metadata

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
  - Yhyu13/phi-2-sft-dpo-gpt4_en-ep1
  - mrm8488/phi-2-coder

phixtral-4x2_8

phixtral-4x2_8 is the first Mixure of Experts (MoE) made with four microsoft/phi-2 models, inspired by the mistralai/Mixtral-8x7B-v0.1 architecture. It performs better than each individual expert.

⚡ Quantized models

GPTQ: https://huggingface.co/TheBloke/phixtral-4x2_8-GPTQ

🏆 Evaluation

The evaluation was performed using LLM AutoEval on Nous suite.

Model	AGIEval	GPT4All	TruthfulQA	Bigbench	Average
phixtral-4x2_8	33.91	70.44	48.78	37.68	47.7
dolphin-2_6-phi-2	33.12	69.85	47.39	37.2	46.89
phi-2-dpo	30.39	71.68	50.75	34.9	46.93
phi-2-sft-dpo-gpt4_en-ep1	30.61	71.13	48.74	35.23	46.43
phi-2-coder*	29.30	71.03	45.13	35.54	45.25
phi-2	27.98	70.8	44.43	35.21	44.61
* results reported by @vince62s here.

Check YALL - Yet Another LLM Leaderboard to compare it with other models.

🧩 Configuration

The model has been made with a custom version of the mergekit library (mixtral branch) and the following configuration:

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: [""]
  - source_model: Yhyu13/phi-2-sft-dpo-gpt4_en-ep1
    positive_prompts: [""]
  - source_model: mrm8488/phi-2-coder
    positive_prompts: [""]

💻 Usage

Here's a Colab notebook to run Phixtral in 4-bit precision on a free T4 GPU.

!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, you can specify the num_experts_per_tok and num_local_experts in the config.json file (2 and 4 by default). This configuration is automatically loaded in configuration.py.

vince62s implemented the MoE inference code in the modeling_phi.py file. In particular, see the MoE class.

🤝 Acknowledgments

A special thanks to 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 for the mergekit library and the implementation of the MoE for clowns.

Thanks to ehartford, lxuechen, Yhyu13, and mrm8488 for their fine-tuned phi-2 models.