DCAgent/exp_tas_max_episodes_512_traces
DCAgent/exp_tas_max_episodes_512_traces is an 8 billion parameter language model, fine-tuned from Qwen/Qwen3-8B. This model is specifically adapted using the DCAgent/exp_tas_max_episodes_512_traces dataset, indicating a specialization in tasks related to agentic behavior or trajectory analysis. Its training parameters suggest an optimization for specific learning objectives, making it suitable for applications requiring nuanced understanding within its fine-tuning domain.
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Model Overview
This model, exp_tas_max_episodes_512_traces, is an 8 billion parameter language model derived from the Qwen/Qwen3-8B architecture. It has undergone a specific fine-tuning process using the DCAgent/exp_tas_max_episodes_512_traces dataset.
Training Details
The fine-tuning process involved several key hyperparameters:
- Learning Rate: 4e-05
- Batch Size: 1 (train), 8 (eval)
- Gradient Accumulation: 2 steps, leading to a total effective batch size of 16
- Optimizer: AdamW (fused) with betas=(0.9, 0.98) and epsilon=1e-08
- LR Scheduler: Cosine with a 0.1 warmup ratio
- Epochs: 7.0
- Devices: Trained across 8 GPUs
Potential Use Cases
Given its fine-tuning on a dataset related to 'exp_tas_max_episodes_512_traces', this model is likely specialized for tasks involving:
- Analysis of agent trajectories or sequences of actions.
- Understanding and generating responses within environments characterized by episodes and traces.
- Applications requiring a nuanced interpretation of sequential data, potentially in reinforcement learning or simulation contexts.
Limitations
As per the provided information, specific details regarding intended uses, limitations, and comprehensive training/evaluation data are not fully documented. Users should conduct thorough testing for their specific applications.