DCAgent/a1-issue_tasks
DCAgent/a1-issue_tasks is an 8 billion parameter language model fine-tuned from Qwen/Qwen3-8B. This model is specifically trained on a dataset derived from `exp_rpt_issue_10k_glm_4.7_traces_jupiter`, indicating a specialization in processing and understanding issue-related tasks or reports. It is optimized for applications requiring detailed analysis or generation based on structured issue data.
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Model Overview
DCAgent/a1-issue_tasks is an 8 billion parameter language model, fine-tuned from the base Qwen/Qwen3-8B architecture. This model has been specifically adapted through supervised fine-tuning (SFT) on a unique dataset: /e/scratch/jureap59/raoof1/sft_data/hf_hub/datasets--DCAgent--exp_rpt_issue_10k_glm_4.7_traces_jupiter/snapshots/6836f54c91f0d07f700073b7025b48491a6c22d9_thinking_preprocessed. This specialized training suggests its primary utility lies in tasks related to issue reporting, analysis, or resolution within a technical or project management context.
Training Details
The model was trained with the following key hyperparameters:
- Learning Rate: 4e-05
- Batch Size: 1 (train), 8 (eval)
- Epochs: 7.0
- Optimizer: AdamW_Torch_Fused with betas=(0.9, 0.98) and epsilon=1e-08
- LR Scheduler: Cosine with a 0.1 warmup ratio
- Distributed Training: Multi-GPU setup across 16 devices, resulting in a total train batch size of 16.
Potential Use Cases
Given its fine-tuning data, this model is likely well-suited for:
- Automated Issue Analysis: Processing and categorizing bug reports, feature requests, or support tickets.
- Report Generation: Summarizing or generating responses based on structured issue data.
- Task Management: Assisting in the understanding and prioritization of development or operational tasks derived from issue tracking systems.