Overview

DCAgent/a1-nemotron_junit is an 8 billion parameter language model derived from the Qwen3-8B architecture. It has undergone specific fine-tuning on a unique dataset located at /e/scratch/jureap59/raoof1/sft_data/hf_hub/datasets--DCAgent--exp_rpt_nemotron-junit_10k_glm_4.7_traces_jupiter/snapshots/7cb33ae94823323131a19fbaf141940115ec5af2_thinking_preprocessed.

Key Characteristics

• Base Model: Fine-tuned from Qwen/Qwen3-8B.
• Parameter Count: 8 billion parameters.
• Context Length: Supports a context length of 32768 tokens.
• Training Data: Specialized on a dataset containing exp_rpt_nemotron-junit_10k_glm_4.7_traces_jupiter.

Training Details

The model was trained using the following key hyperparameters:

• Learning Rate: 4e-05
• Optimizer: ADAMW_TORCH_FUSED
• Epochs: 7.0
• Batch Size: A total training batch size of 16 across 16 devices.

Intended Use Cases

This model is specifically designed for applications that require processing, analysis, or generation based on the unique data patterns present in the exp_rpt_nemotron-junit_10k_glm_4.7_traces_jupiter dataset. Its fine-tuning makes it particularly suited for tasks related to this specific data domain, rather than general-purpose language understanding or generation.