heli-stand/Qwen2.5-7B-hh-rlhf-sft
The heli-stand/Qwen2.5-7B-hh-rlhf-sft model is a 7.6 billion parameter language model, supervised fine-tuned from the Qwen2.5-7B architecture. It has been specifically trained on the Anthropic helpfulness-harmlessness RLHF datasets, enhancing its ability to generate helpful and harmless responses. With a context length of 32768 tokens, this model is optimized for applications requiring balanced and safe conversational AI outputs.
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Model Overview
This model, heli-stand/Qwen2.5-7B-hh-rlhf-sft, is a 7.6 billion parameter language model built upon the robust Qwen2.5-7B architecture. Its primary distinction lies in its supervised fine-tuning (SFT) process, which utilized the comprehensive Anthropic helpfulness-harmlessness RLHF datasets. This specialized training aims to imbue the model with a strong foundation for generating responses that are both informative and safe.
Key Capabilities
- Helpful and Harmless Generation: Fine-tuned specifically to align with principles of helpfulness and harmlessness, making it suitable for sensitive applications.
- Qwen2.5-7B Foundation: Benefits from the strong base capabilities of the Qwen2.5-7B model, including a 32768 token context window.
Training Details
The model underwent a single epoch of training with an AdamW optimizer, a learning rate of 1e-5, and a batch size of 128. The maximum sequence length for training was 1024 tokens. Evaluation indicated a best evaluation loss of 1.678.
Good For
- Conversational AI: Ideal for chatbots and virtual assistants where safety and balanced responses are critical.
- Content Moderation: Can assist in generating or evaluating content for adherence to helpfulness and harmlessness guidelines.
- Research in Alignment: Useful for researchers exploring the impact of RLHF datasets on model behavior and safety.