sanaeai/Qwen2.5-14B-Instruct-rep-ce
TEXT GENERATIONConcurrency Cost:1Model Size:14.8BQuant:FP8Ctx Length:32kPublished:Mar 24, 2026License:apache-2.0Architecture:Transformer Open Weights Cold
The sanaeai/Qwen2.5-14B-Instruct-rep-ce is a 14.8 billion parameter instruction-tuned causal language model developed by sanaeai. This model is a fine-tuned variant of the Qwen2.5 architecture, optimized for faster training using Unsloth and Huggingface's TRL library. It is designed for general instruction-following tasks, leveraging its efficient training methodology to provide a capable language model.
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Model Overview
The sanaeai/Qwen2.5-14B-Instruct-rep-ce is a 14.8 billion parameter instruction-tuned language model. Developed by sanaeai, this model is a fine-tuned version of the Qwen2.5-14B-Instruct base, specifically utilizing unsloth/qwen2.5-14b-instruct-unsloth-bnb-4bit as its foundation.
Key Characteristics
- Efficient Fine-tuning: This model was fine-tuned using Unsloth and Huggingface's TRL library, enabling a reported 2x faster training process compared to standard methods.
- Instruction-tuned: Designed to follow instructions effectively, making it suitable for a wide range of conversational and task-oriented applications.
- Qwen2.5 Architecture: Benefits from the robust capabilities of the Qwen2.5 model family, known for strong performance across various language understanding and generation tasks.
Potential Use Cases
- General-purpose AI assistant: Capable of handling diverse queries and generating coherent responses.
- Text generation: Suitable for creative writing, content creation, and summarization tasks.
- Instruction following: Ideal for applications requiring the model to adhere to specific prompts and constraints.