Model Overview
DCAgent/exp_tas_repetition_penalty_1_05_traces is an 8 billion parameter language model, derived from the Qwen3-8B architecture. It has been fine-tuned on the DCAgent/exp_tas_repetition_penalty_1.05_traces dataset, indicating a specialization for tasks related to the characteristics of this specific dataset. The model supports a substantial context length of 32768 tokens, allowing for processing of lengthy inputs.
Training Details
The model underwent training with a learning rate of 4e-05 over 7.0 epochs. Key hyperparameters included a train_batch_size of 1, gradient_accumulation_steps of 2, and an AdamW_torch_fused optimizer. A cosine learning rate scheduler with a 0.1 warmup ratio was utilized. The training was performed using multi-GPU distribution across 8 devices.
Potential Use Cases
Given its fine-tuning on a specific dataset, this model is likely best suited for:
- Specialized data analysis: Tasks that align with the patterns and characteristics present in the
DCAgent/exp_tas_repetition_penalty_1.05_traces dataset. - Research and experimentation: Exploring the effects of repetition penalties or trace-based data on language model performance.
Limitations
As a specialized fine-tune, its general-purpose language capabilities might be less robust compared to its base model. Further information on its intended uses, limitations, and evaluation data is needed for a comprehensive understanding of its performance and applicability.