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import gradio as gr | |
from transformers import pipeline | |
from transformers import AutoModelForCausalLM, AutoTokenizer | |
model_path = "finetuned_phi2" | |
model = AutoModelForCausalLM.from_pretrained(model_path, low_cpu_mem_usage=True, trust_remote_code=True) | |
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True) | |
def generate(question, context): | |
system_message = "You are a question answering chatbot. Provide a clear and detailed explanation" | |
prompt = f"[INST] <<SYS>>\n{system_message}\n<</SYS>>\n\n {question} [/INST]" # replace the command here with something relevant to your task | |
num_new_tokens = 200 # change to the number of new tokens you want to generate | |
# Count the number of tokens in the prompt | |
num_prompt_tokens = len(tokenizer(prompt)['input_ids']) | |
# Calculate the maximum length for the generation | |
max_length = num_prompt_tokens + num_new_tokens | |
gen = pipeline('text-generation', model=model, tokenizer=tokenizer, max_length=max_length) | |
result = gen(prompt) | |
return (result[0]['generated_text'].replace(prompt, '')) | |
bbchatbot = gr.Chatbot( | |
avatar_images=["logo/user logo.png", "logo/bot logo.png"], bubble_full_width=False, show_label=False, show_copy_button=True, likeable=True,) | |
demo = gr.ChatInterface(fn=generate, | |
chatbot=bbchatbot, | |
title="🧑🏽💻Microsoft Phi2 Chatbot🤖" | |
) | |
demo.queue().launch(show_api=False) |