Model Overview

Koalacrown/qwen3-4b-multiturn-sft-16bit is a 4 billion parameter language model based on the Qwen3 architecture, developed by Koalacrown. It is specifically fine-tuned for supervised multiturn conversations, making it suitable for interactive AI applications. The model leverages a 32768 token context window, allowing it to handle longer conversational histories and more complex prompts.

Key Training Details

• Base Model: Fine-tuned from Koalacrown/qwen3-4b-cold-start-16bit.
• Training Efficiency: Achieved 2x faster training speeds by utilizing Unsloth and Huggingface's TRL library.
• License: Distributed under the Apache-2.0 license.

Good For

• Multiturn Conversational AI: Its supervised fine-tuning for multiturn interactions makes it well-suited for chatbots, virtual assistants, and dialogue systems.
• Applications Requiring Long Context: The 32768 token context length is beneficial for tasks that need to maintain extensive conversational history or process large amounts of input text.
• Efficient Deployment: As a 4 billion parameter model, it offers a balance between performance and computational efficiency, potentially enabling faster inference compared to larger models.