JayHyeon/Qwen2.5-0.5B-SFT-1e-4-3ep
JayHyeon/Qwen2.5-0.5B-SFT-1e-4-3ep is a 0.5 billion parameter language model, fine-tuned from the Qwen2.5-0.5B base model. Developed by JayHyeon, it was trained using Supervised Fine-Tuning (SFT) on the HuggingFaceH4/ultrafeedback_binarized dataset. This model is designed for general text generation tasks, leveraging its instruction-tuned capabilities for conversational AI and response generation.
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Model Overview
This model, JayHyeon/Qwen2.5-0.5B-SFT-1e-4-3ep, is a 0.5 billion parameter language model built upon the Qwen2.5-0.5B architecture. It has been specifically fine-tuned using Supervised Fine-Tuning (SFT) on the HuggingFaceH4/ultrafeedback_binarized dataset, a common dataset for instruction-tuning models to improve their conversational abilities and alignment.
Key Capabilities
- Instruction Following: Enhanced ability to follow user instructions due to SFT on a feedback dataset.
- Text Generation: Capable of generating coherent and contextually relevant text based on prompts.
- Conversational AI: Optimized for dialogue and response generation, making it suitable for chatbot-like applications.
Training Details
The model was trained using the TRL (Transformer Reinforcement Learning) library, specifically with SFT. This process involved using a learning rate of 1e-4 over 3 epochs. The base model, Qwen2.5-0.5B, provides a robust foundation with a context length of 32768 tokens, which is maintained in this fine-tuned version.
Good For
- Quick Prototyping: Its smaller size (0.5B parameters) makes it efficient for rapid experimentation and deployment.
- Instruction-based Tasks: Ideal for applications requiring the model to respond to specific instructions or prompts.
- Educational or Research Purposes: A good candidate for exploring SFT techniques on a compact, yet capable, model.