DCAgent/a1-go_browse_wa
DCAgent/a1-go_browse_wa is an 8 billion parameter language model fine-tuned from Qwen/Qwen3-8B. This model is specifically trained on the /e/scratch/jureap59/raoof1/sft_data/hf_hub/datasets--DCAgent--neulab-go-browse-wa-sandboxes_glm_4.7_traces_jupiter/snapshots/dd1b9192915cfa62681657f80a3c4cccece9c18f_thinking_preprocessed dataset. It is designed for specialized applications related to the dataset it was fine-tuned on, likely involving web browsing or agent-based interactions. The model has a context length of 32768 tokens.
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Overview
DCAgent/a1-go_browse_wa is an 8 billion parameter language model, fine-tuned from the Qwen/Qwen3-8B architecture. This model was trained using a specific dataset, /e/scratch/jureap59/raoof1/sft_data/hf_hub/datasets--DCAgent--neulab-go-browse-wa-sandboxes_glm_4.7_traces_jupiter/snapshots/dd1b9192915cfa62681657f80a3c4cccece9c18f_thinking_preprocessed, suggesting a specialization in tasks related to its training data, potentially involving web browsing or agent-based interactions. It supports a substantial context length of 32768 tokens.
Training Details
The model was trained with the following key hyperparameters:
- Learning Rate: 4e-05
- Batch Size: 1 (train), 8 (eval)
- Optimizer: ADAMW_TORCH_FUSED with betas=(0.9, 0.98) and epsilon=1e-08
- Scheduler: Cosine learning rate scheduler with a 0.1 warmup ratio
- Epochs: 7.0
- Devices: 16 GPUs were used for distributed training, resulting in a total effective training batch size of 16 and evaluation batch size of 128.
Framework Versions
The training utilized:
- Transformers 4.57.6
- Pytorch 2.9.1+cu130
- Datasets 4.7.0
- Tokenizers 0.22.2