Overview

Rakancorle1/policyguard-4B-SS is a 4 billion parameter language model, fine-tuned from the Qwen3-4B-Instruct-2507 base model. This specialization was achieved through training on the policyguardbench-sstrain dataset, suggesting its primary utility lies in applications related to policy analysis, generation, or adherence.

Key Training Details

The model was trained with a learning rate of 2e-05 over 3 epochs, utilizing a multi-GPU setup with 4 devices. A total training batch size of 64 and an evaluation batch size of 32 were used, with gradient accumulation steps set to 4. The optimizer employed was ADAMW_TORCH_FUSED, and a cosine learning rate scheduler was used with 0.03 warmup steps. The training leveraged Transformers 5.2.0, Pytorch 2.11.0+cu130, Datasets 4.0.0, and Tokenizers 0.22.2.

Potential Use Cases

Given its fine-tuning on a policy-specific dataset, policyguard-4B-SS is likely well-suited for tasks such as:

• Policy Compliance Checking: Analyzing text for adherence to predefined policies.
• Policy Generation: Assisting in drafting or refining policy documents.
• Content Moderation: Identifying content that violates specific guidelines or policies.
• Legal and Regulatory Analysis: Processing and understanding legal or regulatory texts.

Limitations

The model card indicates that more information is needed regarding its specific intended uses, limitations, and detailed training/evaluation data. Users should exercise caution and conduct thorough testing for critical applications.