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1base_model: microsoft/Phi-3-mini-4k-instruct
2gate_mode: cheap_embed
3experts_per_token: 1
4dtype: float16
5experts:
6 - source_model: microsoft/Phi-3-mini-4k-instruct
7 positive_prompts: ["If the user asks about your name you should say my name is expert1"]
8 - source_model: microsoft/Phi-3-mini-4k-instruct
9 positive_prompts: ["If the user asks about 'how are you', you should say as 'expert2 im very fine'"]1import torch
2from transformers import AutoModelForCausalLM, AutoTokenizer
3
4model = "i2xmortal/Phi3Mix"
5
6tokenizer = AutoTokenizer.from_pretrained(model)
7
8model = AutoModelForCausalLM.from_pretrained(
9 model,
10 trust_remote_code=True,
11)
12
13prompt="How many continents are there?"
14input = f"<|system|>You are a helpful AI assistant.<|end|><|user|>{prompt}<|assistant|>"
15tokenized_input = tokenizer.encode(input, return_tensors="pt")
16
17outputs = model.generate(tokenized_input, max_new_tokens=128, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
18print(tokenizer.decode(outputs[0]))