activeDap/Qwen2.5-7B_ultrafeedback_chosen
activeDap/Qwen2.5-7B_ultrafeedback_chosen is a 7.6 billion parameter causal language model, fine-tuned by activeDap from the Qwen/Qwen2.5-7B base model. This model is specifically optimized using the activeDap/ultrafeedback_chosen dataset, enhancing its ability to generate high-quality, helpful responses. It is designed for supervised fine-tuning tasks, making it suitable for applications requiring refined conversational or instruction-following capabilities.
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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.