yunjae-won/mpq3_qwen4bi_sft_dpo_beta1e-1_step7168
The yunjae-won/mpq3_qwen4bi_sft_dpo_beta1e-1_step7168 is a 4 billion parameter language model developed by yunjae-won. This model is a fine-tuned version, indicated by 'sft_dpo', suggesting optimization through supervised fine-tuning and direct preference optimization. With a context length of 32768 tokens, it is designed for tasks requiring extensive contextual understanding. Its specific differentiators and primary use cases are not detailed in the provided information.
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Model Overview
The yunjae-won/mpq3_qwen4bi_sft_dpo_beta1e-1_step7168 is a 4 billion parameter language model. The model name indicates it has undergone supervised fine-tuning (SFT) and direct preference optimization (DPO), suggesting an emphasis on aligning its outputs with human preferences and instructions. It features a substantial context length of 32768 tokens, which allows it to process and generate longer sequences of text, making it suitable for applications requiring extensive contextual understanding.
Key Characteristics
- Parameter Count: 4 billion parameters, offering a balance between performance and computational efficiency.
- Context Length: 32768 tokens, enabling the model to handle complex and lengthy inputs or generate detailed responses.
- Training Methodology: The
sft_dpoin its name implies a training regimen focused on improving instruction following and output quality through human feedback and preference learning.
Current Limitations
As per the provided model card, specific details regarding the model's development, funding, exact model type, language support, license, and finetuning base are currently marked as "More Information Needed." Consequently, its direct use cases, downstream applications, known biases, risks, and detailed performance metrics are not yet available. Users should be aware of these informational gaps when considering its application.