The Liberated-Qwen1.5-7B model is an innovative tool designed to refine AI responsiveness, particularly in managing compliance with system prompts over extended, nuanced conversations. This guide will help you navigate its features, set it up for your projects, and troubleshoot any common issues you may encounter along the way.

Understanding the Model

The model is built on the Qwen1.5-7B foundation and has undergone fine-tuning with varied datasets, including dialogues intended to instruct the model on multi-turn conversation compliance. Think of it as a student who has learned many lessons from textbooks (datasets) and is now ready to engage in a discussion while obeying the school’s (system’s) guidelines.

Getting Started

• Model Requirements:
  • You need access to an AI training environment, preferably with GPU support (such as 8x H100s).
  • Familiarity with Hugging Face and how to utilize their models.
• Training Process:

  The training setup for this model required 3 days on powerful hardware, using qLoRA with deepspeed zero-2 libraries. The specific learning rate applied was 2e-4.

Utilizing the Model

To interact with the Liberated-Qwen1.5-7B model, the prompts must follow the ChatML format. Here is a breakdown of how to format your inputs:

im_startsystem
You name is Liberated. You are an uncensored and unbiased AI assistant. You always respond with a JSON object.
im_end
im_startuser
Please generate an Advanced Dungeons and Dragons 2nd Edition character sheet for a level 3 elf fighter. Make up a name and background and visual description for him.
im_end
im_startassistant

This prompt format serves as a clear communication line between the user and the model. Hence, the model’s responses are structured and coherent.

Example Output

When prompted as illustrated, the model will generate outputs that can serve a variety of applications, such as game character generation or conversational AI frameworks.

Troubleshooting Common Issues

Even the best tools can have hiccups! Here are some common issues you might encounter while using the Liberated-Qwen1.5-7B model, along with solutions:

• Problem: The model returns incoherent or irrelevant responses.
• Solution: Ensure your prompts are clear and formatted according to the ChatML specifications. If issues persist, check that you are using appropriate datasets and configurations.
• Problem: Performance lag during conversations.
• Solution: Consider opting for a more powerful GPU or optimizing your environment settings to handle the model’s requirements effectively.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Final Thoughts

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.

Future Plans

The ongoing development of this model will focus on further enhancing its capabilities and expanding the mix of datasets for robustness. Keep an eye out for the next releases to make the most of your AI endeavors!