huseyinatahaninan/appworld_distillation_sft_v2-SFT-Qwen3-8B

Model Overview

This model, appworld_distillation_sft_v2-SFT-Qwen3-8B, is an 8 billion parameter language model derived from the Qwen3-8B architecture. It has undergone supervised fine-tuning (SFT) using the appworld_distillation_sft_v2 dataset, suggesting a focus on tasks related to application world distillation.

Key Training Details

The model was trained with a learning rate of 5e-06 over 25 epochs, utilizing an AdamW optimizer and a cosine learning rate scheduler with a 0.1 warmup ratio. Training was conducted on 8 devices with a total batch size of 32, accumulating gradients over 4 steps. The training process resulted in a final validation loss of 0.6342.

Potential Use Cases

Given its fine-tuning on the appworld_distillation_sft_v2 dataset, this model is likely optimized for:

• Tasks involving the distillation of information within an "appworld" context.
• Applications requiring specialized understanding or generation based on the specific data it was trained on.

Limitations

The model card indicates that more information is needed regarding its specific intended uses, limitations, and the detailed nature of the training and evaluation data. Users should exercise caution and conduct further evaluation to determine its suitability for specific applications beyond its apparent specialization.