brandolorian/answer-Qwen-stioning
The brandolorian/answer-Qwen-stioning model is a 0.6 billion parameter instruction-tuned causal language model developed by brandolorian, fine-tuned from Qwen1.5-0.5B. It is designed for question-answering tasks, demonstrating an eval_loss of 2.6400. This model offers a context length of 32768 tokens, making it suitable for processing moderately long inputs in specific NLP applications.
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Model Overview
The brandolorian/answer-Qwen-stioning model is a fine-tuned variant of the Qwen1.5-0.5B architecture, developed by brandolorian. This 0.6 billion parameter model is specifically optimized for question-answering tasks, building upon the foundational capabilities of the Qwen series.
Key Characteristics
- Base Model: Fine-tuned from Qwen/Qwen1.5-0.5B.
- Parameter Count: Features 0.6 billion parameters, offering a balance between performance and computational efficiency.
- Context Length: Supports a substantial context window of 32768 tokens, enabling it to process and understand longer queries and documents.
- Performance Metrics: Achieved an
eval_lossof 2.6400 during its evaluation, with aneval_samples_per_secondof 178.744.
Training Details
The model was trained with a learning rate of 2e-05, a train_batch_size of 16, and num_epochs set to 9. It utilized mixed-precision training (Native AMP) and the Adam optimizer. The training process involved Transformers 4.38.0.dev0 and Pytorch 2.1.0+cu121.
Intended Use Cases
This model is primarily suited for applications requiring efficient and accurate question-answering capabilities, particularly where the base Qwen1.5-0.5B architecture is a good fit. Its fine-tuning suggests improved performance on tasks involving extracting information or generating direct answers from provided contexts.