antonius-vincent/Qwen2.5-3B-Instruct-Indonesian-QLoRA
TEXT GENERATIONConcurrent Unit Cost:1Model Size:3.1BQuant:BF16Context Size:32kTool Calling:SupportedPublished:Jul 18, 2026License:apache-2.0Architecture:Transformer Open Weights Featherless Exclusive Cold
The antonius-vincent/Qwen2.5-3B-Instruct-Indonesian-QLoRA is a 3.1 billion parameter instruction-tuned causal language model, developed by antonius-vincent. Finetuned from unsloth/Qwen2.5-3B-Instruct-bnb-4bit, this model is optimized for Indonesian language tasks. It leverages QLoRA and Unsloth for efficient training, making it suitable for applications requiring a compact yet capable Indonesian-focused LLM.
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Model Overview
This model, developed by antonius-vincent, is an instruction-tuned variant of the Qwen2.5-3B architecture, specifically optimized for the Indonesian language. It is finetuned from the unsloth/Qwen2.5-3B-Instruct-bnb-4bit base model, indicating a focus on efficient deployment and performance.
Key Characteristics
- Architecture: Based on the Qwen2.5-3B model, a 3.1 billion parameter causal language model.
- Language Focus: Primarily designed and optimized for Indonesian language understanding and generation.
- Training Efficiency: Utilizes Unsloth and Huggingface's TRL library for faster and more efficient finetuning, suggesting a QLoRA (Quantized Low-Rank Adaptation) approach.
- Context Length: Supports a context length of 32768 tokens, allowing for processing longer inputs and generating more coherent responses.
Potential Use Cases
- Indonesian Language Applications: Ideal for chatbots, content generation, translation, and summarization tasks specifically in Indonesian.
- Resource-Efficient Deployment: Its 3.1B parameter size combined with QLoRA finetuning makes it suitable for environments with limited computational resources.
- Instruction Following: As an instruction-tuned model, it is designed to follow user prompts and generate relevant outputs effectively.