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from time import perf_counter
import streamlit as st
from transformers import pipeline, AutoModelForSequenceClassification, AutoTokenizer, GenerationConfig
def formatted_prompt(input)-> str:
return f"<|im_start|>user\n{input}<|im_end|>\n<|im_start|>assistant:"
def generate_response(user_input):
prompt = formatted_prompt(user_input)
inputs = tokenizer([prompt], return_tensors="pt")
generation_config = GenerationConfig(
penalty_alpha=0.6,
do_sample=True,
top_k=5,
temperature=0.5,
repetition_penalty=1.2,
max_new_tokens=500,
pad_token_id=tokenizer.eos_token_id
)
start_time = perf_counter()
inputs = tokenizer(prompt, return_tensors="pt").to('cuda')
outputs = model.generate(**inputs, generation_config=generation_config)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
output_time = perf_counter() - start_time
st.write(response)
st.write(f"Time taken for inference: {round(output_time, 2)} seconds")
@st.cache(allow_output_mutation=True)
def load_model_and_tokenizer(model_name, token):
model = AutoModelForSequenceClassification.from_pretrained(model_name, token=token)
tokenizer = AutoTokenizer.from_pretrained(model_name, token=token)
return model, tokenizer
# Load your model and tokenizer from Hugging Face
model_name = "orYx-models/finetuned-tiny-llama-medical-papers"
token = "Tinyllama_secret" # Replace <your_token> with your actual Hugging Face Spaces secret
model, tokenizer = load_model_and_tokenizer(model_name, token)
# Define the pipeline with your model
pipe = pipeline("text2text-generation", model=model, tokenizer=tokenizer)
user_input = st.text_area("Enter some text:")
if user_input:
generate_response(user_input)
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