Model Overview

activeDap/Qwen2.5-7B_ultrafeedback_chosen is a 7.6 billion parameter language model derived from the Qwen/Qwen2.5-7B base model. It has undergone supervised fine-tuning (SFT) using the activeDap/ultrafeedback_chosen dataset, which is designed to improve response quality through high-quality feedback.

Key Capabilities

• Enhanced Response Quality: Fine-tuned on a curated feedback dataset, aiming for more helpful and accurate outputs.
• Instruction Following: Optimized for prompt-completion tasks, particularly where assistant-like responses are desired.
• Causal Language Modeling: Inherits the strong generative capabilities of the Qwen2.5-7B architecture.

Training Details

The model was trained for 816 steps with a final training loss of 1.2449. Key training parameters include a per-device batch size of 16, a total batch size of 64 (across 4 GPUs), and a learning rate of 2e-05. The training utilized the Transformers and TRL libraries, focusing on an Assistant-only loss for fine-tuning.

Good For

• Applications requiring a 7B-class model with improved conversational quality.
• Developing chatbots or virtual assistants where response helpfulness is critical.
• Further fine-tuning on domain-specific datasets that benefit from a strong instruction-following base.