Create app.py

app.py

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+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)
+
+TITLE = '''
+<h1 style="text-align: center;">Meta Llama3.1 8B <a href="https://huggingface.co/spaces/ysharma/Chat_with_Meta_llama3_1_8b?duplicate=true" id="duplicate-button"><button style="color:white">Duplicate this Space</button></a></h1>
+'''
+
+DESCRIPTION = '''
+<div>
+<p>This Space demonstrates the instruction-tuned model <a href="https://huggingface.co/meta-llama/Meta-Llama-3.1-8B-Instruct"><b>Meta Llama3.1 8b Chat</b></a>. Feel free to play with this demo, or duplicate to run privately!</p>
+<p>🔨 Interested in trying out more powerful Instruct versions of Llama3.1? Check out the <a href="https://huggingface.co/chat/"><b>Hugging Chat</b></a> integration for 🐘 Meta Llama 3.1 70b, and 🦕 Meta Llama 3.1 405b</p>
+<p>🔎 For more details about the Llama3.1 release and how to use the model with <code>transformers</code>, take a look <a href="https://huggingface.co/blog/llama3">at our blog post</a>.</p>
+</div>
+'''
+
+PLACEHOLDER = """
+<div style="padding: 30px; text-align: center; display: flex; flex-direction: column; align-items: center;">
+   <img src="https://ysharma-dummy-chat-app.hf.space/file=/tmp/gradio/8e75e61cc9bab22b7ce3dec85ab0e6db1da5d107/Meta_lockup_positive%20primary_RGB.jpg" style="width: 80%; max-width: 550px; height: auto; opacity: 0.55;  "> 
+   <h1 style="font-size: 28px; margin-bottom: 2px; opacity: 0.55;">Meta llama3.1</h1>
+   <p style="font-size: 18px; margin-bottom: 2px; opacity: 0.65;">Ask me anything...</p>
+</div>
+"""
+
+css = """
+h1 {
+  text-align: center;
+  display: block;
+  display: flex;
+    align-items: center;
+    justify-content: center;
+}
+
+#duplicate-button {
+  margin-left: 10px;
+  color: white;
+  background: #1565c0;
+  border-radius: 100vh;
+  font-size: 1rem;
+  padding: 3px 5px;
+}
+"""
+
+model = "llhf/Meta-Llama-3.1-8B-Instruct"
+
+# Load the tokenizer and model
+tokenizer = AutoTokenizer.from_pretrained(f"{model}")
+model = AutoModelForCausalLM.from_pretrained(f"{model}", device_map="auto")
+terminators = [
+    tokenizer.eos_token_id,
+    tokenizer.convert_tokens_to_ids("<|eot_id|>")
+]
+
+# Gradio inference function
+@spaces.GPU(duration=120)
+def chat_llama3_1_8b(message: str, 
+              history: list, 
+              temperature: float, 
+              max_new_tokens: int
+             ) -> str:
+    """
+    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.
+    Returns:
+        str: The generated response.
+    """
+    conversation = []
+
+    # Gradio now supports the Messages API out of the box!
+    conversation.append({"role": "user", "content": message})
+
+    input_ids = tokenizer.apply_chat_template(conversation, return_tensors="pt").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,
+        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
+        
+    t = Thread(target=model.generate, kwargs=generate_kwargs)
+    t.start()
+
+    outputs = []
+    for text in streamer:
+        outputs.append(text)
+        yield "".join(outputs)
+        
+
+# Gradio block
+chatbot=gr.Chatbot(height=450, placeholder=PLACEHOLDER, label='Gradio ChatInterface')
+
+with gr.Blocks(fill_height=True, css=css) as demo:
+
+    gr.Markdown(TITLE)
+    gr.Markdown(DESCRIPTION)
+    #gr.DuplicateButton(value="Duplicate Space for private use", elem_id="duplicate-button")
+    gr.ChatInterface(
+        fn=chat_llama3_1_8b,
+        chatbot=chatbot,
+        type="messages",
+        fill_height=True,
+        examples_per_page=3,
+        additional_inputs_accordion=gr.Accordion(label="⚙️ Parameters", open=False, render=False),
+        additional_inputs=[
+            gr.Slider(minimum=0,
+                      maximum=1, 
+                      step=0.1,
+                      value=0.95, 
+                      label="Temperature", 
+                      render=False),
+            gr.Slider(minimum=128, 
+                      maximum=4096,
+                      step=1,
+                      value=512, 
+                      label="Max new tokens", 
+                      render=False ),
+            ],
+        examples=[
+            ["There's a llama in my garden 😱 What should I do?"],
+            ["What is the best way to open a can of worms?"],
+            ["The odd numbers in this group add up to an even number: 15, 32, 5, 13, 82, 7, 1. "],
+            ['How to setup a human base on Mars? Give short answer.'],
+            ['Explain theory of relativity to me like I’m 8 years old.'],
+            ['What is 9,000 * 9,000?'],
+            ['Write a pun-filled happy birthday message to my friend Alex.'],
+            ['Justify why a penguin might make a good king of the jungle.']
+            ],
+        cache_examples=False,
+                     )
+    
+    
+if __name__ == "__main__":
+    demo.launch()
+