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# Model Card: Neural-phi2
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
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- **Model Architecture**: A finetuned version of the Phi2 model from Microsoft, utilizing Direct Preference Optimization (DPO) on the `distilabel-intel-orca-dpo-pairs` dataset.
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- **Model Size**: Approximately
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- **Training Data**: The model was finetuned on the `distilabel-intel-orca-dpo-pairs` dataset, which consists of chat-like prompts and responses.
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- **Training Procedure**: The Phi2 model was finetuned using the DPO technique
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- Loading and formatting the `distilabel-intel-orca-dpo-pairs` dataset
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- Defining the training configuration, including batch size, learning rate, and number of epochs
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- Initializing the DPO Trainer and training the model
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---
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# Model Card: Neural-phi2
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
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## Model Details
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- **Model Name**: Neural-phi2
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- **Model Type**: Large Language Model (LLM)
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- **Model Architecture**: A finetuned version of the Phi2 model from Microsoft, utilizing Direct Preference Optimization (DPO) on the `distilabel-intel-orca-dpo-pairs` dataset.
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- **Model Size**: Approximately 2B parameters
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- **Training Data**: The model was finetuned on the `distilabel-intel-orca-dpo-pairs` dataset, which consists of chat-like prompts and responses.
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- **Training Procedure**: The Phi2 model was finetuned using the DPO technique. The training process involved:
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- Loading and formatting the `distilabel-intel-orca-dpo-pairs` dataset
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- Defining the training configuration, including batch size, learning rate, and number of epochs
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- Initializing the DPO Trainer and training the model
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