Update app.py

app.py

@@ -1,142 +1,202 @@
 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;">Meta Llama3 8B</h1>
-<p>This Space demonstrates the instruction-tuned model <a href="https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct"><b>Meta Llama3 8b Chat</b></a>. Meta Llama3 is the new open LLM and comes in two sizes: 8b and 70b. Feel free to play with it, or duplicate to run privately!</p>
-<p>🔎 For more details about the Llama3 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>
-<p>🦕 Looking for an even more powerful model? Check out the <a href="https://huggingface.co/chat/"><b>Hugging Chat</b></a> integration for Meta Llama 3 70b</p>
-</div>
-'''
-
-LICENSE = """
-<p/>
----
-Built with Meta Llama 3
-"""
-
-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</h1>
-   <p style="font-size: 18px; margin-bottom: 2px; opacity: 0.65;">Ask me anything...</p>
-</div>
-"""
-
-
-css = """
-h1 {
-  text-align: center;
-  display: block;
-}
-#duplicate-button {
-  margin: auto;
-  color: white;
-  background: #1565c0;
-  border-radius: 100vh;
-}
-"""
-
-# Load the tokenizer and model
-tokenizer = AutoTokenizer.from_pretrained("meta-llama/Meta-Llama-3-8B-Instruct")
-model = AutoModelForCausalLM.from_pretrained("meta-llama/Meta-Llama-3-8B-Instruct", device_map="auto")  # to("cuda:0") 
-terminators = [
-    tokenizer.eos_token_id,
-    tokenizer.convert_tokens_to_ids("<|eot_id|>")
-]
-
-@spaces.GPU(duration=120)
-def chat_llama3_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 = []
-    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)
-    
-    streamer = TextIteratorStreamer(tokenizer, timeout=10.0, skip_prompt=True, skip_special_tokens=True)
+import torch
+
+import transformers
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "meta-llama/Meta-Llama-3-8B-Instruct"
 
-    generate_kwargs = dict(
-        input_ids= input_ids,
-        streamer=streamer,
+pipeline = transformers.pipeline(
+    "text-generation",
+    model=model_name,
+    model_kwargs={"torch_dtype": torch.bfloat16},
+    device="cpu",
+)
+
+def chat_function(message, history, system_prompt,max_new_tokens,temperature):
+    messages = [
+        {"role": "system", "content": system_prompt},
+        {"role": "user", "content": message},
+    ]
+    prompt = pipeline.tokenizer.apply_chat_template(
+        messages,
+        tokenize=False,
+        add_generation_prompt=True
+    )
+    terminators = [
+        pipeline.tokenizer.eos_token_id,
+        pipeline.tokenizer.convert_tokens_to_ids("<|eot_id|>")
+    ]
+    temp = temperature + 0.1
+    outputs = pipeline(
+        prompt,
         max_new_tokens=max_new_tokens,
-        do_sample=True,
-        temperature=temperature,
         eos_token_id=terminators,
+        do_sample=True,
+        temperature=temp,
+        top_p=0.9,
     )
-    # 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
+    return outputs[0]["generated_text"][len(prompt):]
+
+gr.ChatInterface(
+    chat_function,
+    chatbot=gr.Chatbot(height=400),
+    textbox=gr.Textbox(placeholder="Enter message here", container=False, scale=7),
+    title="Meta-Llama-3-8B-Instruct",
+    description="""
+    To Learn about Fine-tuning Llama-3-8B, Ckeck https://exnrt.com/blog/ai/finetune-llama3-8b/.
+    """,
+    additional_inputs=[
+        gr.Textbox("You are helpful AI.", label="System Prompt"),
+        gr.Slider(512, 4096, label="Max New Tokens"),
+        gr.Slider(0, 1, label="Temperature")
+    ]
+).launch()
+
+
+#The Code
+
+# 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;">Meta Llama3 8B</h1>
+# <p>This Space demonstrates the instruction-tuned model <a href="https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct"><b>Meta Llama3 8b Chat</b></a>. Meta Llama3 is the new open LLM and comes in two sizes: 8b and 70b. Feel free to play with it, or duplicate to run privately!</p>
+# <p>🔎 For more details about the Llama3 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>
+# <p>🦕 Looking for an even more powerful model? Check out the <a href="https://huggingface.co/chat/"><b>Hugging Chat</b></a> integration for Meta Llama 3 70b</p>
+# </div>
+# '''
+
+# LICENSE = """
+# <p/>
+# ---
+# Built with Meta Llama 3
+# """
+
+# 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</h1>
+#    <p style="font-size: 18px; margin-bottom: 2px; opacity: 0.65;">Ask me anything...</p>
+# </div>
+# """
+
+
+# css = """
+# h1 {
+#   text-align: center;
+#   display: block;
+# }
+# #duplicate-button {
+#   margin: auto;
+#   color: white;
+#   background: #1565c0;
+#   border-radius: 100vh;
+# }
+# """
+
+# # Load the tokenizer and model
+# tokenizer = AutoTokenizer.from_pretrained("meta-llama/Meta-Llama-3-8B-Instruct")
+# model = AutoModelForCausalLM.from_pretrained("meta-llama/Meta-Llama-3-8B-Instruct", device_map="auto")  # to("cuda:0") 
+# terminators = [
+#     tokenizer.eos_token_id,
+#     tokenizer.convert_tokens_to_ids("<|eot_id|>")
+# ]
+
+# @spaces.GPU(duration=120)
+# def chat_llama3_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 = []
+#     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)
+    
+#     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)
-        print(outputs)
-        yield "".join(outputs)
+#     t = Thread(target=model.generate, kwargs=generate_kwargs)
+#     t.start()
+
+#     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')
+# # Gradio block
+# chatbot=gr.Chatbot(height=450, placeholder=PLACEHOLDER, label='Gradio ChatInterface')
 
-with gr.Blocks(fill_height=True, css=css) as demo:
+# with gr.Blocks(fill_height=True, css=css) as demo:
     
-    gr.Markdown(DESCRIPTION)
-    gr.DuplicateButton(value="Duplicate Space for private use", elem_id="duplicate-button")
-    gr.ChatInterface(
-        fn=chat_llama3_8b,
-        chatbot=chatbot,
-        fill_height=True,
-        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=[
-            ['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.']
-            ],
-        cache_examples=False,
-                     )
+#     gr.Markdown(DESCRIPTION)
+#     gr.DuplicateButton(value="Duplicate Space for private use", elem_id="duplicate-button")
+#     gr.ChatInterface(
+#         fn=chat_llama3_8b,
+#         chatbot=chatbot,
+#         fill_height=True,
+#         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=[
+#             ['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.']
+#             ],
+#         cache_examples=False,
+#                      )
     
-    gr.Markdown(LICENSE)
+#     gr.Markdown(LICENSE)
     
-if __name__ == "__main__":
-    demo.launch()
+# if __name__ == "__main__":
+#     demo.launch()