zamber1991/Qwen2.5-1.5B-KTO-Finetuning
TEXT GENERATIONConcurrency Cost:1Model Size:1.5BQuant:BF16Ctx Length:32kPublished:Mar 22, 2026License:apache-2.0Architecture:Transformer Open Weights Warm
The zamber1991/Qwen2.5-1.5B-KTO-Finetuning model is a 1.5 billion parameter Qwen2.5-based language model, developed by zamber1991. This model was finetuned using Unsloth and Huggingface's TRL library, enabling faster training. It is optimized for tasks benefiting from efficient finetuning, making it suitable for applications requiring a compact yet capable model.
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Model Overview
The zamber1991/Qwen2.5-1.5B-KTO-Finetuning is a 1.5 billion parameter language model based on the Qwen2.5 architecture. It was developed by zamber1991 and represents a finetuned version of unsloth/qwen2.5-1.5b-instruct-unsloth-bnb-4bit.
Key Characteristics
- Efficient Finetuning: This model was trained significantly faster using Unsloth and Huggingface's TRL library. This approach allows for more rapid iteration and deployment of specialized models.
- Base Architecture: Built upon the Qwen2.5 series, it inherits the foundational capabilities of this robust model family.
- Parameter Count: With 1.5 billion parameters, it offers a balance between performance and computational efficiency, making it suitable for resource-constrained environments or applications where smaller models are preferred.
Use Cases
This model is particularly well-suited for scenarios where:
- Rapid and efficient finetuning is a priority.
- A compact model size (1.5B parameters) is advantageous for deployment or inference speed.
- Applications can benefit from the underlying capabilities of the Qwen2.5 architecture, enhanced by specific finetuning.