gjyotin305/Qwen2.5-3B-Instruct_old_sft_alpaca_005
The gjyotin305/Qwen2.5-3B-Instruct_old_sft_alpaca_005 is a 3.1 billion parameter instruction-tuned causal language model, fine-tuned by gjyotin305 from unsloth/Qwen2.5-3B-Instruct. This model was trained using Unsloth and Huggingface's TRL library, enabling faster fine-tuning. It is designed for general instruction-following tasks, leveraging its 32768 token context length for processing longer inputs.
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Model Overview
The gjyotin305/Qwen2.5-3B-Instruct_old_sft_alpaca_005 is a 3.1 billion parameter instruction-tuned language model. Developed by gjyotin305, this model is a fine-tuned version of unsloth/Qwen2.5-3B-Instruct.
Key Characteristics
- Base Model: Fine-tuned from the Qwen2.5-3B-Instruct architecture.
- Training Efficiency: Utilizes Unsloth and Huggingface's TRL library for accelerated fine-tuning, reportedly achieving 2x faster training.
- Context Length: Supports a substantial context window of 32768 tokens, suitable for handling extensive conversational histories or long documents.
Use Cases
This model is primarily suited for general instruction-following tasks, benefiting from its instruction-tuned nature and large context window. Its efficient fine-tuning process suggests potential for rapid adaptation to specific downstream applications.