How to use from
vLLM
Install from pip and serve model
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "fukayatti/Phi3Mix"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "fukayatti/Phi3Mix",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'
Use Docker
docker model run hf.co/fukayatti/Phi3Mix
Quick Links

Phi3Mix

Phi3Mix is a Mixture of Experts (MoE) made with the following models using Phi3_LazyMergekit:

🧩 Configuration

base_model: microsoft/Phi-3-small-128k-instruct
gate_mode: cheap_embed
experts_per_token: 1
dtype: float16
experts:
  - source_model: microsoft/Phi-3-small-128k-instruct
    positive_prompts: ["research, logic, math, science"]
  - source_model: Rakuten/RakutenAI-7B
    positive_prompts: ["creative, art"]

πŸ’» Usage

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model = "fukayatti/Phi3Mix"

tokenizer = AutoTokenizer.from_pretrained(model)

model = AutoModelForCausalLM.from_pretrained(
    model,
    trust_remote_code=True,
)

prompt="How many continents are there?"
input = f"<|system|>You are a helpful AI assistant.<|end|><|user|>{prompt}<|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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