ShenaoZhang/0.001_idpo_noreplacerej_iter_1
ShenaoZhang/0.001_idpo_noreplacerej_iter_1 is a 7 billion parameter language model fine-tuned from HuggingFaceH4/mistral-7b-sft-beta. This model was trained using the HuggingFaceH4/ultrafeedback_binarized dataset, focusing on specific training hyperparameters including a learning rate of 5e-07 and a total batch size of 128. It is intended for tasks benefiting from its fine-tuning on feedback-binarized data, offering a specialized iteration for research and development.
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Overview
ShenaoZhang/0.001_idpo_noreplacerej_iter_1 is a 7 billion parameter language model derived from the HuggingFaceH4/mistral-7b-sft-beta base model. It has undergone fine-tuning on the HuggingFaceH4/ultrafeedback_binarized dataset, indicating a specialization in processing and generating responses based on binarized feedback data. The training process utilized specific hyperparameters, including a learning rate of 5e-07, a total training batch size of 128, and an Adam optimizer.
Key Training Details
- Base Model:
HuggingFaceH4/mistral-7b-sft-beta - Dataset:
HuggingFaceH4/ultrafeedback_binarized - Learning Rate: 5e-07
- Optimizer: Adam with betas=(0.9, 0.999) and epsilon=1e-08
- Epochs: 1
- Frameworks: Transformers 4.36.2, Pytorch 2.1.2+cu121, Datasets 2.14.6, Tokenizers 0.15.2
Potential Use Cases
This model is suitable for research and development in areas where fine-tuning on feedback-binarized data is relevant. Its specific training regimen suggests potential applications in tasks that benefit from iterative refinement based on binary feedback signals, such as preference modeling or reinforcement learning from human feedback (RLHF) related experiments.