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import torch | |
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline | |
import gradio as gr | |
torch.random.manual_seed(0) | |
model = AutoModelForCausalLM.from_pretrained( | |
"microsoft/Phi-3-mini-128k-instruct", | |
device_map="auto", | |
torch_dtype="auto", | |
trust_remote_code=True, | |
low_cpu_mem_usage=True, | |
) | |
tokenizer = AutoTokenizer.from_pretrained("microsoft/Phi-3-mini-128k-instruct") | |
messages = [ | |
{"role": "user", "content": "Can you provide ways to eat combinations of bananas and dragonfruits?"}, | |
{"role": "assistant", "content": "Sure! Here are some ways to eat bananas and dragonfruits together: 1. Banana and dragonfruit smoothie: Blend bananas and dragonfruits together with some milk and honey. 2. Banana and dragonfruit salad: Mix sliced bananas and dragonfruits together with some lemon juice and honey."}, | |
{"role": "user", "content": "What about solving an 2x + 3 = 7 equation?"}, | |
] | |
pipe = pipeline( | |
"text-generation", | |
model=model, | |
tokenizer=tokenizer, | |
) | |
generation_args = { | |
"max_new_tokens": 256, | |
"return_full_text": False, | |
"temperature": 0.2, | |
"do_sample": True, | |
} | |
def phi3_fun(message,chat_history): | |
messages=[ | |
{"role": "user", "content": message}, | |
] | |
output = pipe(messages, **generation_args) | |
respond = output[0]['generated_text'] | |
return respond | |
title = "Phi-3 " | |
examples = [ | |
'How are You?', | |
"talk about your self", | |
] | |
gr.ChatInterface( | |
fn=phi3_fun, | |
title =title, | |
examples = examples | |
).launch(debug=True) | |
# demo = gr.Interface(fn=phi3_fun, inputs="text", outputs="text",title =title, | |
# examples = examples) | |
# demo.launch() | |
# output = pipe(messages, **generation_args) | |
# print(output[0]['generated_text']) | |