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# import gradio as gr | |
# from huggingface_hub import InferenceClient | |
# """ | |
# For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference | |
# """ | |
# client = InferenceClient("harsh4733/Llama-2-7b-chat-finetune-webglm") | |
# def respond( | |
# message, | |
# history: list[tuple[str, str]], | |
# system_message, | |
# max_tokens, | |
# temperature, | |
# top_p, | |
# ): | |
# messages = [{"role": "system", "content": system_message}] | |
# for val in history: | |
# if val[0]: | |
# messages.append({"role": "user", "content": val[0]}) | |
# if val[1]: | |
# messages.append({"role": "assistant", "content": val[1]}) | |
# messages.append({"role": "user", "content": message}) | |
# response = "" | |
# for message in client.chat_completion( | |
# messages, | |
# max_tokens=max_tokens, | |
# stream=True, | |
# temperature=temperature, | |
# top_p=top_p, | |
# ): | |
# token = message.choices[0].delta.content | |
# response += token | |
# yield response | |
# """ | |
# For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface | |
# """ | |
# demo = gr.ChatInterface( | |
# respond, | |
# additional_inputs=[ | |
# gr.Textbox(value="You are a friendly Chatbot.", label="System message"), | |
# gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"), | |
# gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"), | |
# gr.Slider( | |
# minimum=0.1, | |
# maximum=1.0, | |
# value=0.95, | |
# step=0.05, | |
# label="Top-p (nucleus sampling)", | |
# ), | |
# ], | |
# ) | |
# import gradio as gr | |
# from transformers import pipeline | |
# def chat_with_model(question, prompt, system_message, max_tokens, temperature, top_p): | |
# prompt_template = f"<s>[INST] <<SYS>>\n{system_message} <</SYS>> {prompt} [/INST]" | |
# pipe = pipeline( | |
# task="text-generation", | |
# model="harsh4733/Llama-2-7b-chat-finetune-webglm", | |
# tokenizer="harsh4733/Llama-2-7b-chat-finetune-webglm", | |
# max_length=max_tokens, | |
# temperature=temperature, | |
# top_p=top_p, | |
# ) | |
# result = pipe(prompt_template) | |
# return result[0]['generated_text'] | |
# def respond( | |
# question, | |
# prompt, | |
# system_message, | |
# max_tokens, | |
# temperature, | |
# top_p, | |
# ): | |
# response = chat_with_model(question, prompt, system_message, max_tokens, temperature, top_p) | |
# return response | |
# # Define Gradio interface | |
# demo = gr.Interface( | |
# fn=respond, | |
# inputs=[ | |
# gr.Textbox(value="What is a large language model?", label="Question"), | |
# gr.Textbox(value="You are a helpful assistant that provides answers to the questions given based on the references provided to you regarding the question.", label="System message"), | |
# gr.Textbox(value="You are a friendly Chatbot.", label="Prompt"), | |
# gr.Slider(minimum=1, maximum=2048, value=512, label="Max new tokens"), | |
# gr.Slider(minimum=0.1, maximum=4.0, value=0.7, label="Temperature"), | |
# gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)"), | |
# ], | |
# outputs=gr.Textbox(label="Response"), | |
# title="Chat with Large Language Model", | |
# description="Interact with a large language model to generate responses based on your input.", | |
# ) | |
# if __name__ == "__main__": | |
# demo.launch() | |
# if __name__ == "__main__": | |
# demo.launch() | |
import gradio as gr | |
from transformers import TFAutoModelForCausalLM, AutoTokenizer | |
import tensorflow as tf | |
def chat_with_model(question, prompt, system_message, max_tokens, temperature, top_p): | |
tokenizer = AutoTokenizer.from_pretrained("harsh4733/Llama-2-7b-chat-finetune-webglm") | |
model = TFAutoModelForCausalLM.from_pretrained("harsh4733/Llama-2-7b-chat-finetune-webglm") | |
prompt_template = f"<s>[INST] <<SYS>>\n{system_message} <</SYS>> {prompt} [/INST]" | |
input_ids = tokenizer.encode(prompt_template, return_tensors="tf", max_length=512, truncation=True) | |
output = model.generate(input_ids, max_length=max_tokens, temperature=temperature, top_p=top_p, num_return_sequences=1) | |
response = tokenizer.decode(output[0], skip_special_tokens=True) | |
return response | |
def respond( | |
question, | |
prompt, | |
system_message, | |
max_tokens, | |
temperature, | |
top_p, | |
): | |
response = chat_with_model(question, prompt, system_message, max_tokens, temperature, top_p) | |
return response | |
# Define Gradio interface | |
demo = gr.Interface( | |
fn=respond, | |
inputs=[ | |
gr.Textbox(value="What is a large language model?", label="Question"), | |
gr.Textbox(value="You are a helpful assistant that provides answers to the questions given based on the references provided to you regarding the question.", label="System message"), | |
gr.Textbox(value="You are a friendly Chatbot.", label="Prompt"), | |
gr.Slider(minimum=1, maximum=2048, value=512, label="Max new tokens"), | |
gr.Slider(minimum=0.1, maximum=4.0, value=0.7, label="Temperature"), | |
gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)"), | |
], | |
outputs=gr.Textbox(label="Response"), | |
title="Chat with Large Language Model", | |
description="Interact with a large language model to generate responses based on your input.", | |
) | |
if __name__ == "__main__": | |
demo.launch() | |