W-61/mistral-7b-base-sft-hh-harmless-4xh200-batch-64
W-61/mistral-7b-base-sft-hh-harmless-4xh200-batch-64 is a 7 billion parameter language model fine-tuned from Mistral-7B-v0.3. This model was specifically trained on the Anthropic/hh-rlhf dataset to enhance harmlessness and alignment. It is optimized for generating safe and helpful responses, making it suitable for applications requiring controlled and ethical AI interactions.
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Model Overview
This model, W-61/mistral-7b-base-sft-hh-harmless-4xh200-batch-64, is a 7 billion parameter language model derived from mistralai/Mistral-7B-v0.3. It has undergone supervised fine-tuning (SFT) using the Anthropic/hh-rlhf dataset, which is designed to train models to be helpful and harmless.
Key Characteristics
- Base Model: Mistral-7B-v0.3, known for its strong performance in its size class.
- Fine-tuning Objective: Enhanced harmlessness and alignment through training on the Anthropic/hh-rlhf dataset.
- Training Details: Trained with a learning rate of 2e-05, a total batch size of 64, and a cosine learning rate scheduler over 1 epoch. The training achieved a validation loss of 1.1678.
Intended Use Cases
This model is particularly well-suited for applications where generating safe, non-toxic, and helpful responses is critical. It can be used in scenarios requiring:
- Content moderation assistance.
- Customer support chatbots focused on ethical interactions.
- Educational tools requiring harmless and informative outputs.
- Any application where mitigating harmful or biased language is a priority.