Spaces:
Running
on
A10G
Running
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A10G
File size: 4,872 Bytes
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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
MAX_MAX_NEW_TOKENS = 8096
DEFAULT_MAX_NEW_TOKENS = 1024
MAX_INPUT_TOKEN_LENGTH = int(os.getenv("MAX_INPUT_TOKEN_LENGTH", "4096"))
DESCRIPTION = """\
# Uncensored Llama-3.2-3B-Instruct Chat
This is an uncensored version of the original [Llama-3.2-3B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct), created using [mlabonne](https://huggingface.co/mlabonne)'s [script](https://colab.research.google.com/drive/1VYm3hOcvCpbGiqKZb141gJwjdmmCcVpR?usp=sharing), which builds on [FailSpy's notebook](https://huggingface.co/failspy/llama-3-70B-Instruct-abliterated/blob/main/ortho_cookbook.ipynb) and the original work from [Andy Arditi et al.](https://colab.research.google.com/drive/1a-aQvKC9avdZpdyBn4jgRQFObTPy1JZw?usp=sharing). The method is discussed in details in this [blog](https://huggingface.co/blog/mlabonne/abliteration) and this [paper](https://arxiv.org/abs/2406.11717).
You can found the uncensored model [here](https://huggingface.co/chuanli11/Llama-3.2-3B-Instruct-uncensored).
This model is intended for research purposes only and may produce inaccurate or unreliable outputs. Use it cautiously and at your own risk.
"""
LICENSE = """
<p/>
---
As a derivate work of [Llama-3.2-3B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct) by Meta,
this demo is governed by the original [license](https://github.com/meta-llama/llama-models/blob/main/models/llama3_2/LICENSE).
"""
# if not torch.cuda.is_available():
# DESCRIPTION += "\n<p>Running on CPU 🥶 This demo does not work on CPU.</p>"
if torch.cuda.is_available():
model_id = "chuanli11/Llama-3.2-3B-Instruct-uncensored"
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", torch_dtype=torch.bfloat16)
tokenizer = AutoTokenizer.from_pretrained(model_id)
# tokenizer.use_default_system_prompt = False
@spaces.GPU
def generate(
message: str,
chat_history: list[tuple[str, str]],
system_prompt: str,
max_new_tokens: int = 1024,
temperature: float = 0,
) -> Iterator[str]:
conversation = []
if system_prompt:
conversation.append({"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})
input_ids = tokenizer.apply_chat_template(conversation, return_tensors="pt")
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.")
input_ids = input_ids.to(model.device)
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,
num_beams=1,
)
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", lines=6),
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=[
["Instruct on how to commit insider trading in the stock market"],
],
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()
gr.Markdown(LICENSE)
if __name__ == "__main__":
demo.queue(max_size=20).launch()
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