introtollm/qwen2.5-3B-cb-1_1
The introtollm/qwen2.5-3B-cb-1_1 model is a fine-tuned version of the Qwen2.5-3B architecture, featuring 3.1 billion parameters and a 32768-token context length. This model has been specifically adapted through fine-tuning on the cb_1_1_50000 dataset. It is designed for tasks benefiting from its base Qwen2.5 capabilities, enhanced by this targeted training.
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Model Overview
The introtollm/qwen2.5-3B-cb-1_1 is a fine-tuned language model based on the Qwen/Qwen2.5-3B architecture. It features approximately 3.1 billion parameters and supports a substantial 32768-token context window, making it suitable for processing longer sequences of text.
Key Characteristics
- Base Model: Qwen2.5-3B, a robust foundation for general language understanding and generation tasks.
- Fine-tuning: The model has undergone specific fine-tuning on the
cb_1_1_50000dataset, indicating a specialization for tasks related to the characteristics of this dataset. - Training Hyperparameters: Training involved a learning rate of 2e-05, a batch size of 1 (with 8 gradient accumulation steps), and an AdamW optimizer. The training process consisted of 2109 steps with a cosine learning rate scheduler and 42 warmup steps.
Potential Use Cases
Given its fine-tuning on the cb_1_1_50000 dataset, this model is likely best suited for applications that align with the data distribution and tasks represented in that dataset. Developers should evaluate its performance for:
- Specific text generation tasks where the
cb_1_1_50000dataset provides relevant examples. - Applications requiring a model with a 3.1B parameter count and a large context window, offering a balance between performance and computational efficiency.