Alelcv27/Qwen2.5-3B-Base-Math
TEXT GENERATIONConcurrency Cost:1Model Size:3.1BQuant:BF16Ctx Length:32kPublished:Apr 14, 2026License:apache-2.0Architecture:Transformer Open Weights Warm
Alelcv27/Qwen2.5-3B-Base-Math is a 3.1 billion parameter Qwen2.5-based causal language model developed by Alelcv27. This model was fine-tuned using Unsloth and Huggingface's TRL library, indicating an optimization for efficient training. It is designed for general language tasks, leveraging its base architecture for broad applicability.
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Model Overview
Alelcv27/Qwen2.5-3B-Base-Math is a 3.1 billion parameter language model, fine-tuned by Alelcv27. It is based on the Qwen2.5 architecture and was developed using efficient training techniques.
Key Capabilities
- Efficiently Trained: This model was fine-tuned using Unsloth and Huggingface's TRL library, which enabled faster training times.
- Qwen2.5 Base: Leverages the robust Qwen2.5 architecture, providing a strong foundation for various natural language processing tasks.
- General Purpose: As a base model, it is suitable for a wide range of applications requiring language understanding and generation.
Good For
- Developers seeking efficient models: Its training methodology with Unsloth suggests it could be a good starting point for further fine-tuning or deployment where resource efficiency is a concern.
- General NLP tasks: Suitable for applications like text generation, summarization, and question answering, given its base model characteristics.
- Experimentation with Qwen2.5: Provides an accessible 3.1B parameter version of Qwen2.5 for research and development.