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import gradio as gr
from huggingface_hub import InferenceClient
from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
from bitsandbytes import BitsAndBytesConfig
import spaces
import torch
from safetensors import safe_open
from jaxtyping import Float, Int
from typing import List, Callable
from torch import Tensor
from threading import Thread
import einops
tokenizer = AutoTokenizer.from_pretrained("NousResearch/Meta-LLaMA-70B-Instruct")
quantization_config = BitsAndBytesConfig(load_in_4_bit=True)
model = AutoModelForCausalLM.from_pretrained("NousResearch/Meta-LLaMA-70B-Instruct", quantization_config, device_map="cuda" ).eval()
@spaces.GPU
def respond(
message,
history: list[tuple[str, str]],
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 = ""
inputs = tokenizer.apply_chat_template(messages, return_tensors="pt")
streamer = TextIteratorStreamer(tokenizer, skip_special_tokens=True, skip_prompt=True)
thread = Thread(
target=model.generate,
kwargs={
"inputs": inputs,
"max_new_tokens": max_tokens,
"temperature": temperature,
"top_p": top_p,
"streamer": streamer,
},
)
thread.start()
for new_text in streamer:
token = new_text.choices[0].delta.content
response += token
yield response
def get_orthogonalized_matrix(matrix: Float[Tensor, '... d_model'], vec: Float[Tensor, 'd_model']) -> Float[Tensor, '... d_model']:
device = matrix.device
vec = vec.to(device)
proj = einops.einsum(matrix, vec.view(-1, 1), '... d_model, d_model single -> ... single') * vec
return matrix - proj
"""
For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/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)",
),
],
)
if __name__ == "__main__":
# get refusal_dir from refusal_dir.safetensors file.
with safe_open("refusal_dir.safetensors", framework="pt", device="cpu") as f:
refusal_dir = f.get_tensor("refusal_dir")
refusal_dir = refusal_dir.cpu().float()
model.model.embed_tokens.weight.data = get_orthogonalized_matrix(model.model.embed_tokens.weight, refusal_dir)
for block in model.model.layers:
block.self_attn.o_proj.weight.data = get_orthogonalized_matrix(block.self_attn.o_proj.weight, refusal_dir)
block.mlp.down_proj.weight.data = get_orthogonalized_matrix(block.mlp.down_proj.weight.T, refusal_dir).T
demo.launch() |