dragonstorm123/qwen3.5-4b-sft-hallucination
The dragonstorm123/qwen3.5-4b-sft-hallucination is a 4.5 billion parameter Qwen3.5 model, developed by dragonstorm123. This model was finetuned using Unsloth and Huggingface's TRL library, enabling 2x faster training. It is designed for supervised fine-tuning tasks, leveraging the Qwen3.5 architecture.
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Model Overview
The dragonstorm123/qwen3.5-4b-sft-hallucination is a 4.5 billion parameter language model, finetuned from the base Qwen/Qwen3.5-4B architecture. Developed by dragonstorm123, this model leverages specific training methodologies to enhance its performance for supervised fine-tuning (SFT) applications.
Key Characteristics
- Base Model: Qwen/Qwen3.5-4B, providing a robust foundation for language understanding and generation.
- Parameter Count: 4.5 billion parameters, offering a balance between computational efficiency and model capability.
- Training Efficiency: The model was finetuned using Unsloth and Huggingface's TRL library, which facilitated a 2x faster training process compared to standard methods.
- Context Length: Supports a context length of 32768 tokens, allowing for processing of longer inputs and maintaining conversational coherence over extended interactions.
Intended Use Cases
This model is primarily suited for tasks requiring supervised fine-tuning, where its optimized training process can be beneficial. Its Qwen3.5 lineage and efficient finetuning make it a candidate for applications that benefit from a moderately sized, performant language model.