novitaguok/Qwen2.5-3B-Indonesian-SFT
TEXT GENERATIONConcurrent Unit Cost:1Model Size:3.1BQuant:BF16Context Size:32kTool Calling:SupportedPublished:Jul 5, 2026License:apache-2.0Architecture:Transformer Open Weights Featherless Exclusive Cold
The novitaguok/Qwen2.5-3B-Indonesian-SFT is a 3.1 billion parameter Qwen2.5 model, fine-tuned for Indonesian language tasks. Developed by novitaguok, this model leverages Unsloth for accelerated training, making it efficient for deployment. It is specifically optimized for supervised fine-tuning (SFT) to enhance its performance in Indonesian language understanding and generation.
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Model Overview
The novitaguok/Qwen2.5-3B-Indonesian-SFT is a 3.1 billion parameter language model, specifically fine-tuned for the Indonesian language. It is based on the Qwen2.5 architecture and was developed by novitaguok.
Key Characteristics
- Base Model: Fine-tuned from
unsloth/qwen2.5-3b-instruct-unsloth-bnb-4bit, indicating its foundation in the Qwen2.5 series. - Training Efficiency: The model was trained significantly faster using Unsloth and Hugging Face's TRL library, highlighting an efficient training methodology.
- Language Focus: Optimized through Supervised Fine-Tuning (SFT) specifically for Indonesian language tasks, making it suitable for applications requiring strong Indonesian linguistic capabilities.
Potential Use Cases
- Indonesian Text Generation: Generating coherent and contextually relevant text in Indonesian.
- Indonesian Language Understanding: Tasks such as sentiment analysis, summarization, or question answering in Indonesian.
- Resource-Efficient Deployment: Its 3.1 billion parameter size, combined with efficient training, suggests it could be a good candidate for applications where computational resources are a consideration, especially for Indonesian-centric use cases.