---
license: apache-2.0
tags:
- moe
- frankenmoe
- merge
- mergekit
- lazymergekit
- microsoft/Phi-3-mini-128k-instruct
base_model:
- microsoft/Phi-3-mini-128k-instruct
- microsoft/Phi-3-mini-128k-instruct
---


# MixtureOfPhi3

<p align="center">
<img src="https://substackcdn.com/image/fetch/f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F11201acc-4089-416d-921b-cbd71fbf8ddb_1024x1024.jpeg" width="300" class="center"/>
</p>


**MixtureOfPhi3** is a Mixure of Experts (MoE) made with the following models using mergekit:
* [Phi-3-mini-128k-instruct](https://huggingface.co/microsoft/Phi-3-mini-128k-instruct)
* [Phi-3-mini-128k-instruct](https://huggingface.co/microsoft/Phi-3-mini-128k-instruct)

This run is only for development purposes, since merging 2 identical models does not bring any performance benefits, but once specialized finetunes of Phi3 models will be available, it will be a starting point for creating MoE from them.

## ©️ Credits
* [mlabonne's phixtral](https://huggingface.co/mlabonne/phixtral-4x2_8) where I adapted the inference code to Phi3's architecture.
* [mergekit](https://github.com/cg123/mergekit) code which I tweaked to merge Phi3s

All of these will be published once I get a chance to refactor them to work flawlessly.

These have been merged using `cheap_embed` where each model is assigned a vector representation of words - such as experts for scientific work, reasoning, math etc.

  

## 🧩 Configuration

```yaml
base_model: microsoft/Phi-3-mini-128k-instruct
gate_mode: cheap_embed
dtype: float16
experts:
  - source_model: microsoft/Phi-3-mini-128k-instruct
    positive_prompts: ["research, logic, math, science"]
  - source_model: microsoft/Phi-3-mini-128k-instruct
    positive_prompts: ["creative, art"]
```

## 💻 Usage

```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer


model = "paulilioaica/MixtureOfPhi3"

tokenizer = AutoTokenizer.from_pretrained(model)
model = AutoModelForCausalLM.from_pretrained(
    model, 
    trust_remote_code=True, 
)

prompt="How many continents are there?"
input = f"<|system|>\nYou are a helpful AI assistant.<|end|>\n<|user|>{prompt}\n<|assistant|>"
tokenized_input = tokenizer.encode(input, return_tensors="pt")

outputs = model.generate(tokenized_input, max_new_tokens=128, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(tokenizer.decode(outputs[0]))
```