DCAgent/b1_top4_seq
DCAgent/b1_top4_seq is an 8 billion parameter language model fine-tuned from Qwen/Qwen3-8B, designed for sequential tasks. This model leverages the Qwen3 architecture and a 32768 token context length, making it suitable for applications requiring processing of long sequences. It was fine-tuned on the DCAgent/b1_top4_seq dataset, indicating a specialization in specific sequential data processing.
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Model Overview
DCAgent/b1_top4_seq is an 8 billion parameter language model, fine-tuned from the robust Qwen/Qwen3-8B base model. It was trained on the /scratch/08134/negin/hub/datasets--DCAgent--b1_top4_seq/snapshots/01924f6f86b8e836e06754caadf99b88aa4cbcb4 dataset, suggesting a specialization in tasks related to sequential data processing.
Key Training Details
- Base Model: Qwen/Qwen3-8B
- 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 setup with 16 devices, resulting in a total training batch size of 16.
Intended Use Cases
While specific intended uses and limitations are not detailed in the provided information, its fine-tuning on a sequential dataset implies potential strengths in:
- Processing and generating sequential data.
- Tasks requiring understanding of ordered information.
Users should conduct further evaluation to determine its suitability for specific applications, especially given the general nature of the provided model description.