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import os
import torch
import gradio as gr
from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer
from huggingface_hub import InferenceClient
# Environment variables
os.environ["TOKENIZERS_PARALLELISM"] = "0"
os.environ["PYTORCH_CUDA_ALLOC_CONF"] = "expandable_segments:True"
# os.environ["GRADIO_CACHE_DIR"] = "/home/jwy4/gradio_cache"
# Initialize Hugging Face Inference Client
client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
# Load model and tokenizer (if you want to use a local model, uncomment and use the load_model_and_tokenizer function)
model = None
tokenizer = None
def load_model_and_tokenizer(model_name, dtype, kv_bits):
global model, tokenizer
if model is None or tokenizer is None:
tokenizer = AutoTokenizer.from_pretrained(model_name)
special_tokens = {"pad_token": "<PAD>"}
tokenizer.add_special_tokens(special_tokens)
config = AutoConfig.from_pretrained(model_name)
if kv_bits != "unquantized":
quantizer_path = f"codebooks/{model_name.split('/')[-1]}_{kv_bits}bit.xmad"
setattr(config, "quantizer_path", quantizer_path)
dtype = torch.__dict__.get(dtype, torch.float32)
model = AutoModelForCausalLM.from_pretrained(model_name, config=config, torch_dtype=dtype, device_map="auto")
if len(tokenizer) > model.get_input_embeddings().weight.shape[0]:
model.resize_token_embeddings(len(tokenizer))
tokenizer.padding_side = "left"
model.config.pad_token_id = tokenizer.pad_token_id
return model, tokenizer
def respond(message, history, 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
# Initialize Gradio 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)"),
],
theme="default",
title="1bit llama3 by xMAD.ai",
description="The first industrial level 1 bit quantization Llama3, we can achieve 800 tokens per second on NVIDIA V100 adn 1200 on NVIDIA A100, 90%% cost down of your cloud hostin cost",
css=".scrollable { height: 400px; overflow-y: auto; padding: 10px; border: 1px solid #ccc; }"
)
if __name__ == "__main__":
# Uncomment if using local model loading
# load_model_and_tokenizer("NousResearch/Meta-Llama-3-8B-Instruct", "fp16", "1")
demo.launch()
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