Llama Uncensored

app.py

@@ -1,30 +1,34 @@
 import gradio as gr
 import os
 import spaces
+from transformers import GemmaTokenizer, AutoModelForCausalLM
 from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
 from threading import Thread
 
 # Set an environment variable
 HF_TOKEN = os.environ.get("HF_TOKEN", None)
 
+
 DESCRIPTION = '''
 <div>
-<h1 style="text-align: center;">Mistral Chat</h1>
+<h1 style="text-align: center;">deepseek-ai/DeepSeek-R1-Distill-Llama-8B</h1>
 </div>
 '''
 
 LICENSE = """
 <p/>
+
 ---
 """
 
 PLACEHOLDER = """
 <div style="padding: 30px; text-align: center; display: flex; flex-direction: column; align-items: center;">
-   <h1 style="font-size: 28px; margin-bottom: 2px; opacity: 0.55;">Mistral Chat 8B</h1>
+   <h1 style="font-size: 28px; margin-bottom: 2px; opacity: 0.55;">DeepSeek-R1-Distill-Llama-8B</h1>
    <p style="font-size: 18px; margin-bottom: 2px; opacity: 0.65;">Ask me anything...</p>
 </div>
 """
 
+
 css = """
 h1 {
   text-align: center;
@@ -41,8 +45,7 @@ h1 {
 
 # Load the tokenizer and model
 tokenizer = AutoTokenizer.from_pretrained("Orenguteng/Llama-3-8B-Lexi-Uncensored")
-model = AutoModelForCausalLM.from_pretrained("Orenguteng/Llama-3-8B-Lexi-Uncensored", device_map="auto")
-
+model = AutoModelForCausalLM.from_pretrained("Orenguteng/Llama-3-8B-Lexi-Uncensored", device_map="auto")  # to("cuda:0") 
 terminators = [
     tokenizer.eos_token_id,
     tokenizer.convert_tokens_to_ids("<|eot_id|>")
@@ -50,30 +53,23 @@ terminators = [
 
 @spaces.GPU(duration=120)
 def chat_llama3_8b(message: str, 
-                    history: list, 
-                    temperature: float, 
-                    max_new_tokens: int,
-                    system_prompt: str) -> str:
+              history: list, 
+              temperature: float, 
+              max_new_tokens: int
+             ) -> str:
     """
-    Generate a streaming response using the Mistral-8B model.
+    Generate a streaming response using the llama3-8b model.
     Args:
         message (str): The input message.
         history (list): The conversation history used by ChatInterface.
         temperature (float): The temperature for generating the response.
         max_new_tokens (int): The maximum number of new tokens to generate.
-        system_prompt (str): The system prompt to guide the assistant's behavior.
     Returns:
         str: The generated response.
     """
     conversation = []
-
-    # Include system prompt at the beginning if provided
-    if system_prompt:
-        conversation.append({"role": "system", "content": system_prompt})
-
     for user, assistant in 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").to(model.device)
@@ -81,14 +77,14 @@ def chat_llama3_8b(message: str,
     streamer = TextIteratorStreamer(tokenizer, timeout=10.0, skip_prompt=True, skip_special_tokens=True)
 
     generate_kwargs = dict(
-        input_ids=input_ids,
+        input_ids= input_ids,
         streamer=streamer,
         max_new_tokens=max_new_tokens,
         do_sample=True,
         temperature=temperature,
         eos_token_id=terminators,
     )
-
+    # This will enforce greedy generation (do_sample=False) when the temperature is passed 0, avoiding the crash.             
     if temperature == 0:
         generate_kwargs['do_sample'] = False
         
@@ -98,37 +94,40 @@ def chat_llama3_8b(message: str,
     outputs = []
     for text in streamer:
         outputs.append(text)
+        #print(outputs)
         yield "".join(outputs)
         
 
 # Gradio block
-chatbot = gr.Chatbot(height=450, placeholder=PLACEHOLDER, label='Gradio ChatInterface')
+chatbot=gr.Chatbot(height=450, placeholder=PLACEHOLDER, label='Gradio ChatInterface')
 
 with gr.Blocks(fill_height=True, css=css) as demo:
     
     gr.Markdown(DESCRIPTION)
-
-    system_prompt_input = gr.Textbox(
-        label="System Prompt",
-        placeholder="Enter system instructions for the model...",
-        lines=2
-    )
-
     gr.ChatInterface(
         fn=chat_llama3_8b,
         chatbot=chatbot,
         fill_height=True,
         additional_inputs_accordion=gr.Accordion(label="⚙️ Parameters", open=False, render=False),
         additional_inputs=[
-            system_prompt_input,
-            gr.Slider(minimum=0, maximum=1, step=0.1, value=0.8, label="Temperature", render=False),
-            gr.Slider(minimum=128, maximum=4096, step=1, value=4096, label="Max new tokens", render=False),
-        ],
+            gr.Slider(minimum=0,
+                      maximum=1, 
+                      step=0.1,
+                      value=0.8, 
+                      label="Temperature", 
+                      render=False),
+            gr.Slider(minimum=128, 
+                      maximum=4096,
+                      step=1,
+                      value=4096, 
+                      label="Max new tokens", 
+                      render=False ),
+            ],
         examples=[
-            ['Are you a sentient being?']
-        ],
-        cache_examples=False
+            ['Who are you?'],
+        cache_examples=False,
     )
-
+    
 if __name__ == "__main__":
     demo.launch()
+