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---
inference: false
license: mit
license_link: https://huggingface.co/microsoft/phi-2/resolve/main/LICENSE
language:
- en
pipeline_tag: text-generation
tags:
- moe
- nlp
- code
- cognitivecomputations/dolphin-2_6-phi-2
- lxuechen/phi-2-dpo
---
# phixtral-2x2_8
phixtral-2x2_8 is the first Mixure of Experts (MoE) made with two [microsoft/phi-2](https://huggingface.co/microsoft/phi-2) models, inspired by the [mistralai/Mixtral-8x7B-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-v0.1) architecture. It performs better than each individual expert.
## 🏆 Evaluation
| Model |AGIEval|GPT4All|TruthfulQA|Bigbench|Average|
|----------------------------------------------------------------|------:|------:|---------:|-------:|------:|
|[**phixtral-2x2_8**](https://huggingface.co/mlabonne/phixtral-2x2_8)| **34.1**| **70.44**| **48.78**| **37.82**| **47.78**|
|[dolphin-2_6-phi-2](https://huggingface.co/cognitivecomputations/dolphin-2_6-phi-2)| 33.12| 69.85| 47.39| 37.2| 46.89|
|[phi-2-dpo](https://huggingface.co/lxuechen/phi-2-dpo)| 30.39| 71.68| 50.75| 34.9| 46.93|
|[phi-2-sft-dpo-gpt4_en-ep1](https://huggingface.co/Yhyu13/phi-2-sft-dpo-gpt4_en-ep1)| 30.61| 71.13| 48.74| 35.23| 46.43|
|[phi-2](https://huggingface.co/microsoft/phi-2)| 27.98| 70.8| 44.43| 35.21| 44.61|
Check [YALL - Yet Another LLM Leaderboard](https://huggingface.co/spaces/mlabonne/Yet_Another_LLM_Leaderboard) to compare it with other models.
## 🧩 Configuration
The model has been made with a custom version of the [mergekit](https://github.com/cg123/mergekit) library (mixtral branch) and the following configuration:
```yaml
base_model: cognitivecomputations/dolphin-2_6-phi-2
gate_mode: cheap_embed
experts:
- source_model: cognitivecomputations/dolphin-2_6-phi-2
positive_prompts: [""]
- source_model: lxuechen/phi-2-dpo
positive_prompts: [""]
```
## 💻 Usage
Here's a [Colab notebook](https://colab.research.google.com/drive/1k6C_oJfEKUq0mtuWKisvoeMHxTcIxWRa?usp=sharing) to run Phixtral in 4-bit precision on a free T4 GPU.
```python
!pip install -q --upgrade transformers einops accelerate bitsandbytes
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "phixtral-4x2_8"
instruction = '''
def print_prime(n):
"""
Print all primes between 1 and n
"""
'''
torch.set_default_device("cuda")
# Load the model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
f"mlabonne/{model_name}",
torch_dtype="auto",
load_in_4bit=True,
trust_remote_code=True
)
tokenizer = AutoTokenizer.from_pretrained(
f"mlabonne/{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)
```
Inspired by [mistralai/Mixtral-8x7B-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-v0.1), you can specify the `num_experts_per_tok` and `num_local_experts` in the [`config.json`](https://huggingface.co/mlabonne/phixtral-2x2_8/blob/main/config.json#L26-L27) file (2 for both by default). This configuration is automatically loaded in `configuration.py`.
[vince62s](https://huggingface.co/vince62s) implemented the MoE inference code in the `modeling_phi.py` file. In particular, see the [MoE class](https://huggingface.co/mlabonne/phixtral-2x2_8/blob/main/modeling_phi.py#L293-L317).
## 🤝 Acknowledgments
A special thanks to [vince62s](https://huggingface.co/vince62s) for the inference code and the dynamic configuration of the number of experts. He was very patient and helped me to debug everything.
Thanks to [Charles Goddard](https://github.com/cg123) for the [mergekit](https://github.com/cg123/mergekit) library and the implementation of the [MoE for clowns](https://goddard.blog/posts/clown-moe/).
Thanks to [ehartford](https://huggingface.co/ehartford) and [lxuechen](https://huggingface.co/lxuechen) for their fine-tuned phi-2 models. |