ishala/qwen3-8b-instruct-indo-sft
The ishala/qwen3-8b-instruct-indo-sft is an 8 billion parameter instruction-tuned language model developed by ishala, based on the Qwen3 architecture. This model was fine-tuned using Unsloth and Huggingface's TRL library, offering a 32768 token context length. It is specifically optimized for instruction-following tasks, leveraging its efficient training methodology.
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Model Overview
The ishala/qwen3-8b-instruct-indo-sft is an 8 billion parameter instruction-tuned language model developed by ishala. It is built upon the Qwen3 architecture and was fine-tuned from the unsloth/qwen3-8b-unsloth-bnb-4bit base model.
Key Characteristics
- Architecture: Qwen3-based, a powerful transformer model.
- Parameter Count: 8 billion parameters, balancing performance with computational efficiency.
- Context Length: Supports a substantial context window of 32768 tokens, enabling processing of longer inputs and generating coherent, extended responses.
- Training Efficiency: Fine-tuned using Unsloth and Huggingface's TRL library, which allowed for a 2x faster training process.
Primary Use Case
This model is primarily designed for instruction-following tasks, where it can interpret and execute user commands or queries effectively. Its instruction-tuned nature makes it suitable for applications requiring precise and contextually relevant responses based on given instructions.