If you’re venturing into the world of AI development, particularly with large language models (LLMs), you’ve stumbled upon a fascinating process: merging models for enhanced performance. In this guide, we’ll explore how to merge models such as Llama-3-15B using a custom NearSwap algorithm, specifically fine-tuned to your requirements.
Understanding the Model Merge
The merge we’re discussing involves a blend of models to create an even more powerful AI. Think of it like mixing two colors of paint to create a new hue; you get the best qualities of both in a new palette! In this case, we are combining:
By utilizing the NearSwap algorithm (inverted at t=0.0001), we ensure a unique outcome compared to other models like “L3-15B-EtherealMaid-t0.0001”.
The NearSwap Algorithm
The key to this technique lies in the NearSwap algorithm that functions like an expert chef adjusting a recipe to taste. The fundamental code looks like this:
#Fixed
def lerp(a, b, t):
return a * (1 - t) + b * t
def nearswap(v0, v1, t):
lweight = np.abs(v0 - v1)
with np.errstate(divide='ignore', invalid='ignore'):
lweight = np.where(lweight != 0, t / lweight, 1.0)
lweight = np.nan_to_num(lweight, nan=1.0, posinf=1.0, neginf=1.0)
np.clip(lweight, a_min=0.0, a_max=1.0, out=lweight)
return lerp(v0, v1, lweight)
In the analogy of a chef, the lerp function represents a mixer creating a delightful blend from the raw ingredients (the values from the two models). The nearswap function is the kitchen’s expert that checks the consistency of the blend, ensuring it’s neither too thick nor too thin before serving—perfecting the model’s performance.
Tuning Your Model
Once the merge is complete, it’s essential to tune the parameters to achieve optimal performance. Here’s a list of successful sampler settings:
- Temperature: 0.9-1.2
- Min P: 0.08
- TFS: 0.97
- Smoothing Factor: 0.3
- Smoothing Curve: 1.1
For even more coherent outputs, consider using the Nymeria preset:
- Temperature: 0.9
- Top K: 30
- Top P: 0.75
- Min P: 0.2
- Rep Pen: 1.1
- Smooth Factor: 0.25
- Smooth Curve: 1
Creating the Prompt Template
A well-structured prompt template is crucial for guiding the AI’s responses. Here’s a straightforward template to get you started:
```bash
<|begin_of_text|><|start_header_id|>system<|end_header_id|>{system_prompt}<|eot_id|><|start_header_id|>user<|end_header_id|>{input}<|eot_id|><|start_header_id|>assistant<|end_header_id|>{output}<|eot_id|>
```Troubleshooting
If you encounter any issues while merging or tuning your models, here are a few troubleshooting ideas:
- Ensure all dependencies are correctly installed and up to date.
- Double-check your parameters for compatibility with the chosen model configuration.
- Experiment with different values for the smoothing factors and temperature settings to find what works best.
- If you experience poor model performance, review your merging method and consider alternative approaches based on the results.
For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.
Conclusion
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.
