tsavage68/chat_1000STEPS_1e7_05beta_DPO
tsavage68/chat_1000STEPS_1e7_05beta_DPO is a 7 billion parameter language model fine-tuned from Meta Llama-2-7b-chat-hf using Direct Preference Optimization (DPO). This model was trained for 1000 steps with a learning rate of 1e-07, achieving a final validation loss of 0.6864. Its specific primary differentiator and intended use cases are not detailed in the provided information, suggesting it is an experimental or foundational DPO fine-tune.
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Model Overview
tsavage68/chat_1000STEPS_1e7_05beta_DPO is a 7 billion parameter language model derived from the meta-llama/Llama-2-7b-chat-hf base model. It has undergone fine-tuning using Direct Preference Optimization (DPO) over 1000 training steps. The model's training process involved a learning rate of 1e-07, a batch size of 4, and an Adam optimizer.
Training Details
- Base Model: meta-llama/Llama-2-7b-chat-hf
- Fine-tuning Method: Direct Preference Optimization (DPO)
- Parameters: 7 billion
- Training Steps: 1000
- Learning Rate: 1e-07
- Optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- Final Validation Loss: 0.6864
- Rewards/accuracies: 0.4571
Current Status
The model card indicates that more information is needed regarding its specific description, intended uses, limitations, and the dataset used for training. This suggests it may be an early-stage or experimental DPO fine-tune, with its unique capabilities and optimal applications yet to be fully defined or documented.