MixtureOfPhi3
MixtureOfPhi3 is a Mixure of Experts (MoE) made with the following models using mergekit:
This has been created using LazyMergekit-Phi3
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 where I adapted the inference code to Phi3's architecture.
- mergekit code which I tweaked to merge Phi3s
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.
Try your own in the link above !
🧩 Configuration
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
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]))