W-61/qwen3-8b-base-beta-dpo-ultrafeedback-4xh200-batch-128-20260423-040315
W-61/qwen3-8b-base-beta-dpo-ultrafeedback-4xh200-batch-128-20260423-040315 is an 8 billion parameter language model developed by W-61, fine-tuned from a Qwen3-8B base model. This model has been optimized using DPO (Direct Preference Optimization) on the HuggingFaceH4/ultrafeedback_binarized dataset, enhancing its ability to align with human preferences. With a 32K context length, it is designed for applications requiring nuanced response generation and preference-aligned outputs.
Loading preview...
Model Overview
This model, developed by W-61, is an 8 billion parameter language model fine-tuned from a Qwen3-8B base model. It leverages Direct Preference Optimization (DPO) on the HuggingFaceH4/ultrafeedback_binarized dataset, aiming to improve its alignment with human preferences and generate more desirable responses. The model supports a substantial context length of 32,768 tokens.
Key Characteristics
- Base Model: Fine-tuned from
W-61/qwen3-8b-base-sft-ultrachat-4xh200-batch-128. - Optimization Method: Utilizes Direct Preference Optimization (DPO) for enhanced response quality and alignment.
- Training Data: Fine-tuned on the
HuggingFaceH4/ultrafeedback_binarizeddataset. - Context Length: Features a 32K token context window, suitable for processing longer inputs and generating coherent extended outputs.
Training Details
The model underwent a single epoch of training with a learning rate of 5e-07, a total batch size of 128, and a cosine learning rate scheduler. Evaluation metrics show a final loss of 0.5897 and a Beta Dpo/gap Mean of 23.0136, indicating effective preference learning.
Intended Uses
This model is suitable for applications where generating responses that align with human preferences is critical, such as advanced chatbots, content generation, and interactive AI systems that benefit from preference-tuned outputs.