Overview

Cooolder/SCOPE-CoT-sft-v2 is a 4 billion parameter language model, building upon the Qwen3-4B-Instruct-2507 architecture. It has undergone supervised fine-tuning (SFT) using the scope_sft_cot dataset, specifically designed to improve its Chain-of-Thought (CoT) reasoning capabilities. The training process involved a learning rate of 1e-05 over 1.0 epoch, utilizing an AdamW optimizer with cosine learning rate scheduling.

Key Capabilities

• Enhanced Chain-of-Thought Reasoning: Fine-tuned to generate more structured and logical step-by-step reasoning processes.
• Foundation Model: Based on the Qwen3-4B-Instruct-2507, inheriting its general language understanding and generation abilities.

Training Details

The model was trained with a batch size of 1 and gradient accumulation steps of 8, resulting in an effective total batch size of 8. The training concluded with a validation loss of 0.3770. The training hyperparameters included a learning rate of 1e-05 and a cosine learning rate scheduler with a warmup ratio of 0.1.

Intended Uses

This model is particularly suited for applications requiring explicit reasoning steps, such as:

• Complex question answering
• Logical problem-solving
• Educational tools that explain solutions step-by-step

Limitations

The current model card indicates that more information is needed regarding its specific limitations and broader intended uses, suggesting further evaluation and documentation are required.