Overview

DCAgent/a1-nemotron_rspec is an 8 billion parameter language model, fine-tuned from the Qwen/Qwen3-8B base model. It was trained using a specific dataset, /e/scratch/jureap59/raoof1/sft_data/hf_hub/datasets--DCAgent--exp_rpt_nemotron-ruby_10k_glm_4.7_traces_jupiter, indicating a specialized application focus rather than general-purpose use.

Training Details

The model underwent 7 epochs of training with a learning rate of 4e-05 and a total batch size of 16 across 16 GPUs. 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.

Potential Use Cases

Given its fine-tuning on a specific dataset, this model is likely best suited for tasks directly related to the content and structure of the exp_rpt_nemotron-ruby_10k_glm_4.7_traces_jupiter dataset. Developers should evaluate its performance on tasks that align with this specialized training data.