SlingshotLLM/jailbreak-qwen-7b-sft
SlingshotLLM/jailbreak-qwen-7b-sft is a 7.6 billion parameter Qwen2.5-based causal language model fine-tuned by SlingshotLLM. This model is specifically trained using Supervised Fine-Tuning (SFT) on a base from unsloth/Qwen2.5-7B-Instruct. It is designed for text generation tasks, leveraging its 131072 token context length for nuanced and extended outputs. Its primary differentiation lies in its specialized fine-tuning, making it suitable for specific conversational or generative applications.
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Model Overview
SlingshotLLM/jailbreak-qwen-7b-sft is a 7.6 billion parameter language model built upon the Qwen2.5 architecture, specifically fine-tuned from unsloth/Qwen2.5-7B-Instruct. This model has undergone Supervised Fine-Tuning (SFT) using the TRL library, indicating a focus on improving its response generation capabilities through direct instruction following.
Key Capabilities
- Instruction Following: Enhanced through SFT, the model is designed to generate text based on user prompts and instructions.
- Text Generation: Capable of producing coherent and contextually relevant text outputs.
- Extended Context: Benefits from a substantial 131072 token context window, allowing for processing and generating longer sequences of text.
Training Details
The model's training leveraged the TRL framework (version 0.23.0) alongside Transformers (4.56.2) and PyTorch (2.8.0). This setup indicates a standard and robust fine-tuning process aimed at optimizing the model's performance for generative tasks. The training procedure was tracked and visualized using Weights & Biases, suggesting a methodical approach to development.
Good For
- Conversational AI: Its fine-tuned nature makes it suitable for dialogue systems where instruction adherence is important.
- Content Creation: Can be used for generating various forms of text content, from answers to creative writing prompts.
- Research and Experimentation: Provides a base for further fine-tuning or experimentation with Qwen2.5-based models, particularly for exploring SFT effects.