Model Overview

The kucingcoder/pengenalan-emosi model is a compact 0.5 billion parameter language model based on the Qwen2.5-Instruct architecture. It has been fine-tuned and converted into the GGUF format, leveraging Unsloth for accelerated training and conversion.

Key Capabilities

• Efficient Inference: Provided in GGUF format, enabling optimized performance on various hardware, including CPUs.
• Instruction-Tuned: Built upon the Qwen2.5-Instruct base, suggesting capabilities for following instructions and engaging in conversational tasks.
• Resource-Friendly: With only 0.5 billion parameters, it is well-suited for deployment in environments with limited computational resources.
• Ollama Integration: Includes an Ollama Modelfile for straightforward local deployment and usage.

Good For

• Local Development: Ideal for developers looking to run an instruction-tuned model locally without significant GPU requirements.
• Edge Devices: Its small footprint makes it a candidate for applications on edge devices or embedded systems.
• Rapid Prototyping: Enables quick experimentation and integration into projects due to its ease of use and efficient format.
• Text-based LLM Applications: Suitable for various text generation, summarization, and question-answering tasks where a compact model is preferred.