dinhxuanhuy/Qwen2.5-3B-PhoMT-250k

TEXT GENERATIONConcurrency Cost:1Model Size:3.1BQuant:BF16Ctx Length:32kTool Calling:SupportedPublished:Jun 16, 2026License:apache-2.0Architecture:Transformer Open Weights Cold

dinhxuanhuy/Qwen2.5-3B-PhoMT-250k is a 3.1 billion parameter Qwen2.5 model developed by dinhxuanhuy, fine-tuned from unsloth/qwen2.5-3b-instruct-unsloth-bnb-4bit. This model was trained using Unsloth and Huggingface's TRL library, achieving 2x faster training. It is designed for general language tasks, leveraging its efficient training methodology.

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Model Overview

dinhxuanhuy/Qwen2.5-3B-PhoMT-250k is a 3.1 billion parameter language model, developed by dinhxuanhuy. It is a fine-tuned variant of the Qwen2.5 architecture, specifically building upon the unsloth/qwen2.5-3b-instruct-unsloth-bnb-4bit base model.

Key Characteristics

  • Efficient Training: This model was trained with a focus on efficiency, utilizing Unsloth and Huggingface's TRL library. This combination enabled a 2x faster training process compared to standard methods.
  • Base Model: It leverages the Qwen2.5-3B-Instruct architecture, providing a strong foundation for various language understanding and generation tasks.

Potential Use Cases

Given its efficient training and Qwen2.5 base, this model is suitable for applications requiring a compact yet capable language model. Its optimized training process suggests it could be a good candidate for scenarios where rapid iteration or resource-constrained deployment is a factor.

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