jackf857/llama-3-8b-base-ipo-ultrafeedback-4xh200-batch-128-rerun-2-runpod
The jackf857/llama-3-8b-base-ipo-ultrafeedback-4xh200-batch-128-rerun-2-runpod model is an 8 billion parameter Llama 3 base model fine-tuned by jackf857. It is specifically optimized using the HuggingFaceH4/ultrafeedback_binarized dataset, focusing on improving response quality and alignment through a reward-based training approach. This model is designed for general language generation tasks where aligned and high-quality outputs are crucial.
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Model Overview
This model, llama-3-8b-base-ipo-ultrafeedback-4xh200-batch-128-rerun-2-runpod, is an 8 billion parameter language model developed by jackf857. It is a fine-tuned variant of the W-61/llama-3-8b-base-sft-ultrachat-8xh200 base model.
Key Characteristics
- Base Model: Llama 3 8B.
- Fine-tuning: Optimized using the
HuggingFaceH4/ultrafeedback_binarizeddataset. - Training Objective: The fine-tuning process aimed to improve response quality and alignment, as indicated by the reward-based evaluation metrics.
- Performance Metrics: During evaluation, the model achieved a rewards accuracy of 0.6880 and a rewards margin of 0.0236, suggesting an improved ability to differentiate between preferred and rejected responses.
Training Details
The model was trained with a learning rate of 5e-07 over 1 epoch, utilizing a total batch size of 128 across 4 GPUs. The training procedure involved a cosine learning rate scheduler with a 0.1 warmup ratio.
Intended Use Cases
This model is suitable for applications requiring a Llama 3 8B-class model with enhanced alignment and quality of generated text, particularly in scenarios where feedback-driven optimization is beneficial.