# import subprocess
# # Install flash attention, skipping CUDA build if necessary
# subprocess.run(
# "pip install flash-attn --no-build-isolation",
# env={"FLASH_ATTENTION_SKIP_CUDA_BUILD": "TRUE"},
# shell=True,
# )
import gradio as gr
import os
import spaces
from transformers import AutoModelForCausalLM
from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
from threading import Thread
# Set an environment variable
HF_TOKEN = os.environ.get("HF_TOKEN", None)
DESCRIPTION = '''
Meta Llama3 8B
This Space demonstrates the instruction-tuned model Meta Llama3 8b Chat. Meta Llama3 is the new open LLM and comes in two sizes: 8b and 70b. Feel free to play with it, or duplicate to run privately!
š For more details about the Llama3 release and how to use the model with transformers
, take a look at our blog post.
š¦ Looking for an even more powerful model? Check out the Hugging Chat integration for Meta Llama 3 70b
'''
LICENSE = """
---
Built with Meta Llama 3
"""
PLACEHOLDER = """
Meta llama3
Ask me anything...
"""
css = """
h1 {
text-align: center;
display: block;
}
#duplicate-button {
margin: auto;
color: white;
background: #1565c0;
border-radius: 100vh;
}
"""
# Load the tokenizer and model
model_name = "gradientai/Llama-3-8B-Instruct-262k"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype="auto", device_map="auto") # to("cuda:0")
from minference import MInference
minference_patch = MInference("minference", model_name)
model = minference_patch(model)
terminators = [
tokenizer.eos_token_id,
tokenizer.convert_tokens_to_ids("<|eot_id|>")
]
@spaces.GPU(duration=120)
def chat_llama3_8b(message: str,
history: list,
temperature: float,
max_new_tokens: int
) -> str:
"""
Generate a streaming response using the llama3-8b model.
Args:
message (str): The input message.
history (list): The conversation history used by ChatInterface.
temperature (float): The temperature for generating the response.
max_new_tokens (int): The maximum number of new tokens to generate.
Returns:
str: The generated response.
"""
# global model
conversation = []
for user, assistant in history:
conversation.extend([{"role": "user", "content": user}, {"role": "assistant", "content": assistant}])
conversation.append({"role": "user", "content": message})
input_ids = tokenizer.apply_chat_template(conversation, return_tensors="pt").to(model.device)
print(model.device)
# subprocess.run(
# "pip install pycuda==2023.1",
# shell=True,
# )
# if "has_patch" not in model.__dict__:
# from minference import MInference
# minference_patch = MInference("minference", model_name)
# model = minference_patch(model)
streamer = TextIteratorStreamer(tokenizer, timeout=10.0, skip_prompt=True, skip_special_tokens=True)
generate_kwargs = dict(
input_ids= input_ids,
streamer=streamer,
max_new_tokens=max_new_tokens,
do_sample=True,
temperature=temperature,
eos_token_id=terminators,
)
# This will enforce greedy generation (do_sample=False) when the temperature is passed 0, avoiding the crash.
if temperature == 0:
generate_kwargs['do_sample'] = False
t = Thread(target=model.generate, kwargs=generate_kwargs)
t.start()
outputs = []
for text in streamer:
outputs.append(text)
#print(outputs)
yield "".join(outputs)
# Gradio block
chatbot=gr.Chatbot(height=450, placeholder=PLACEHOLDER, label='Gradio ChatInterface')
with gr.Blocks(fill_height=True, css=css) as demo:
gr.Markdown(DESCRIPTION)
gr.DuplicateButton(value="Duplicate Space for private use", elem_id="duplicate-button")
gr.ChatInterface(
fn=chat_llama3_8b,
chatbot=chatbot,
fill_height=True,
additional_inputs_accordion=gr.Accordion(label="āļø Parameters", open=False, render=False),
additional_inputs=[
gr.Slider(minimum=0,
maximum=1,
step=0.1,
value=0.95,
label="Temperature",
render=False),
gr.Slider(minimum=128,
maximum=4096,
step=1,
value=512,
label="Max new tokens",
render=False ),
],
examples=[
['How to setup a human base on Mars? Give short answer.'],
['Explain theory of relativity to me like Iām 8 years old.'],
['What is 9,000 * 9,000?'],
['Write a pun-filled happy birthday message to my friend Alex.'],
['Justify why a penguin might make a good king of the jungle.']
],
cache_examples=False,
)
gr.Markdown(LICENSE)
if __name__ == "__main__":
demo.launch()