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| import gradio as gr | |
| import spaces | |
| import torch | |
| import transformers | |
| import torch | |
| from transformers import AutoModelForCausalLM, AutoTokenizer | |
| # model_name = "meta-llama/Meta-Llama-3-8B-Instruct" | |
| model_name = "mistralai/Mistral-7B-Instruct-v0.2" | |
| 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 = [] | |
| # Check if history is None or empty and handle accordingly | |
| if history: | |
| for user_msg, assistant_msg in history: | |
| messages.append({"role": "user", "content": user_msg}) | |
| messages.append({"role": "assistant", "content": assistant_msg}) | |
| # Always add the current user message | |
| messages.append({"role": "user", "content": message}) | |
| # Construct the prompt using the pipeline's tokenizer | |
| prompt = pipeline.tokenizer.apply_chat_template( | |
| messages, | |
| tokenize=False, | |
| add_generation_prompt=True | |
| ) | |
| # Generate the response | |
| terminators = [ | |
| pipeline.tokenizer.eos_token_id, | |
| pipeline.tokenizer.convert_tokens_to_ids("") | |
| ] | |
| # Adjust the temperature slightly above given to ensure variety | |
| adjusted_temp = temperature + 0.1 | |
| # Generate outputs with adjusted parameters | |
| outputs = pipeline( | |
| prompt, | |
| max_new_tokens=max_new_tokens, | |
| do_sample=True, | |
| temperature=adjusted_temp, | |
| top_p=0.9 | |
| ) | |
| # Extract the generated text, skipping the length of the prompt | |
| generated_text = outputs[0]["generated_text"] | |
| return generated_text[len(prompt):] # Return the new part of the conversation | |
| # Update Gradio interface setup | |
| gr.Interface( | |
| fn=chat_function, | |
| inputs=[ | |
| gr.Textbox(placeholder="Enter your message here", label="Your Message"), | |
| gr.JSON(label="Conversation History (format as [[user, assistant], ...])"), # Without optional | |
| gr.Textbox(label="System Prompt"), | |
| gr.Slider(512, 4096, label="Max New Tokens"), | |
| gr.Slider(0.0, 1.0, step=0.1, label="Temperature") | |
| ], | |
| outputs=gr.Textbox(label="AI Response") | |
| ).launch() | |
| # 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, | |
| # eos_token_id=terminators, | |
| # do_sample=True, | |
| # temperature=temp, | |
| # top_p=0.9, | |
| # ) | |
| # 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) | |
| # # 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.'] | |
| # ], | |
| # cache_examples=False, | |
| # ) | |
| # gr.Markdown(LICENSE) | |
| # if __name__ == "__main__": | |
| # demo.launch() |