NLP-Final-Project/qwen2.5-7b-instruct-bbq-age-sft
NLP-Final-Project/qwen2.5-7b-instruct-bbq-age-sft is a 7.6 billion parameter instruction-tuned causal language model, fine-tuned from Qwen/Qwen2.5-7B-Instruct. This model was trained using Supervised Fine-Tuning (SFT) with the TRL framework. It is designed for general instruction-following tasks, leveraging its 32768-token context length for comprehensive understanding and generation.
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Model Overview
This model, NLP-Final-Project/qwen2.5-7b-instruct-bbq-age-sft, is a 7.6 billion parameter instruction-tuned language model. It is built upon the robust architecture of Qwen/Qwen2.5-7B-Instruct, a model developed by Qwen. The fine-tuning process utilized the TRL library for Supervised Fine-Tuning (SFT).
Key Capabilities
- Instruction Following: Designed to accurately follow user instructions, making it suitable for a wide range of conversational and task-oriented applications.
- Context Handling: Benefits from the base model's 32768-token context length, allowing it to process and generate longer, more coherent responses.
- General Purpose: As an instruction-tuned model, it is versatile for various natural language understanding and generation tasks.
Training Details
The model was trained using the Supervised Fine-Tuning (SFT) method, leveraging the TRL framework (version 1.3.0). The training environment included Transformers 5.8.0, Pytorch 2.11.0, Datasets 4.8.5, and Tokenizers 0.22.2.
Usage
Developers can quickly integrate this model using the Hugging Face transformers library, as demonstrated in the provided quick start example for text generation tasks.