stevensama73/Qwen2.5-3B-8B-sft-indonesian
TEXT GENERATIONConcurrency Cost:1Model Size:3.1BQuant:BF16Ctx Length:32kPublished:May 21, 2026License:apache-2.0Architecture:Transformer0.0K Open Weights Warm
The stevensama73/Qwen2.5-3B-8B-sft-indonesian model is a 3.1 billion parameter language model developed by stevensama73, fine-tuned from unsloth/Qwen2.5-3B-Instruct-bnb-4bit. This model is specifically optimized for Indonesian language tasks, leveraging Unsloth and Huggingface's TRL library for faster training. It is designed for applications requiring efficient and accurate natural language processing in Indonesian.
Loading preview...
stevensama73/Qwen2.5-3B-8B-sft-indonesian Overview
This model, developed by stevensama73, is a fine-tuned version of the Qwen2.5-3B-Instruct-bnb-4bit base model. It has been specifically adapted for Indonesian language tasks, making it a specialized tool for developers working with Indonesian NLP.
Key Capabilities
- Indonesian Language Specialization: The model is fine-tuned for Indonesian, suggesting enhanced performance and understanding for text in this language.
- Efficient Training: It leverages Unsloth and Huggingface's TRL library, indicating an optimized training process that allows for faster iteration and development.
- Compact Size: With 3.1 billion parameters, it offers a balance between performance and computational efficiency, suitable for various deployment scenarios.
Good For
- Indonesian NLP Applications: Ideal for tasks such as text generation, summarization, translation, or chatbots specifically targeting the Indonesian language.
- Resource-Efficient Deployments: Its 3.1B parameter count makes it a good candidate for applications where computational resources are a consideration, potentially offering faster inference compared to larger models.
- Developers using Unsloth: Those already familiar with or looking to utilize the Unsloth framework for efficient fine-tuning will find this model's origin particularly relevant.