Kyleyee/Qwen2.5-1.5B-sft-hh-3e
Kyleyee/Qwen2.5-1.5B-sft-hh-3e is a 1.5 billion parameter language model, fine-tuned from the Qwen/Qwen2.5-1.5B base model. This model has been specifically trained using Supervised Fine-Tuning (SFT) on the Kyleyee/train_data_SFT_Helpful dataset, leveraging the TRL library. It is optimized for generating helpful and conversational responses, making it suitable for applications requiring instruction-following and dialogue capabilities.
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Model Overview
Kyleyee/Qwen2.5-1.5B-sft-hh-3e is a 1.5 billion parameter language model derived from the Qwen/Qwen2.5-1.5B architecture. It has been fine-tuned using Supervised Fine-Tuning (SFT) with the TRL library, specifically on the Kyleyee/train_data_SFT_Helpful dataset. This training process aims to enhance its ability to provide helpful and coherent responses, particularly in conversational contexts.
Key Capabilities
- Instruction Following: Designed to understand and respond to user prompts effectively.
- Helpful Dialogue Generation: Optimized for producing informative and relevant answers in a conversational format.
- Efficient Performance: As a 1.5 billion parameter model, it offers a balance between capability and computational efficiency.
Training Details
The model was trained using the TRL (Transformer Reinforcement Learning) framework, with specific versions of libraries including TRL 0.16.0.dev0, Transformers 4.48.3, and Pytorch 2.6.0. The training run details are available for visualization on Weights & Biases.
Good For
- Chatbots and Conversational AI: Ideal for applications requiring helpful and engaging dialogue.
- Question Answering: Can be used to generate direct and informative answers to user queries.
- Instruction-based tasks: Suitable for tasks where the model needs to follow specific instructions to produce output.