Create app.py

app.py

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+#llama 3.2 3b IT
+import os
+from threading import Thread
+from typing import Iterator
+
+import gradio as gr
+#import spaces
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
+
+DESCRIPTION = """\
+# Llama 3.2 3B Instruct
+Llama 3.2 3B is Meta's latest iteration of open LLMs.
+This is a demo of [`meta-llama/Llama-3.2-3B-Instruct`](https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct), fine-tuned for instruction following.
+For more details, please check [our post](https://huggingface.co/blog/llama32).
+"""
+
+MAX_MAX_NEW_TOKENS = 2048
+DEFAULT_MAX_NEW_TOKENS = 1024
+MAX_INPUT_TOKEN_LENGTH = int(os.getenv("MAX_INPUT_TOKEN_LENGTH", "4096"))
+
+device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+
+# Download model from Huggingface Hub
+# Change this to meta-llama or the correct org name from Huggingface Hub
+model_id = "meta-internal/Llama-3.2-3B-Instruct"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    device_map="auto",
+    torch_dtype=torch.bfloat16,
+)
+model.eval()
+
+# Main Gradio inference function
+def generate(
+    message: str,
+    chat_history: list[tuple[str, str]],
+    max_new_tokens: int = 1024,
+    temperature: float = 0.6,
+    top_p: float = 0.9,
+    top_k: int = 50,
+    repetition_penalty: float = 1.2,
+) -> Iterator[str]:
+
+    conversation = [{k: v for k, v in d.items() if k != 'metadata'} for d in chat_history]
+    conversation.append({"role": "user", "content": message})
+
+    input_ids = tokenizer.apply_chat_template(conversation, add_generation_prompt=True, return_tensors="pt")
+    if input_ids.shape[1] > MAX_INPUT_TOKEN_LENGTH:
+        input_ids = input_ids[:, -MAX_INPUT_TOKEN_LENGTH:]
+        gr.Warning(f"Trimmed input from conversation as it was longer than {MAX_INPUT_TOKEN_LENGTH} tokens.")
+    input_ids = input_ids.to(model.device)
+
+    streamer = TextIteratorStreamer(tokenizer, timeout=20.0, 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,
+        top_p=top_p,
+        top_k=top_k,
+        temperature=temperature,
+        num_beams=1,
+        repetition_penalty=repetition_penalty,
+    )
+    t = Thread(target=model.generate, kwargs=generate_kwargs)
+    t.start()
+
+    conversation.append({"role": "assistant", "content": ""})
+    outputs = []
+    for text in streamer:
+        outputs.append(text)
+        bot_response = "".join(outputs)
+        conversation[-1]['content'] = bot_response
+        yield "", conversation
+
+
+# Implementing Gradio 5 features and building a ChatInterface UI yourself
+PLACEHOLDER = """<div style="padding: 20px; text-align: center; display: flex; flex-direction: column; align-items: center;">
+   <img src="https://ysharma-dummy-chat-app.hf.space/file=/tmp/gradio/c21ff9c8e7ecb2f7d957a72f2ef03c610ac7bbc4/Meta_lockup_positive%20primary_RGB_small.jpg" style="width: 80%; max-width: 550px; height: auto; opacity: 0.55; margin-bottom: 10px;">
+   <h1 style="font-size: 28px; margin: 0;">Meta llama3.2</h1>
+   <p style="font-size: 18px; margin: 5px 0 0; opacity: 0.65;">
+       <a href="https://huggingface.co/blog/llama32" target="_blank" style="color: inherit; text-decoration: none;">Learn more about Llama 3.2</a>
+   </p>
+</div>"""
+
+
+def handle_retry(history, retry_data: gr.RetryData):
+    new_history = history[:retry_data.index]
+    previous_prompt = history[retry_data.index]['content']
+    yield from generate(previous_prompt, chat_history = new_history, max_new_tokens = 1024, temperature = 0.6, top_p = 0.9, top_k = 50, repetition_penalty = 1.2)
+
+def handle_like(data: gr.LikeData):
+    if data.liked:
+        print("You upvoted this response: ", data.value)
+    else:
+        print("You downvoted this response: ", data.value)
+
+def handle_undo(history, undo_data: gr.UndoData):
+    chatbot = history[:undo_data.index]
+    prompt = history[undo_data.index]['content']
+    return chatbot, prompt
+
+def chat_examples_fill(data: gr.SelectData):
+    yield from generate(data.value['text'], chat_history = [], max_new_tokens = 1024, temperature = 0.6, top_p = 0.9, top_k = 50, repetition_penalty = 1.2)
+
+
+with gr.Blocks(theme=gr.themes.Soft(), fill_height=True) as demo:
+    with gr.Column(elem_id="container", scale=1):
+        chatbot = gr.Chatbot(
+            label="Llama3.2 3B Instruct Chatbotw using Gradio 5",
+            show_label=False,
+            type="messages",
+            scale=1,
+            suggestions = [
+                {"text": "How many R are there in a Strawberry?"},
+                {"text": "What is the meaning of life for an AI?"},
+                {"text": "Are tomatoes vegetables?"},
+                {"text": "There's a llama in my garden 😱 What should I do?"},
+                {"text": "What is the best way to open a can of worms?"},
+                {"text": "The odd numbers in this group add up to an even number: 15, 32, 5, 13, 82, 7, 1. "},
+                {"text": 'How to setup a human base on Mars? Give short answer.'},
+                {"text": 'Explain theory of relativity to me like I’m 8 years old.'},
+                {"text": 'What is 9,000 * 9,000?'},
+                {"text": 'Write a pun-filled happy birthday message to my friend Alex.'},
+                {"text": 'Justify why a penguin might make a good king of the jungle.'}
+                ],
+            placeholder = PLACEHOLDER,
+            )
+
+    msg = gr.Textbox(submit_btn=True, show_label=False)
+    with gr.Accordion('Additional inputs', open=False):
+        max_new_tokens = gr.Slider(label="Max new tokens", minimum=1, maximum=10, step=1, value=23, )
+        temperature = gr.Slider(label="Temperature",minimum=0.1, maximum=4.0, step=0.1, value=0.6,)
+        top_p = gr.Slider(label="Top-p (nucleus sampling)", minimum=0.05, maximum=1.0, step=0.05, value=0.9, )
+        top_k = gr.Slider(label="Top-k", minimum=1, maximum=1000, step=1, value=50, )
+        repetition_penalty = gr.Slider(label="Repetition penalty", minimum=1.0, maximum=2.0, step=0.05, value=1.2, )
+
+    msg.submit(generate, [msg, chatbot, max_new_tokens, temperature, top_p, top_k, repetition_penalty], [msg, chatbot])
+    chatbot.retry(handle_retry, chatbot, [msg, chatbot])
+    chatbot.like(handle_like, None, None)
+    chatbot.undo(handle_undo, chatbot, [chatbot, msg])
+    chatbot.suggestion_select(chat_examples_fill, None, [msg, chatbot] )
+
+
+demo.launch()