---
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
## This is meant to be finetuned

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


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.


## 🧩 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 AutoConfig, AutoModelForCausalLM, AutoTokenizer
import torch

model_name = "paulilioaica/PhiMiX-2x2B"

torch.set_default_device("cuda")

config = AutoConfig.from_pretrained(model_name, trust_remote_code=True)
model = AutoModelForCausalLM.from_config(config, trust_remote_code=True)

instruction = '''
    def print_prime(n):
        """
        Print all primes between 1 and n
        """
'''


tokenizer = AutoTokenizer.from_pretrained(
    f"{model_name}", 
    trust_remote_code=True
)

# Tokenize the input string
inputs = tokenizer(
    instruction, 
    return_tensors="pt", 
    return_attention_mask=False
)

# Generate text using the model
outputs = model.generate(**inputs, max_length=200)

# Decode and print the output
text = tokenizer.batch_decode(outputs)[0]
print(text)

```