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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) | |
| 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|>") | |
| ] | |
| 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="⚙️ 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.'], | |
| ['Justify why a penguin might make a good king of the jungle.'] | |
| ], | |
| cache_examples=False, | |
| ) | |
| gr.Markdown(LICENSE) | |
| if __name__ == "__main__": | |
| demo.launch() | |