Overview

This model, sft_a1_nemotron_bash_withtests__Qwen3-8B, is an 8 billion parameter language model built upon the Qwen/Qwen3-8B architecture. It has been specifically fine-tuned using a specialized dataset, /e/scratch/jureap59/raoof1/sft_data/hf_hub/datasets--DCAgent--exp_rpt_nemotron-bash-withtests_glm_4.7_traces_jupiter/snapshots/715628be9b8527ffb9e5318c14acf0fbd3077e50_thinking_preprocessed, which focuses on bash scripting and testing traces.

Training Details

The model underwent 7 epochs of training with a learning rate of 4e-05 and a total training batch size of 16 across 16 devices. It utilized the AdamW_Torch_Fused optimizer with cosine learning rate scheduling and a warmup ratio of 0.1. The training environment included Transformers 4.57.6, Pytorch 2.9.1+cu130, Datasets 4.7.0, and Tokenizers 0.22.2.

Intended Use

While specific intended uses and limitations are not detailed in the provided README, the fine-tuning on a bash-related dataset suggests its primary application in scenarios involving the generation, analysis, or understanding of bash commands and test cases. Developers seeking a model specialized in these areas may find this model particularly useful.