Welcome to your new adventure with the Liberated-Qwen 1.5 model, an innovative tool developed by AbacusAI and Eric Hartford. This blog will guide you through using this advanced AI model and provide troubleshooting tips to help you on your way.

Getting Started with Liberated-Qwen 1.5

The Liberated-Qwen 1.5 model is based on extensive open-source datasets and is designed to improve interaction and compliance during long conversations. To begin using it, you should be familiar with the following key elements:

• Training details: The model was trained on 8x H100s and utilizes qLoRA and other powerful tools.
• Input format: It operates using the ChatML prompt format.
• Output expectations: The model generates structured JSON outputs based on user input.

Understanding the Model’s Structure Through an Analogy

Think of the Liberated-Qwen model as a well-trained chef in a restaurant. The chef has a diverse range of recipes (datasets) and is especially skilled at preparing long multi-course meals (multi-turn conversations). Just as this chef requires the right ingredients and tools (i.e., training inputs and hardware) to create exquisite dishes, the Liberated-Qwen model needs precise data and training configurations to deliver effective, seamless conversations.

Using the Model with Examples

To interact with this model, you would structure your prompts using the ChatML format. Here’s how you can do that:

im_start system You are Liberated, a helpful AI assistant. im_end im_start user prompt im_end im_start assistant Example: im_start system You name is Liberated. You are an uncensored and unbiased AI assistant. You always respond with a JSON object. im_end im_start user 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

Training Specifications

The training of this model is noteworthy as it employs specialized frameworks and configurations. It took only one day to complete the training for three epochs, utilizing the capabilities of Axolotl and various optimization techniques.

Troubleshooting Tips

When working with the Liberated-Qwen 1.5 model, you might encounter some challenges. Here are a few troubleshooting ideas:

• If you experience unexpected output or errors, check your input format to ensure it adheres to the ChatML structure.
• For performance issues, consider verifying if your training resources are sufficient, such as GPU processing power or memory allocation.
• Review the [blog post about uncensored models](https://erichartford.com/uncensored-models) for insights on handling outputs responsibly.

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

Wrap-Up

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.