tengfeima-ai/Qwen2.5-0.5B-Math-SFT-Concise
The tengfeima-ai/Qwen2.5-0.5B-Math-SFT-Concise is a 0.5 billion parameter language model, fine-tuned from Qwen/Qwen2.5-0.5B-Instruct, with a 32768 token context length. This model is specifically fine-tuned on the concise_sft dataset, indicating an optimization for tasks requiring concise and focused responses. It is designed for applications where a smaller, efficient model with a strong emphasis on brevity and directness is beneficial.
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Model Overview
The tengfeima-ai/Qwen2.5-0.5B-Math-SFT-Concise is a 0.5 billion parameter language model, fine-tuned from the base Qwen/Qwen2.5-0.5B-Instruct model. It has been specialized through fine-tuning on the concise_sft dataset, suggesting an optimization for generating brief and direct outputs. The model maintains a substantial context length of 32768 tokens, allowing it to process and generate responses based on extensive input.
Training Details
This model was trained using the following key hyperparameters:
- Learning Rate: 2e-05
- Batch Size: 8 (train), 8 (eval)
- Gradient Accumulation: 4 steps, resulting in a total training batch size of 64
- Optimizer: ADAMW_TORCH
- LR Scheduler: Cosine type with a 0.1 warmup ratio
- Epochs: 3
During training, the model achieved a final validation loss of 0.6726, with the lowest validation loss recorded at 0.6232 during the 2500th step. The training process utilized Transformers 4.57.1, Pytorch 2.4.1+cu124, Datasets 4.0.0, and Tokenizers 0.22.2.
Potential Use Cases
Given its fine-tuning on a "concise_sft" dataset, this model is likely best suited for applications requiring:
- Summarization tasks where brevity is paramount.
- Generating direct answers or short explanations.
- Efficient deployment in resource-constrained environments due to its smaller parameter count.