elmosiussuli/qwen2.5-1.5b-indonesian-sft-pgabl
The elmosiussuli/qwen2.5-1.5b-indonesian-sft-pgabl is a 1.5 billion parameter Qwen2.5 model, fine-tuned by elmosiussuli from unsloth/Qwen2.5-1.5B-Instruct-bnb-4bit. This model is specifically optimized for Indonesian language tasks, leveraging Unsloth and Huggingface's TRL library for efficient training. It is designed for applications requiring a compact yet capable language model for Indonesian language processing.
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Model Overview
The elmosiussuli/qwen2.5-1.5b-indonesian-sft-pgabl is a 1.5 billion parameter language model based on the Qwen2.5 architecture. Developed by elmosiussuli, this model has been fine-tuned specifically for Indonesian language tasks, building upon the unsloth/Qwen2.5-1.5B-Instruct-bnb-4bit base model.
Key Characteristics
- Architecture: Qwen2.5, a causal language model known for its performance.
- Parameter Count: 1.5 billion parameters, offering a balance between performance and computational efficiency.
- Language Focus: Primarily optimized for the Indonesian language through supervised fine-tuning (SFT).
- Training Efficiency: The fine-tuning process utilized Unsloth and Huggingface's TRL library, enabling faster training.
- Context Length: Supports a context length of 32768 tokens, allowing for processing longer sequences of text.
Use Cases
This model is particularly well-suited for applications requiring a compact and efficient language model with strong capabilities in Indonesian. Potential use cases include:
- Indonesian text generation and completion.
- Indonesian language understanding tasks.
- Building chatbots or conversational AI systems in Indonesian.
- Any application where a smaller, specialized Indonesian LLM is beneficial for deployment on resource-constrained environments.