Overview

DCAgent/b1_top4 is an 8 billion parameter language model, fine-tuned from the base Qwen3-8B architecture. It was trained using a specific dataset located at /e/scratch/jureap59/raoof1/sft_data/hf_hub/datasets--DCAgent--b1_top4/snapshots/db1eb508ebd868241a13f4083e7939710048d63c_thinking_preprocessed.

Training Details

The model underwent 7 epochs of training with a learning rate of 4e-05, using an AdamW optimizer with betas=(0.9, 0.98) and epsilon=1e-08. Training was distributed across 16 devices with a total batch size of 16. A cosine learning rate scheduler with a 0.1 warmup ratio was employed. The training utilized Transformers 4.57.6, Pytorch 2.9.1+cu130, Datasets 4.7.0, and Tokenizers 0.22.2.

Potential Use Cases

Given its fine-tuning on a specialized dataset, DCAgent/b1_top4 is likely best suited for tasks that align with the characteristics and content of the /e/scratch/jureap59/raoof1/sft_data/hf_hub/datasets--DCAgent--b1_top4/snapshots/db1eb508ebd868241a13f4083e7939710048d63c_thinking_preprocessed dataset. Developers should evaluate its performance on their specific applications, particularly those requiring nuanced understanding or generation based on the fine-tuning domain.