dedyirama/dicoding-llm
dedyirama/dicoding-llm is a 1.5 billion parameter Qwen2.5-based instruction-tuned causal language model developed by dedyirama. This model was fine-tuned using Unsloth and Huggingface's TRL library, enabling faster training. It is designed for general instruction-following tasks, leveraging its efficient training methodology.
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Overview
dedyirama/dicoding-llm is a 1.5 billion parameter instruction-tuned language model based on the Qwen2.5 architecture. Developed by dedyirama, this model was fine-tuned from unsloth/Qwen2.5-1.5B-Instruct-bnb-4bit.
Key Characteristics
- Architecture: Qwen2.5-based, a causal language model.
- Parameter Count: 1.5 billion parameters, offering a balance between performance and computational efficiency.
- Training Efficiency: Fine-tuned using Unsloth and Huggingface's TRL library, which facilitated a 2x faster training process.
- Context Length: Supports a context window of 32768 tokens.
Use Cases
This model is suitable for general instruction-following tasks where a compact yet capable language model is required. Its efficient training process suggests potential for applications needing rapid deployment or iteration on fine-tuning.