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import os
from threading import Thread
from typing import Iterator
import gradio as gr
import spaces
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
# Set the environment variable
os.environ['TF_ENABLE_ONEDNN_OPTS'] = '0'
DESCRIPTION = """\
# Llama 3.2 3B Instruct
Llama 3.2 3B is Meta's latest iteration of open LLMs.
This is a demo of [`meta-llama/Llama-3.2-3B-Instruct`](https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct), fine-tuned for instruction following.
For more details, please check [our post](https://huggingface.co/blog/llama32).
"""
# Access token for the model (if required)
access_token = os.getenv('HF_TOKEN')
# Download the Base model
#model_id = "./models/Llama-32-3B-Instruct"
model_id = "meta-llama/Llama-3.1-8B-Instruct"
MAX_MAX_NEW_TOKENS = 2048
DEFAULT_MAX_NEW_TOKENS = 1024
MAX_INPUT_TOKEN_LENGTH = int(os.getenv("MAX_INPUT_TOKEN_LENGTH", "4096"))
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
#model_id = "nltpt/Llama-3.2-3B-Instruct"
tokenizer = AutoTokenizer.from_pretrained(model_id,token=access_token)
#tokenizer.padding_side = 'right'
#tokenizer.eos_token_id = 107
#tokenizer.pad_token = tokenizer.eos_token
model = AutoModelForCausalLM.from_pretrained(
model_id,
device_map=device,
torch_dtype=torch.bfloat16,
token=access_token
)
model.eval()
@spaces.GPU(duration=90)
def generate(
message: str,
chat_history: list[tuple[str, str]],
system_prompt: str,
max_new_tokens: int = 1024,
temperature: float = 0.6,
top_p: float = 0.9,
top_k: int = 50,
repetition_penalty: float = 1.2,
) -> Iterator[str]:
conversation = [{"role": "system", "content": system_prompt}]
for user, assistant in chat_history:
conversation.extend(
[
{"role": "user", "content": user},
{"role": "assistant", "content": assistant},
]
)
conversation.append({"role": "user", "content": message})
# Set pad_token_id if it's not already set
if tokenizer.pad_token_id is None:
tokenizer.padding_side = 'right'
tokenizer.pad_token = tokenizer.eos_token
input_ids = tokenizer.apply_chat_template(conversation, add_generation_prompt=True,add_special_tokens=True, return_tensors="pt",padding=True ,return_attention_mask=True)
if input_ids.shape[1] > MAX_INPUT_TOKEN_LENGTH:
input_ids = input_ids[:, -MAX_INPUT_TOKEN_LENGTH:]
gr.Warning(f"Trimmed input from conversation as it was longer than {MAX_INPUT_TOKEN_LENGTH} tokens.")
# Ensure attention mask is set
#attention_mask = input_ids['attention_mask']
input_ids = input_ids.to(model.device)
#attention_mask = attention_mask.to(model.device)
streamer = TextIteratorStreamer(tokenizer, timeout=20.0, 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,
top_p=top_p,
top_k=top_k,
temperature=temperature,
num_beams=1,
repetition_penalty=repetition_penalty
)
t = Thread(target=model.generate, kwargs=generate_kwargs)
t.start()
outputs = []
for text in streamer:
outputs.append(text)
yield "".join(outputs)
chat_interface = gr.ChatInterface(
fn=generate,
additional_inputs=[
gr.Textbox(
label="System Prompt",
placeholder="Enter system prompt here...",
lines=2,
),
gr.Slider(
label="Max new tokens",
minimum=1,
maximum=MAX_MAX_NEW_TOKENS,
step=1,
value=DEFAULT_MAX_NEW_TOKENS,
),
gr.Slider(
label="Temperature",
minimum=0.1,
maximum=4.0,
step=0.1,
value=0.6,
),
gr.Slider(
label="Top-p (nucleus sampling)",
minimum=0.05,
maximum=1.0,
step=0.05,
value=0.9,
),
gr.Slider(
label="Top-k",
minimum=1,
maximum=1000,
step=1,
value=50,
),
gr.Slider(
label="Repetition penalty",
minimum=1.0,
maximum=2.0,
step=0.05,
value=1.2,
),
],
stop_btn=None,
examples=[
["Hello there! How are you doing?"],
["Can you explain briefly to me what is the Python programming language?"],
["Explain the plot of Cinderella in a sentence."],
["How many hours does it take a man to eat a Helicopter?"],
["Write a 100-word article on 'Benefits of Open-Source in AI research'"],
],
cache_examples=False,
)
with gr.Blocks(css="style.css", fill_height=True) as demo:
gr.Markdown(DESCRIPTION)
gr.DuplicateButton(value="Duplicate Space for private use", elem_id="duplicate-button")
chat_interface.render()
if __name__ == "__main__":
demo.queue(max_size=20).launch() |