ffddfre23/qwen2_5_3b_anton
TEXT GENERATIONConcurrency Cost:1Model Size:3.1BQuant:BF16Ctx Length:32kPublished:Mar 4, 2026License:apache-2.0Architecture:Transformer Open Weights Warm
ffddfre23/qwen2_5_3b_anton is a 3.1 billion parameter Qwen2-based causal language model developed by ffddfre23. This instruction-tuned model was finetuned using Unsloth and Huggingface's TRL library, resulting in a 2x faster training process. It is designed for general language tasks, leveraging its efficient training methodology to provide a capable model within its size class.
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Model Overview
ffddfre23/qwen2_5_3b_anton is a 3.1 billion parameter language model based on the Qwen2 architecture. Developed by ffddfre23, this model is an instruction-tuned variant, finetuned from unsloth/qwen2.5-3b-instruct-bnb-4bit.
Key Characteristics
- Efficient Training: The model was trained with Unsloth and Huggingface's TRL library, enabling a 2x faster finetuning process compared to standard methods.
- Architecture: Built upon the Qwen2 family, known for its strong performance across various language understanding and generation tasks.
- Parameter Count: With 3.1 billion parameters, it offers a balance between performance and computational efficiency.
Use Cases
This model is suitable for a range of applications where a compact yet capable language model is required, including:
- Instruction-following tasks.
- Text generation and completion.
- General natural language processing tasks.