---
license: apache-2.0
tags:
- moe
- frankenmoe
- merge
- mergekit
- lazymergekit
- cognitivecomputations/dolphin-2_6-phi-2
- rhysjones/phi-2-orange
base_model:
- cognitivecomputations/dolphin-2_6-phi-2
- rhysjones/phi-2-orange
---

# PhiMiX-2x2B


## Code is work in progress

<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="500" class="center"/>

PhiMiX-2x2B is a Mixure of Experts (MoE) made with the following models using mergekit:
* [cognitivecomputations/dolphin-2_6-phi-2](https://huggingface.co/cognitivecomputations/dolphin-2_6-phi-2)
* [rhysjones/phi-2-orange](https://huggingface.co/rhysjones/phi-2-orange)


## ©️ Credits
* [mlabonne's phixtral](https://huggingface.co/mlabonne/phixtral-4x2_8) for the PhiConfig and inference code.
* [mergekit](https://github.com/cg123/mergekit) code which I tweaked (you can find the PhiConfig [here](https://github.com/cg123/mergekit/blob/508348ae34be17ea0a95d0a288a6e34491a2558a/mergekit/architecture.py#L289))
by mainly adding the config in the `moe_mixtral.py` script from `mixtral` branch.
* artwork by 


## 🧩 Configuration

```yaml
base_model: rhysjones/phi-2-orange
gate_mode: random
dtype: float16
experts:
  - source_model: cognitivecomputations/dolphin-2_6-phi-2
    positive_prompts: [""]
  - source_model: rhysjones/phi-2-orange
    positive_prompts: [""]
```

## 💻 Usage

```python
!pip install -qU transformers bitsandbytes accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "paulilioaica/PhiMiX-2x2B"

tokenizer = AutoTokenizer.from_pretrained(model)
pipeline = transformers.pipeline(
    "text-generation",
    model=model,
    model_kwargs={"torch_dtype": torch.float16, "load_in_4bit": True},
)

messages = [{"role": "user", "content": "Explain what a Mixture of Experts is in less than 100 words."}]
prompt = pipeline.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
```