Rifan007/qwen2.5-1.5b-alpaca-id
Rifan007/qwen2.5-1.5b-alpaca-id is a 1.5 billion parameter Qwen2.5-based language model developed by Rifan007, fine-tuned from unsloth/Qwen2.5-1.5B-Instruct-bnb-4bit. This model was optimized for faster training using Unsloth and Huggingface's TRL library, offering a compact yet capable solution for various natural language processing tasks. With a context length of 32768 tokens, it is suitable for applications requiring efficient processing of longer sequences.
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Model Overview
Rifan007/qwen2.5-1.5b-alpaca-id is a 1.5 billion parameter language model built upon the Qwen2.5 architecture. Developed by Rifan007, this model is a fine-tuned version of unsloth/Qwen2.5-1.5B-Instruct-bnb-4bit, leveraging the Unsloth library and Huggingface's TRL for accelerated training.
Key Capabilities
- Efficient Training: Achieves 2x faster training speeds due to optimization with Unsloth, making it resource-friendly for fine-tuning.
- Qwen2.5 Architecture: Benefits from the robust capabilities of the Qwen2.5 base model.
- Extended Context Window: Supports a context length of 32768 tokens, enabling the processing of substantial input texts.
- Compact Size: At 1.5 billion parameters, it offers a balance between performance and computational efficiency.
Good For
- Resource-constrained environments: Its optimized training and smaller parameter count make it suitable for deployment where computational resources are limited.
- Applications requiring long context understanding: The 32768-token context window is beneficial for tasks like summarization of lengthy documents, detailed question answering, or conversational AI with extended memory.
- Rapid prototyping and experimentation: The faster training facilitated by Unsloth allows for quicker iteration cycles in development.
This model is licensed under Apache-2.0, providing flexibility for various uses.