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import os
import time
import gradio as gr
import torch
from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer
os.environ["TOKENIZERS_PARALLELISM"] = "0"
os.environ["PYTORCH_CUDA_ALLOC_CONF"] = "expandable_segments:True"
def load_model_and_tokenizer():
model_name = "NousResearch/Hermes-2-Theta-Llama-3-8B"
tokenizer = AutoTokenizer.from_pretrained(model_name)
special_tokens = {"pad_token": "<PAD>"}
tokenizer.add_special_tokens(special_tokens)
config = AutoConfig.from_pretrained(model_name)
setattr(
config,
"quantizer_path",
f"codebooks/Hermes-2-Theta-Llama-3-8B_1bit.xmad",
)
setattr(config, "window_length", 32)
model = AutoModelForCausalLM.from_pretrained(
model_name, config=config, torch_dtype=torch.float16, device_map="cuda:2"
)
if len(tokenizer) > model.get_input_embeddings().weight.shape[0]:
print(
"WARNING: Resizing the embedding matrix to match the tokenizer vocab size."
)
model.resize_token_embeddings(len(tokenizer))
model.config.pad_token_id = tokenizer.pad_token_id
return model, tokenizer
model, tokenizer = load_model_and_tokenizer()
def process_dialog(message, history):
dialog = [{"role": "user", "content": message}]
prompt = tokenizer.apply_chat_template(dialog, tokenize=False, add_generation_prompt=True)
tokenized_input_prompt_ids = tokenizer(prompt, return_tensors="pt").input_ids.to(model.device)
with torch.no_grad():
token_ids_for_each_answer = model.generate(
tokenized_input_prompt_ids,
max_new_tokens=512,
temperature=0.7,
do_sample=True,
eos_token_id=tokenizer.eos_token_id,
pad_token_id=tokenizer.pad_token_id,
)
response = token_ids_for_each_answer[0][tokenized_input_prompt_ids.shape[-1]:]
cleaned_response = tokenizer.decode(
response,
skip_special_tokens=True,
clean_up_tokenization_spaces=True,
)
return cleaned_response
def chatbot_response(message, history):
response = process_dialog(message, history)
return response
demo = gr.ChatInterface(
fn=chatbot_response,
examples=["Hello", "How are you?", "Tell me a joke"],
title="LLM Chatbot",
description="A demo chatbot using a quantized LLaMA model.",
)
if __name__ == "__main__":
demo.launch()
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