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>š 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>š¦ 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) # Gradio block chatbot=gr.Chatbot(height=450, placeholder=PLACEHOLDER, label='Gradio ChatInterface') 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="āļø 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 Iām 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, ) gr.Markdown(LICENSE) if __name__ == "__main__": demo.launch()