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 = '''

Test Model

''' LICENSE = """

--- """ PLACEHOLDER = """ """ 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("Orenguteng/Llama-3-8B-Lexi-Uncensored") model = AutoModelForCausalLM.from_pretrained("Orenguteng/Llama-3-8B-Lexi-Uncensored", 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, top_p: float, system_prompt: str ) -> 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. top_p (float): The top_p value for nucleus sampling. system_prompt (str): The system prompt to guide the conversation. Returns: str: The generated response. """ conversation = [{"role": "system", "content": system_prompt}] for user, assistant in history: conversation.append({"role": "user", "content": user}) conversation.append({"role": "assistant", "content": assistant}) conversation.append({"role": "user", "content": message}) input_ids = tokenizer.apply_chat_template(conversation, return_tensors="pt").to(model.device) attention_mask = input_ids.ne(tokenizer.pad_token_id).long() streamer = TextIteratorStreamer(tokenizer, timeout=10.0, skip_prompt=True, skip_special_tokens=True) generate_kwargs = dict( input_ids= input_ids, attention_mask=attention_mask, streamer=streamer, max_new_tokens=max_new_tokens, do_sample=True, temperature=temperature, top_p=top_p, eos_token_id=terminators, pad_token_id=tokenizer.eos_token_id, ) # 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 = [] first_chunk = True for text in streamer: if first_chunk and text.startswith("assistant"): text = text[len("assistant"):].lstrip(": \n") # Remove "assistant" and any following symbols first_chunk = False outputs.append(text) yield "".join(outputs) # Gradio block chatbot=gr.Chatbot(height=450, placeholder=PLACEHOLDER, label='Gradio ChatInterface', type='messages') with gr.Blocks(fill_height=True, css=css) as aida: gr.Markdown(DESCRIPTION) gr.ChatInterface( fn=chat_llama3_8b, chatbot=None, 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=0.8, label="Temperature", render=False), gr.Slider(minimum=128, maximum=4096, step=1, value=4096, label="Max new tokens", render=False ), gr.Slider(minimum=0, maximum=1, step=0.1, value=0.9, label="Top_p", render=False), gr.Textbox(lines=2, placeholder="Enter system prompt here...", label="System Prompt", render=False), ], examples=[ ['Who Are you?'] ], cache_examples=False, ) if __name__ == "__main__": aida.launch(share=True)