DCAgent/a1-stack_jest
DCAgent/a1-stack_jest is an 8 billion parameter causal language model fine-tuned from Qwen/Qwen3-8B. This model is specifically trained on the 'exp_rpt_stack-jest-large_10k_glm_4.7_traces_jupiter' dataset, indicating an optimization for tasks related to report generation or trace analysis within a specific domain. It leverages a 32768 token context length, making it suitable for processing longer inputs relevant to its specialized training data.
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Overview
DCAgent/a1-stack_jest is an 8 billion parameter language model, fine-tuned from the base Qwen3-8B architecture. This model has undergone specialized training on the exp_rpt_stack-jest-large_10k_glm_4.7_traces_jupiter dataset, suggesting its development for tasks involving report processing, trace analysis, or similar domain-specific applications. The fine-tuning process utilized a learning rate of 4e-05, a cosine learning rate scheduler with a 0.1 warmup ratio, and was trained for 7 epochs across 16 GPUs.
Key Training Details
- Base Model: Qwen/Qwen3-8B
- Fine-tuning Dataset:
exp_rpt_stack-jest-large_10k_glm_4.7_traces_jupiter - Learning Rate: 4e-05
- Optimizer: AdamW_Torch_Fused with betas=(0.9, 0.98) and epsilon=1e-08
- Epochs: 7.0
- Distributed Training: Multi-GPU (16 devices)
Intended Use Cases
Given its specialized training data, DCAgent/a1-stack_jest is likely optimized for:
- Generating or analyzing reports based on structured traces.
- Processing and understanding domain-specific logs or diagnostic outputs.
- Tasks requiring contextual understanding from large trace datasets.