nandansarkar/qwen3_0-6B_adversarial_1
The nandansarkar/qwen3_0-6B_adversarial_1 model is a 0.8 billion parameter language model, fine-tuned from a base Qwen3.0-6B model. It was specifically trained on an adversarial dataset, suggesting a focus on robustness or performance in challenging scenarios. This model is intended for use cases requiring a compact yet specialized language model with potential adversarial training benefits.
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Model Overview
The nandansarkar/qwen3_0-6B_adversarial_1 is a 0.8 billion parameter language model, fine-tuned from a base Qwen3.0-6B model. Its primary distinction lies in its training methodology, having been fine-tuned on an adversarial dataset. This specialized training suggests an optimization for handling difficult or intentionally misleading inputs, potentially enhancing its robustness and reliability in specific applications.
Key Training Details
- Base Model: Fine-tuned from
/home/nsarkar/orcd/pool/GPTeacher/model_checkpoints/base_qwen3_0-6B_filter. - Dataset: Trained on an
adversarial_dataset. - Hyperparameters:
- Learning Rate:
1e-05 - Optimizer:
adamw_torch - Epochs:
1 - Batch Size:
32(total train)
- 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 suitable for:
- Applications requiring enhanced resilience against adversarial attacks or noisy data.
- Scenarios where model robustness and reliability are critical.
- Research into adversarial training techniques and their impact on language model performance.