Instructions to use paulilioaica/MixtureOfPhi3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use paulilioaica/MixtureOfPhi3 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="paulilioaica/MixtureOfPhi3", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("paulilioaica/MixtureOfPhi3", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("paulilioaica/MixtureOfPhi3", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use paulilioaica/MixtureOfPhi3 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "paulilioaica/MixtureOfPhi3"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "paulilioaica/MixtureOfPhi3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/paulilioaica/MixtureOfPhi3

SGLang

How to use paulilioaica/MixtureOfPhi3 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "paulilioaica/MixtureOfPhi3" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "paulilioaica/MixtureOfPhi3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "paulilioaica/MixtureOfPhi3" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "paulilioaica/MixtureOfPhi3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use paulilioaica/MixtureOfPhi3 with Docker Model Runner:
```
docker model run hf.co/paulilioaica/MixtureOfPhi3
```

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]))

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Safetensors

Model size

6B params

Tensor type

F16

Model tree for paulilioaica/MixtureOfPhi3

Base model

microsoft/Phi-3-mini-128k-instruct

Finetuned

(44)

this model