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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() | |
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() |