Overview

waleko/Qwen3-8B-SFT-envbench_gpt5-yellow-green is an 8 billion parameter language model derived from the Qwen3-8B base model. It has undergone supervised fine-tuning (SFT) using the envbench_gpt5-yellow-green dataset, indicating a specialization for tasks related to this specific data distribution.

Key Capabilities

• Specialized Performance: Achieved an accuracy of 0.8569 and a loss of 0.4850 on its evaluation set, suggesting proficiency in tasks represented by the envbench_gpt5-yellow-green dataset.
• Fine-tuned Architecture: Built upon the robust Qwen3-8B architecture, benefiting from its foundational capabilities.

Good for

• Domain-Specific Applications: Ideal for use cases that align closely with the data and tasks present in the envbench_gpt5-yellow-green dataset.
• Research and Experimentation: Suitable for researchers exploring the impact of specific dataset fine-tuning on general-purpose LLMs.

Training Details

The model was trained with a learning rate of 5e-05, a total batch size of 16 (achieved with gradient accumulation steps of 4 on 4 GPUs), and a cosine learning rate scheduler with a 0.1 warmup ratio over 5 epochs.