update

app.py

@@ -10,15 +10,15 @@ from model.modeling_llamask import LlamaskForCausalLM
 from model.tokenizer_utils import generate_custom_mask, prepare_tokenizer
 
 
-# access_token = os.getenv("HF_TOKEN")
-# model_id = "meta-llama/Meta-Llama-3.1-8B-Instruct"
-# device = 'cpu'
+access_token = os.getenv("HF_TOKEN")
+model_id = "meta-llama/Meta-Llama-3.1-8B-Instruct"
+device = 'cpu'
 
-# model = LlamaskForCausalLM.from_pretrained(model_id, torch_dtype= torch.bfloat16, token=access_token)
-# model = model.to(device)
-# tokenizer = AutoTokenizer.from_pretrained(model_id, padding_side="left")
+model = LlamaskForCausalLM.from_pretrained(model_id, torch_dtype= torch.bfloat16, token=access_token)
+model = model.to(device)
+tokenizer = AutoTokenizer.from_pretrained(model_id, padding_side="left")
 
-# prepare_tokenizer(tokenizer)
+prepare_tokenizer(tokenizer)
 
 
 def respond(
@@ -27,19 +27,19 @@ def respond(
     max_tokens,
     temperature,
 ):
-    # prompt = """<|start_header_id|>system<|end_header_id|>
+    prompt = """<|start_header_id|>system<|end_header_id|>
 
-    # You are a helpful assistant.<|eot_id|><|start_header_id|>user<|end_header_id|>
-    # {message}
-    # <|eot_id|><|start_header_id|>assistant<|end_header_id|>
-    # """
-    # model_inputs = generate_custom_mask(tokenizer, [prompt], device)
+    You are a helpful assistant.<|eot_id|><|start_header_id|>user<|end_header_id|>
+    {message}
+    <|eot_id|><|start_header_id|>assistant<|end_header_id|>
+    """
+    model_inputs = generate_custom_mask(tokenizer, [prompt], device)
    
-    # outputs = model.generate(temperature=0.7, max_tokens=64, **model_inputs)
-    # outputs = outputs[:, model_inputs['input_ids'].shape[1]:]
-    # result = tokenizer.batch_decode(outputs, skip_special_tokens=True)
+    outputs = model.generate(temperature=0.7, max_tokens=64, **model_inputs)
+    outputs = outputs[:, model_inputs['input_ids'].shape[1]:]
+    result = tokenizer.batch_decode(outputs, skip_special_tokens=True)
 
-    return "test"
+    return result
 
 """
 For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
@@ -47,7 +47,7 @@ For information on how to customize the ChatInterface, peruse the gradio docs: h
 demo = gr.ChatInterface(
     respond,
     additional_inputs=[
-        gr.Markdown("Please enter your message. Add privacy tags (<sensitive>...</sensitive>) around the words you want to hide. Only the most recent message submitted will be taken into account (no history is retained)."),
+        gr.Markdown("Please enter your message. Add privacy tags (\<sensitive\>...\<\/sensitive\>) around the words you want to hide. Only the most recent message submitted will be taken into account (no history is retained)."),
         gr.Slider(minimum=1, maximum=128, value=32, step=1, label="Max new tokens"),
         gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
     ],