion parameters.
- Context Length: Supports a context of 40960 tokens.
- Training Focus: Specifically fine-tuned on
adversarial_dataset_5, suggesting specialized capabilities related to adversarial data.
Potential Use Cases
This model is likely suitable for research and development scenarios that require a language model with specific training on adversarial datasets. Its large context window could be beneficial for tasks requiring extensive input or output related to adversarial content.