Thiraput01/PeaceKeeper-4B-V2
Thiraput01/PeaceKeeper-4B-V2 is a 4 billion parameter Qwen3-based causal language model developed by Thiraput01, fine-tuned using Unsloth and Huggingface's TRL library. This model is optimized for efficient training, achieving 2x faster finetuning compared to standard methods. With a 32768 token context length, it is designed for general language tasks where efficient deployment and performance are key.
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Model Overview
Thiraput01/PeaceKeeper-4B-V2 is a 4 billion parameter language model based on the Qwen3 architecture, developed by Thiraput01. This model has been specifically fine-tuned using the Unsloth library in conjunction with Huggingface's TRL, enabling significantly faster training times.
Key Characteristics
- Architecture: Qwen3-based, providing a robust foundation for various language understanding and generation tasks.
- Parameter Count: 4 billion parameters, offering a balance between performance and computational efficiency.
- Context Length: Supports a substantial context window of 32768 tokens, allowing for processing longer inputs and generating more coherent, extended outputs.
- Efficient Training: Leverages Unsloth for 2x faster fine-tuning, making it an efficient choice for developers looking to adapt models quickly.
Use Cases
This model is suitable for applications requiring a capable language model with efficient fine-tuning capabilities. Its large context window makes it well-suited for tasks such as:
- Text summarization of lengthy documents.
- Advanced question answering over extensive texts.
- Content generation requiring long-range coherence.
- Applications where rapid iteration and fine-tuning are beneficial.