import from transformers dumbass

app.py

@@ -1,100 +1,100 @@
-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
-    
+import gradio as gr
+from huggingface_hub import InferenceClient
+from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
+from transformers 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()