Model Overview
W-61/llama-3-8b-base-sft-hh-harmless-8xh200 is an 8 billion parameter language model derived from the Meta-Llama-3-8B architecture. This model has undergone a specific fine-tuning process using the Anthropic/hh-rlhf dataset, which is known for its focus on training models to be helpful and harmless.
Key Characteristics
- Base Model: Meta-Llama-3-8B, providing a strong foundation for general language understanding and generation.
- Fine-tuning Objective: Enhanced safety and helpfulness, achieved through training on the Anthropic/hh-rlhf dataset.
- Parameter Count: 8 billion parameters, offering a balance between performance and computational efficiency.
- Context Length: Supports an 8192-token context window.
Training Details
The model was trained for 1 epoch with a learning rate of 2e-05 and a total batch size of 128 across 8 GPUs. The training process resulted in a validation loss of 1.5660.
Intended Use Cases
This model is particularly well-suited for applications where generating harmless and helpful responses is critical. It can be considered for use in:
- Safe conversational AI: Chatbots or virtual assistants requiring ethical and non-toxic interactions.
- Content moderation: Assisting in filtering or generating content that adheres to safety guidelines.
- Educational tools: Providing informative and appropriate responses in learning environments.