W-61/llama3-hh-helpful-qt045-b0p01-20260429-085449
W-61/llama3-hh-helpful-qt045-b0p01-20260429-085449 is an 8 billion parameter language model, fine-tuned from W-61/llama-3-8b-base-sft-hh-helpful-4xh200 on the Anthropic/hh-rlhf dataset. This model is optimized for generating helpful and harmless responses, leveraging its 8192 token context length. Its primary use case is for applications requiring robust and ethically aligned conversational AI.
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Overview
This model, W-61/llama3-hh-helpful-qt045-b0p01-20260429-085449, is an 8 billion parameter language model. It is a fine-tuned iteration of the W-61/llama-3-8b-base-sft-hh-helpful-4xh200 base model, specifically trained on the Anthropic/hh-rlhf dataset. This fine-tuning process aims to enhance the model's ability to produce helpful and harmless outputs, making it suitable for applications where ethical and constructive responses are paramount.
Key Capabilities
- Helpful Response Generation: Optimized for providing informative and useful answers.
- Harmless Content Production: Designed to avoid generating harmful or biased content.
- Conversational AI: Suitable for dialogue systems and interactive applications requiring aligned responses.
Training Details
The model was trained using a learning rate of 5e-07, with a total training batch size of 64 across 4 GPUs. The training utilized the AdamW_Torch optimizer and a cosine learning rate scheduler with a 0.1 warmup ratio over 1 epoch. The training environment included Transformers 4.51.0, Pytorch 2.3.1+cu121, Datasets 2.21.0, and Tokenizers 0.21.4.
Good for
- Building chatbots that prioritize safety and helpfulness.
- Applications requiring ethically aligned AI responses.
- Research into helpful and harmless AI behavior.