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# import gradio as gr | |
# gr.load("models/manohar02/Llama-2-7b-quantize").launch() | |
import gradio as gr | |
from langchain import HuggingFacePipeline, PromptTemplate, LLMChain | |
from transformers import AutoTokenizer | |
import transformers | |
import torch | |
# Define the Hugging Face model | |
model = "manohar02/Llama-2-7b-quantize" | |
# Define the Hugging Face pipeline | |
pipeline = transformers.pipeline( | |
"text-generation", # task | |
model=model, | |
torch_dtype=torch.bfloat16, | |
max_length=20000, | |
do_sample=True, | |
top_k=10, | |
num_return_sequences=1, | |
eos_token_id=AutoTokenizer.from_pretrained(model).eos_token_id | |
) | |
llm = HuggingFacePipeline(pipeline=pipeline, model_kwargs={'temperature': 0}) | |
# Define the template for summarization | |
template = """ | |
Write a concise summary of the following text delimited by triple backquotes. | |
'''{text}''' | |
SUMMARY: | |
""" | |
prompt = PromptTemplate(template=template, input_variables=["text"]) | |
llm_chain = LLMChain(prompt=prompt, llm=llm) | |
# Function to generate summary | |
def generate_summary(text): | |
summary = llm_chain.run(text) | |
return summary.split('SUMMARY:')[-1].strip() | |
# Create a Gradio interface | |
iface = gr.Interface(fn=generate_summary, inputs="text", outputs="text", title="LLaMA2 Summarizer") | |
iface.launch() |