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import gradio as gr
from langchain_community.llms import LlamaCpp
from langchain.prompts import PromptTemplate
from langchain.chains import LLMChain
from langchain_core.callbacks import StreamingStdOutCallbackHandler
callbacks = [StreamingStdOutCallbackHandler()]
print("creating ll started")
llm = LlamaCpp(
model_path="cerebras_Llama3-DocChat-1.0-8B_Base_adapt_basic_model_16bit.gguf",
temperature=0.75,
max_tokens=30,
top_p=4,
callback_manager=callbacks,
verbose=True, # Verbose is required to pass to the callback manager
)
print("creating ll ended")
template = """You are the Finiantial expert:
### Instruction:
{question}
### Input:
### Response:
"""
prompt = PromptTemplate(template=template, input_variables=["question"])
llm_chain_model = LLMChain(prompt=prompt, llm=llm)
print("creating model created")
def greet(question):
print(f"question is {question}")
out_gen = llm_chain_model.run(question)
print(f"out is {out_gen}")
return out_gen
demo = gr.Interface(fn=greet, inputs="text", outputs="text")
demo.launch(debug=True, share=True) |