HoangTran223/SFT_5e-5_Qwen2.5-1.5B_Ultrafb_2e
HoangTran223/SFT_5e-5_Qwen2.5-1.5B_Ultrafb_2e is a 1.5 billion parameter language model, fine-tuned using Supervised Fine-Tuning (SFT) on an unspecified base model, likely from the Qwen2.5 family. This model is designed for general text generation tasks, leveraging its compact size and a substantial 32768-token context length for efficient processing. Its fine-tuned nature suggests an optimization for specific conversational or instruction-following applications, making it suitable for deployment in resource-constrained environments.
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Model Overview
HoangTran223/SFT_5e-5_Qwen2.5-1.5B_Ultrafb_2e is a 1.5 billion parameter language model, fine-tuned using Supervised Fine-Tuning (SFT). While the specific base model is not explicitly stated, its naming convention suggests a foundation in the Qwen2.5 architecture. This model was trained using the TRL (Transformers Reinforcement Learning) library, indicating a focus on optimizing its performance for specific tasks through fine-tuning.
Key Capabilities
- Text Generation: Capable of generating coherent and contextually relevant text based on given prompts.
- Instruction Following: The SFT training process typically enhances the model's ability to follow instructions and respond to user queries effectively.
- Efficient Deployment: With 1.5 billion parameters, it offers a balance between performance and computational efficiency, making it suitable for applications where larger models might be impractical.
- Large Context Window: Features a substantial context length of 32768 tokens, allowing it to process and generate text based on extensive input histories.
Training Details
The model was trained using the SFT method, leveraging the TRL library (version 1.1.0). The training environment included Transformers version 4.57.6, Pytorch 2.10.0, Datasets 4.8.4, and Tokenizers 0.22.2. Further details on the specific dataset used for fine-tuning are not provided in the model card.