RakeshUdayagiri/qwen-gsm8k
RakeshUdayagiri/qwen-gsm8k is a 0.5 billion parameter language model fine-tuned from Qwen/Qwen2.5-0.5B-Instruct. This model has been specifically trained using the SFT method with TRL, indicating an optimization for instruction-following and potentially mathematical reasoning tasks, given the 'gsm8k' in its name. With a context length of 32768 tokens, it is designed for efficient performance in specific problem-solving scenarios.
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Model Overview
RakeshUdayagiri/qwen-gsm8k is a specialized language model derived from the Qwen/Qwen2.5-0.5B-Instruct base model. It features 0.5 billion parameters and supports a substantial context length of 32768 tokens, making it suitable for tasks requiring processing of longer inputs.
Key Capabilities
- Instruction Following: As a fine-tuned version of an instruct model, it is designed to follow user instructions effectively.
- Specialized Training: The model was trained using Supervised Fine-Tuning (SFT) with the TRL library, suggesting a focus on improving performance for specific downstream tasks.
- Efficient Inference: Its relatively small size (0.5B parameters) allows for more efficient deployment and faster inference compared to larger models.
Training Details
The model's training leveraged the TRL framework (version 0.26.2) alongside Transformers (4.56.1), PyTorch (2.11.0+cu128), Datasets (4.0.0), and Tokenizers (0.22.0). This setup indicates a standard and robust fine-tuning process.
Use Cases
This model is particularly well-suited for applications where a compact yet capable instruction-tuned model is required, especially in scenarios that might benefit from its specific fine-tuning, such as problem-solving or question-answering tasks.