DCAgent/a1-stack_selfdoc_gpt5mini
The DCAgent/a1-stack_selfdoc_gpt5mini is an 8 billion parameter language model, fine-tuned from Qwen/Qwen3-8B, with a context length of 32768 tokens. This model is specifically adapted for tasks related to self-documentation and understanding of execution traces, leveraging a specialized dataset. It is designed to process and interpret complex system logs and operational data for improved analysis.
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Model Overview
The DCAgent/a1-stack_selfdoc_gpt5mini is an 8 billion parameter language model, fine-tuned from the Qwen/Qwen3-8B architecture. It boasts a substantial context length of 32768 tokens, enabling it to process extensive inputs.
Key Characteristics
- Base Model: Fine-tuned from
Qwen/Qwen3-8B. - Parameter Count: 8 billion parameters.
- Context Length: Supports up to 32768 tokens.
- Training Data: Specialized dataset derived from
/e/scratch/jureap59/raoof1/sft_data/hf_hub/datasets--DCAgent--exp_rpt_stack-selfdoc-gpt5mini_glm_4.7_traces_jupiter/snapshots/808b1f7106246e31e0782a4b711778e14291be9a_thinking_preprocessed.
Training Details
The model was trained with a learning rate of 4e-05 over 7 epochs, utilizing a multi-GPU setup with 16 devices. The optimizer used was ADAMW_TORCH_FUSED with specific beta and epsilon values, and a cosine learning rate scheduler with a 0.1 warmup ratio. The training involved a total batch size of 16.
Intended Use Cases
While specific intended uses and limitations require further information, the fine-tuning on a dataset related to exp_rpt_stack-selfdoc-gpt5mini_glm_4.7_traces_jupiter suggests its primary application is in processing and interpreting complex system traces and self-documentation tasks. This specialization indicates potential utility in areas requiring detailed analysis of operational data or code execution flows.