taharmasmaliyev07/Qwen2.5-3B-Instruct-Perplexity-E3-BF16
TEXT GENERATIONConcurrency Cost:1Model Size:3.1BQuant:BF16Ctx Length:32kPublished:Apr 19, 2026License:apache-2.0Architecture:Transformer Open Weights Cold
Qwen2.5-3B-Instruct-Perplexity-E3-BF16 is a 3.1 billion parameter instruction-tuned causal language model developed by taharmasmaliyev07. Finetuned from unsloth/Qwen2.5-3B-Instruct, this model was trained significantly faster using the Unsloth framework. It is designed for general instruction-following tasks, leveraging its efficient training methodology to provide a capable model in a compact size.
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Model Overview
This model, taharmasmaliyev07/Qwen2.5-3B-Instruct-Perplexity-E3-BF16, is a 3.1 billion parameter instruction-tuned language model. It is based on the Qwen2.5 architecture and was finetuned from unsloth/Qwen2.5-3B-Instruct.
Key Characteristics
- Efficient Training: A notable feature of this model is its development using the Unsloth framework, which enabled a 2x faster training process. This suggests an optimization for speed and resource efficiency during fine-tuning.
- Instruction-Tuned: As an instruction-tuned model, it is designed to follow user prompts and instructions effectively, making it suitable for a variety of conversational and task-oriented applications.
- Compact Size: With 3.1 billion parameters, it offers a balance between performance and computational requirements, making it accessible for deployment in environments with limited resources.
Good For
- Applications requiring a capable instruction-following model with a smaller footprint.
- Scenarios where faster fine-tuning or deployment is a priority.
- General natural language understanding and generation tasks.