NazakathKanz/vedaz-qwen2.5-3b
TEXT GENERATIONConcurrent Unit Cost:1Model Size:3.1BQuant:BF16Context Size:32kTool Calling:SupportedPublished:Jul 3, 2026License:apache-2.0Architecture:Transformer Open Weights Featherless Exclusive Cold
NazakathKanz/vedaz-qwen2.5-3b is a 3.1 billion parameter Qwen2.5-based instruction-tuned causal language model developed by NazakathKanz. This model was finetuned using Unsloth and Huggingface's TRL library, enabling 2x faster training. It is designed for general language tasks, leveraging its efficient training methodology for practical applications.
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Model Overview
NazakathKanz/vedaz-qwen2.5-3b is a 3.1 billion parameter instruction-tuned model based on the Qwen2.5 architecture. It was developed by NazakathKanz and finetuned from unsloth/Qwen2.5-3B-Instruct-bnb-4bit.
Key Characteristics
- Efficient Training: This model was finetuned using Unsloth and Huggingface's TRL library, which facilitated a 2x faster training process compared to standard methods.
- Base Model: Built upon the Qwen2.5-3B-Instruct foundation, it inherits the capabilities of a robust causal language model.
- License: The model is released under the Apache-2.0 license, allowing for broad use and distribution.
Potential Use Cases
- General Text Generation: Suitable for various natural language processing tasks, including content creation, summarization, and question answering.
- Research and Development: Its efficient training methodology makes it an interesting candidate for further experimentation and finetuning on specific datasets.
- Resource-Constrained Environments: As a 3.1 billion parameter model, it offers a balance between performance and computational requirements, potentially making it suitable for deployment in environments with limited resources.