yunjae-won/mpq3_qwen4bi_sft_dpo_beta1e-1_step9728
The yunjae-won/mpq3_qwen4bi_sft_dpo_beta1e-1_step9728 is a 4 billion parameter language model, fine-tuned from a Qwen base model. This model has undergone Supervised Fine-Tuning (SFT) and Direct Preference Optimization (DPO) with a beta value of 1e-1, reaching step 9728 in its training. It is designed to process a context length of up to 32768 tokens, making it suitable for tasks requiring extensive contextual understanding.
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Model Overview
The yunjae-won/mpq3_qwen4bi_sft_dpo_beta1e-1_step9728 is a 4 billion parameter language model, fine-tuned from a Qwen base architecture. This model has been developed through a training regimen that includes Supervised Fine-Tuning (SFT) and Direct Preference Optimization (DPO), with a beta parameter of 1e-1, concluding at step 9728.
Key Characteristics
- Parameter Count: 4 billion parameters, offering a balance between performance and computational efficiency.
- Context Length: Supports a substantial context window of 32768 tokens, enabling the processing of long inputs and maintaining coherence over extended conversations or documents.
- Training Methodology: Utilizes a combination of Supervised Fine-Tuning (SFT) and Direct Preference Optimization (DPO), suggesting an emphasis on aligning model outputs with human preferences and instructions.
Intended Use Cases
Given its architecture and training, this model is generally suitable for a range of natural language processing tasks that benefit from a large context window and preference-aligned responses. However, specific direct and downstream use cases, as well as detailed performance metrics, are not provided in the available model card. Users should conduct their own evaluations for specific applications.