jackf857/qwen3-8b-base-beta-dpo-hh-harmless-4xh200-batch-64
The jackf857/qwen3-8b-base-beta-dpo-hh-harmless-4xh200-batch-64 is an 8 billion parameter language model, fine-tuned from a Qwen3-8B-base variant on the Anthropic/hh-rlhf dataset. This model has a context length of 32768 tokens and is specifically optimized for harmlessness and alignment through Beta DPO training. It is intended for applications requiring robust and safe conversational AI outputs.
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Model Overview
This model, jackf857/qwen3-8b-base-beta-dpo-hh-harmless-4xh200-batch-64, is an 8 billion parameter language model derived from a Qwen3-8B-base architecture. It has been fine-tuned using the Anthropic/hh-rlhf dataset with a Beta DPO (Direct Preference Optimization) training procedure, specifically targeting improved harmlessness and alignment.
Key Characteristics
- Base Model: Fine-tuned from
/scratch/qu.yang1/dynamic-dpo-v4/outputs/qwen3-8b-base-sft-hh-helpful-4xh200-batch-64-20260417-214452. - Training Objective: Optimized for harmlessness and alignment using the Anthropic/hh-rlhf dataset.
- Training Method: Utilizes Beta DPO, achieving a final Beta Dpo/beta of 0.1628 and a loss of 0.6518 on the evaluation set.
- Context Length: Supports a context window of 32768 tokens.
Intended Use Cases
This model is particularly suited for applications where generating safe, non-toxic, and aligned responses is critical. Its fine-tuning on the Anthropic/hh-rlhf dataset suggests a strong focus on reducing harmful outputs, making it a candidate for:
- Safe conversational agents: Developing chatbots or virtual assistants that prioritize harmless interactions.
- Content moderation: Assisting in filtering or generating content that adheres to safety guidelines.
- Research in alignment: Exploring the effects of DPO on model behavior and safety.