DCAgent/exp_tas_max_episodes_32_traces
The DCAgent/exp_tas_max_episodes_32_traces is an 8 billion parameter language model, fine-tuned from Qwen/Qwen3-8B, specifically adapted using the DCAgent/exp_tas_max_episodes_32_traces dataset. This model is optimized for tasks related to its specific fine-tuning data, offering specialized performance within that domain. It leverages a 32768 token context length, making it suitable for processing extensive inputs relevant to its training. Its primary utility lies in applications requiring deep understanding and generation based on the characteristics of its fine-tuning dataset.
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Overview
This model, exp_tas_max_episodes_32_traces, is an 8 billion parameter language model derived from the Qwen/Qwen3-8B architecture. It has undergone fine-tuning on the DCAgent/exp_tas_max_episodes_32_traces dataset, indicating a specialization towards the characteristics and patterns present in this particular data. The training process involved a learning rate of 4e-05, a total batch size of 16 (with gradient accumulation steps of 2), and was conducted over 7 epochs using a cosine learning rate scheduler.
Key Training Details
- Base Model: Qwen/Qwen3-8B
- Fine-tuning Dataset: DCAgent/exp_tas_max_episodes_32_traces
- Learning Rate: 4e-05
- Optimizer: ADAMW_TORCH_FUSED
- Epochs: 7.0
- Context Length: 32768 tokens
Intended Use
While specific intended uses and limitations require further information, the model's fine-tuning suggests its application would be most effective in scenarios closely aligned with the data it was trained on. Developers should consider the nature of the DCAgent/exp_tas_max_episodes_32_traces dataset to determine suitability for their specific tasks.