EnzGamers/ABCDAI-R1-1.5b-SFT
EnzGamers/ABCDAI-R1-1.5b-SFT is a 1.5 billion parameter language model developed by EnzGamers, fine-tuned from Qwen/Qwen2.5-1.5B. This model has been trained using Supervised Fine-Tuning (SFT) with the TRL framework. It is designed for general text generation tasks, leveraging its 32768 token context length for processing longer inputs. The model provides a solid foundation for applications requiring instruction-following capabilities at a smaller scale.
Loading preview...
Model Overview
EnzGamers/ABCDAI-R1-1.5b-SFT is a 1.5 billion parameter language model, representing a fine-tuned version of the Qwen/Qwen2.5-1.5B base model. Developed by EnzGamers, this model has undergone Supervised Fine-Tuning (SFT) using the TRL library, which is a framework for Transformer Reinforcement Learning.
Key Capabilities
- Instruction Following: The model is fine-tuned to respond to user prompts, making it suitable for conversational AI and question-answering tasks.
- Text Generation: Capable of generating coherent and contextually relevant text based on given inputs.
- Efficient Deployment: With 1.5 billion parameters, it offers a balance between performance and computational efficiency, making it accessible for various applications.
- Extended Context Window: Benefits from a 32768 token context length, allowing it to process and generate longer sequences of text.
Training Details
This model was trained using the SFT method, leveraging specific versions of popular machine learning frameworks:
- TRL: 1.8.0
- Transformers: 5.13.1
- Pytorch: 2.10.0+cu128
- Datasets: 5.0.0
- Tokenizers: 0.22.2
Use Cases
- General-purpose text generation: Ideal for tasks like creative writing, content creation, and summarization.
- Chatbots and conversational agents: Can be integrated into applications requiring interactive dialogue.
- Prototyping and experimentation: Its smaller size makes it a good candidate for rapid development and testing of LLM-powered features.