AutoSurferForCopilot/RedditQwen3.5TradFt
AutoSurferForCopilot/RedditQwen3.5TradFt is a 9 billion parameter language model fine-tuned from Qwen/Qwen3.5-9B. This model is specifically adapted using the autosurfer_refined dataset, indicating a specialization for tasks related to automated browsing or content summarization. It leverages a 32768 token context length, making it suitable for processing extensive textual inputs.
Loading preview...
Model Overview
AutoSurferForCopilot/RedditQwen3.5TradFt is a 9 billion parameter language model, fine-tuned from the base Qwen/Qwen3.5-9B architecture. This model has been specialized through fine-tuning on the autosurfer_refined dataset, suggesting an optimization for tasks involving automated content processing, potentially related to web browsing or information extraction.
Training Details
The model was trained with a learning rate of 1e-05 over 3 epochs, utilizing a multi-GPU setup with 4 devices and a total batch size of 8 (achieved with a train_batch_size of 1 and gradient_accumulation_steps of 2). The optimizer used was ADAMW_TORCH_FUSED with standard betas and epsilon, and a cosine learning rate scheduler with 0.1 warmup steps. The training environment included Transformers 5.8.0, Pytorch 2.13.0+cu130, Datasets 4.0.0, and Tokenizers 0.22.2.
Potential Use Cases
Given its fine-tuning on the autosurfer_refined dataset, this model is likely well-suited for applications requiring:
- Automated content summarization or extraction from web pages.
- Processing and understanding large volumes of text, benefiting from its 32768 token context length.
- Tasks related to automated data collection or analysis where refined text processing is crucial.