Welcome to your guide on merging Yi models! This article will take you step-by-step through the process of creating a model capable of handling over 40,000 tokens while improving narration through instruct-enhanced storytelling. Let’s dive in!
Understanding RPMerge
RPMerge is essentially a cooking recipe for AI models where several Yi 34B models are blended together. Imagine you’re making a smoothie, selecting only the fruits (or models) that complement each other, ensuring that the final taste is better than any single ingredient on its own. Here, the goal is to enhance context length and storytelling capabilities, much like selecting the sweetest and juiciest fruits for your blend.
Ingredients Required
To get started with the merge, you will need the following models:
- DrNicefellowChatAllInOne-Yi-34B-200K-V1
- migtisseraTess-34B-v1.5b
- cgatoThespis-34b-v0.7
- Doctor-Shotgunlimarpv3-yi-llama-34b-lora
- adamo1139yi-34b-200k-rawrr-dpo-2
- migtisseraTess-M-Creative-v1.0
- NousResearchNous-Capybara-34B
Prompt Templates
Creating effective prompts is critical. You can utilize the Orca-Vicuna system message format:
SYSTEM: system_message
USER: prompt
ASSISTANT:
Raw prompting techniques can yield great results too. Take inspiration from discussions about quality story writing on platforms like Reddit.
Tuning Your Model
For smooth operation, you will need to carefully tune the model parameters. Think of this as adjusting the heat while cooking; too high or too low can spoil the dish. The following tuning options are recommended:
- Use a lower temperature with a minimum probability (MinP) of 0.1 or higher.
- A little repetition penalty can enhance creativity.
- Mirostat settings should have a low tau.
Running Your Model Efficiently
Yi models require higher-end GPUs to function seamlessly. Here’s a practical approach to maximize performance:
- Use **exllamav2** for optimal context performance.
- Set
max_position_embeddingsinconfig.jsonto a value lower than 200,000 to avoid out-of-memory (OOM) errors.
Testing Your Merged Model
After the merge, it’s time to test! Ensure that your model can handle novel-style continuations and provide assistant-like responses effectively. If you run into refusals or errors, consider the following troubleshooting steps:
Troubleshooting Tips
- Double-check the configurations – incorrect parameters can lead to errors.
- If the model fails to perform as expected, revisit the amount and types of models in your merge.
- For persistent issues, examine your GPU settings and ensure they meet the requirements.
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.