gjyotin305/Qwen2.5-3B-Instruct_old_sft_alpaca_001
gjyotin305/Qwen2.5-3B-Instruct_old_sft_alpaca_001 is a 3.1 billion parameter instruction-tuned causal language model developed by gjyotin305. This model is a fine-tuned version of unsloth/Qwen2.5-3B-Instruct, optimized for performance and efficiency. It was trained using Unsloth and Hugging Face's TRL library, resulting in faster training times. This model is suitable for general instruction-following tasks, leveraging its efficient fine-tuning process.
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Model Overview
gjyotin305/Qwen2.5-3B-Instruct_old_sft_alpaca_001 is an instruction-tuned language model with approximately 3.1 billion parameters. It was developed by gjyotin305 and is a fine-tuned variant of the unsloth/Qwen2.5-3B-Instruct base model.
Key Characteristics
- Efficient Training: This model was fine-tuned using Unsloth and Hugging Face's TRL library, which enabled 2x faster training compared to standard methods.
- Base Model: Built upon the Qwen2.5-3B-Instruct architecture, providing a solid foundation for instruction-following capabilities.
- Context Length: Supports a context length of 32768 tokens, allowing for processing longer inputs and generating more extensive responses.
Use Cases
This model is well-suited for applications requiring a compact yet capable instruction-following language model. Its efficient training process suggests it could be a good candidate for scenarios where rapid iteration or resource-constrained deployment is a factor. It can be used for various natural language processing tasks that benefit from instruction-tuned models, such as question answering, summarization, and text generation based on given prompts.