Koalacrown/qwen3-4b-multiturn-sft-16bit
Koalacrown/qwen3-4b-multiturn-sft-16bit is a 4 billion parameter Qwen3 model developed by Koalacrown, fine-tuned for multiturn supervised instruction. This model was trained using Unsloth and Huggingface's TRL library, achieving 2x faster training speeds. With a 32768 token context length, it is optimized for conversational AI applications requiring efficient processing and extended memory.
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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.