import gradio as gr import torch from transformers import ( AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer, ) import os from threading import Thread import spaces import time import subprocess subprocess.run('pip install flash-attn --no-build-isolation', env={'FLASH_ATTENTION_SKIP_CUDA_BUILD': "TRUE"}, shell=True) token = os.environ["HF_TOKEN"] model = AutoModelForCausalLM.from_pretrained( "microsoft/Phi-3-mini-128k-instruct", token=token,trust_remote_code=True ) tok = AutoTokenizer.from_pretrained("microsoft/Phi-3-mini-128k-instruct", token=token) terminators = [ tok.eos_token_id, ] if torch.cuda.is_available(): device = torch.device("cuda") print(f"Using GPU: {torch.cuda.get_device_name(device)}") else: device = torch.device("cpu") print("Using CPU") model = model.to(device) # Dispatch Errors @spaces.GPU(duration=60) def chat(message, history, temperature,do_sample, max_tokens): chat = [] for item in history: chat.append({"role": "user", "content": item[0]}) if item[1] is not None: chat.append({"role": "assistant", "content": item[1]}) chat.append({"role": "user", "content": message}) messages = tok.apply_chat_template(chat, tokenize=False, add_generation_prompt=True) model_inputs = tok([messages], return_tensors="pt").to(device) streamer = TextIteratorStreamer( tok, timeout=20.0, skip_prompt=True, skip_special_tokens=True ) generate_kwargs = dict( model_inputs, streamer=streamer, max_new_tokens=max_tokens, do_sample=True, temperature=temperature, eos_token_id=terminators, ) if temperature == 0: generate_kwargs['do_sample'] = False t = Thread(target=model.generate, kwargs=generate_kwargs) t.start() partial_text = "" for new_text in streamer: partial_text += new_text yield partial_text yield partial_text demo = gr.ChatInterface( fn=chat, examples=[["Write me a poem about Machine Learning."]], # multimodal=False, additional_inputs_accordion=gr.Accordion( label="⚙️ Parameters", open=False, render=False ), additional_inputs=[ gr.Slider( minimum=0, maximum=1, step=0.1, value=0.9, label="Temperature", render=False ), gr.Checkbox(label="Sampling",value=True), gr.Slider( minimum=128, maximum=4096, step=1, value=512, label="Max new tokens", render=False, ), ], stop_btn="Stop Generation", title="Chat With LLMs", description="Now Running [microsoft/Phi-3-mini-128k-instruct](https://huggingface.com/microsoft/Phi-3-mini-128k-instruct)" ) demo.launch()