W-61/llama3-hh-helpful-qt045-b0p3-20260429-085449
W-61/llama3-hh-helpful-qt045-b0p3-20260429-085449 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 designed for helpful conversational AI, leveraging its 8192 token context length to process and generate relevant responses. It is optimized for tasks requiring helpful and harmless dialogue, building upon the Llama 3 architecture.
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Overview
This model, llama3-hh-helpful-qt045-b0p3-20260429-085449, is an 8 billion parameter language model developed by W-61. It is a fine-tuned variant of the W-61/llama-3-8b-base-sft-hh-helpful-4xh200 base model, specifically trained using the Anthropic/hh-rlhf dataset. The training process involved a single epoch with a learning rate of 5e-07, a total batch size of 64, and utilized a cosine learning rate scheduler with a 0.1 warmup ratio. The model was trained using Transformers 4.51.0 and Pytorch 2.3.1+cu121.
Key Capabilities
- Helpful Dialogue Generation: Fine-tuned on the Anthropic/hh-rlhf dataset, indicating an optimization for generating helpful and harmless responses in conversational contexts.
- Llama 3 Architecture: Benefits from the underlying Llama 3 architecture, providing a strong foundation for language understanding and generation.
- 8192 Token Context Window: Supports processing and generating longer sequences of text, suitable for more complex interactions.
Good For
- Conversational AI: Ideal for applications requiring models to provide helpful and safe responses.
- Dialogue Systems: Can be integrated into chatbots or virtual assistants where adherence to helpfulness and harmlessness principles is crucial.
- Research in RLHF: Provides a fine-tuned model for further experimentation and development in reinforcement learning from human feedback.