kmseong/llama2_7b_chat-WaRP-SN-Tune-lr7e-5
TEXT GENERATIONConcurrency Cost:1Model Size:7BQuant:FP8Ctx Length:4kPublished:Apr 24, 2026License:llama3.2Architecture:Transformer Warm
The kmseong/llama2_7b_chat-WaRP-SN-Tune-lr7e-5 is a 7 billion parameter Llama 2 Chat-based model, developed by kmseong, that incorporates a Weight space Rotation Process (WaRP) for safety alignment. This model applies WaRP to the attention (q, k, v) and MLP (up, down) layers, followed by non-freeze training. It is designed for chat applications, leveraging its 4096-token context length and specific safety alignment techniques.
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Model Overview
The kmseong/llama2_7b_chat-WaRP-SN-Tune-lr7e-5 is a 7 billion parameter language model built upon the Llama 2 Chat architecture. Developed by kmseong, this model integrates a novel Weight space Rotation Process (WaRP) for enhanced safety alignment.
Key Characteristics
- Architecture: Based on the Llama 2 Chat model, providing a strong foundation for conversational AI.
- Parameter Count: Features 7 billion parameters, balancing performance with computational efficiency.
- Context Length: Supports a context window of 4096 tokens, suitable for engaging in moderately long conversations.
- Safety Alignment: Utilizes the Weight space Rotation Process (WaRP) applied to specific layers:
- Attention mechanism (query, key, value projections)
- Multi-Layer Perceptron (MLP) up and down projection layers
- Training Method: Involves a non-freeze training approach after the application of WaRP, allowing for further adaptation and refinement.
Potential Use Cases
- Safe Chatbots: Ideal for developing conversational agents where safety and alignment are critical.
- Content Moderation: Can be explored for tasks requiring nuanced understanding of potentially harmful content due to its safety-focused fine-tuning.
- Research in Alignment: Serves as a valuable model for researchers studying weight space manipulation and safety alignment techniques in large language models.