W-61/llama3-hh-helpful-qt045-b0p5-20260429-085449
W-61/llama3-hh-helpful-qt045-b0p5-20260429-085449 is an 8 billion parameter language model developed by W-61, fine-tuned from llama-3-8b-base-sft-hh-helpful-4xh200. This model specializes in helpfulness, having been fine-tuned on the Anthropic/hh-rlhf dataset. It is designed for applications requiring helpful and aligned responses, leveraging an 8192 token context length.
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Overview
This model, developed by W-61, is an 8 billion parameter language model fine-tuned for helpfulness. It is based on the llama-3-8b-base-sft-hh-helpful-4xh200 model and has undergone further training using the Anthropic/hh-rlhf dataset.
Key Capabilities
- Helpful Response Generation: Specialized in producing helpful and aligned outputs due to its fine-tuning on the Anthropic/hh-rlhf dataset.
- Base Model Enhancement: Builds upon an existing Llama 3 8B base, suggesting a foundation in general language understanding and generation.
- Context Handling: Supports an 8192 token context length, enabling processing of moderately long inputs and generating coherent, extended responses.
Training Details
The model was trained with a learning rate of 5e-07, a total batch size of 64, and for 1 epoch. It utilized a cosine learning rate scheduler with a 0.1 warmup ratio. The training environment included Transformers 4.51.0, Pytorch 2.3.1+cu121, Datasets 2.21.0, and Tokenizers 0.21.4.
Good For
- Applications requiring models that prioritize helpfulness and alignment in their responses.
- Tasks where a model fine-tuned on human feedback data is beneficial for improved interaction quality.