laion/nemotron-terminal-security__Qwen3-8B
The laion/nemotron-terminal-security__Qwen3-8B model is a fine-tuned 8 billion parameter language model based on the Qwen3-8B architecture, developed by laion. This model has been specifically fine-tuned on a dataset related to terminal security, suggesting an optimization for tasks within this domain. It leverages a 32768 token context length, making it suitable for processing extensive security-related logs or documentation. Its primary application is likely in analyzing, generating, or understanding text pertinent to terminal security operations.
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Model Overview
This model, laion/nemotron-terminal-security__Qwen3-8B, is an 8 billion parameter language model derived from the Qwen3-8B architecture. It has undergone specific fine-tuning by laion using a dataset focused on terminal security (/e/data1/datasets/playground/ot/hf_hub/datasets--laion--nemotron-terminal-security).
Key Training Details
The fine-tuning process involved several specific hyperparameters:
- Learning Rate: 4e-05
- Batch Sizes: A
train_batch_sizeof 1 andeval_batch_sizeof 8, with atotal_train_batch_sizeof 96 across 32 devices. - Optimizer: ADAMW_TORCH_FUSED with betas=(0.9,0.98) and epsilon=1e-08.
- Scheduler: Cosine learning rate scheduler with a warmup ratio of 0.1.
- Epochs: Trained for 7.0 epochs.
Potential Use Cases
Given its fine-tuning on a terminal security dataset, this model is likely optimized for tasks such as:
- Analyzing security logs and events.
- Generating reports or summaries related to terminal security incidents.
- Assisting in understanding security protocols or vulnerabilities within terminal environments.
Further details on specific intended uses, limitations, and evaluation data are not provided in the current model card.