Hugging Face CriteriaPO/qwen2.5-3b-sft-10 is a 3.1 billion parameter causal language model, fine-tuned from Qwen/Qwen2.5-3B using the TRL framework. This model is specifically trained with Supervised Fine-Tuning (SFT) to enhance its conversational capabilities and response generation. It is designed for general text generation tasks, offering a balance between model size and performance for various applications. The model leverages a 32K context length, making it suitable for processing longer prompts and generating coherent, extended outputs.
Model Overview
CriteriaPO/qwen2.5-3b-sft-10 is a 3.1 billion parameter language model, fine-tuned from the base Qwen/Qwen2.5-3B architecture. This model has undergone Supervised Fine-Tuning (SFT) using the TRL framework, specifically version 0.12.2, to optimize its performance for instruction-following and conversational tasks.
Key Capabilities
- Instruction Following: Enhanced ability to understand and respond to user prompts based on its SFT training.
- Text Generation: Capable of generating coherent and contextually relevant text for a variety of inputs.
- Context Handling: Supports a substantial context length of 32,768 tokens, allowing for more detailed and extended interactions.
Training Details
The model was trained using SFT, leveraging the TRL library. The training process utilized Transformers version 4.46.3, Pytorch 2.1.2+cu121, Datasets 3.1.0, and Tokenizers 0.20.3. Further details on the training run can be explored via the associated Weights & Biases project.
Good For
- General Conversational AI: Suitable for chatbots and interactive applications requiring natural language responses.
- Content Creation: Can assist in generating various forms of text, from creative writing to informative summaries.
- Prototyping: Its relatively compact size (3.1B parameters) makes it efficient for development and deployment in resource-constrained environments.