README.md

---
license: mit
language:
- en
---
A Moe model built on top of microsoft/phi-2, g-ronimo/phi-2-OpenHermes-2.5 and mlx-community/phi-2-dpo-7k, random init gates weights


## Example
```
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
import torch

DEV = torch.device('cuda' if torch.cuda.is_available() else 'cpu')


model_name_or_path = "mzbac/phi2-2x3"

model = AutoModelForCausalLM.from_pretrained(model_name_or_path,
                                             trust_remote_code=True,
                                            torch_dtype=torch.bfloat16,
                                             )
model.to(DEV)
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True)

prompt = "Instruct: how backpropagation works.\nOutput:"

print("\n\n*** Generate:")

inputs = tokenizer.encode(prompt, return_tensors="pt").to(DEV)

generate_kwargs = dict(
    input_ids=inputs,
    temperature=0.3, 
    max_new_tokens=500,
    do_sample=True,
)

outputs = model.generate(**generate_kwargs)
print(tokenizer.decode(outputs[0]))
```