CharlesLi/llama_2_rlhf_safe_4o_default_100_full
The CharlesLi/llama_2_rlhf_safe_4o_default_100_full model is a 7 billion parameter Llama-2-7b-chat-hf variant, fine-tuned by CharlesLi for safety-aligned chat applications. This model leverages Reinforcement Learning from Human Feedback (RLHF) to enhance its conversational safety and adherence to desired behavioral norms. It is specifically designed for use cases requiring a robust and safety-conscious conversational AI.
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Model Overview
The CharlesLi/llama_2_rlhf_safe_4o_default_100_full model is a fine-tuned version of Meta's Llama-2-7b-chat-hf, specifically adapted for safety-aligned conversational tasks. Developed by CharlesLi, this 7 billion parameter model has undergone Reinforcement Learning from Human Feedback (RLHF) to improve its responses in chat environments, focusing on generating safer and more appropriate outputs.
Key Characteristics
- Base Model: Built upon the robust
meta-llama/Llama-2-7b-chat-hfarchitecture. - Fine-tuning: Utilizes a generator dataset with RLHF for enhanced safety and alignment.
- Parameter Count: Features 7 billion parameters, offering a balance between performance and computational efficiency.
- Context Length: Supports a context window of 4096 tokens.
- Training Details: Trained with a learning rate of 2e-05, a batch size of 32 (total), and a cosine learning rate scheduler over 1 epoch.
Intended Use Cases
This model is particularly well-suited for applications where:
- Safety is paramount: Designed for chat applications requiring a strong emphasis on safe and aligned responses.
- Conversational AI: Ideal for chatbots, virtual assistants, and interactive dialogue systems.
- Llama-2 ecosystem integration: Benefits from the established capabilities and community support of the Llama-2 family.