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import gradio as gr |
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import torch |
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from transformers import ( |
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AutoModelForCausalLM, |
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AutoTokenizer, |
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TextIteratorStreamer, |
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) |
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import os |
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from threading import Thread |
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import spaces |
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import time |
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import subprocess |
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subprocess.run( |
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"pip install flash-attn --no-build-isolation", |
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env={"FLASH_ATTENTION_SKIP_CUDA_BUILD": "TRUE"}, |
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shell=True, |
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) |
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DESCRIPTION = """\ |
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# Phi-3-mini-4k-instruct |
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|
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This Space demonstrates the open source [microsoft/Phi-3-mini-4k-instruct](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct) model. |
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- refer to the [Hugging Face Paper page for Phi 3](https://huggingface.co/papers/2404.14219) for more info. |
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""" |
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token = os.environ["HF_TOKEN"] |
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model = AutoModelForCausalLM.from_pretrained( |
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"microsoft/Phi-3-mini-4k-instruct", |
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token=token, |
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trust_remote_code=True, |
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) |
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tok = AutoTokenizer.from_pretrained("microsoft/Phi-3-mini-4k-instruct", token=token) |
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terminators = [ |
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tok.eos_token_id, |
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] |
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if torch.cuda.is_available(): |
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device = torch.device("cuda") |
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print(f"Using GPU: {torch.cuda.get_device_name(device)}") |
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else: |
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device = torch.device("cpu") |
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print("Using CPU") |
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model = model.to(device) |
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@spaces.GPU(duration=60) |
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def chat(message, history, temperature, do_sample, max_tokens): |
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chat = [] |
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for item in history: |
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chat.append({"role": "user", "content": item[0]}) |
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if item[1] is not None: |
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chat.append({"role": "assistant", "content": item[1]}) |
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chat.append({"role": "user", "content": message}) |
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messages = tok.apply_chat_template(chat, tokenize=False, add_generation_prompt=True) |
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model_inputs = tok([messages], return_tensors="pt").to(device) |
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streamer = TextIteratorStreamer( |
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tok, timeout=20.0, skip_prompt=True, skip_special_tokens=True |
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) |
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generate_kwargs = dict( |
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model_inputs, |
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streamer=streamer, |
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max_new_tokens=max_tokens, |
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do_sample=True, |
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temperature=temperature, |
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eos_token_id=terminators, |
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) |
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if temperature == 0: |
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generate_kwargs["do_sample"] = False |
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t = Thread(target=model.generate, kwargs=generate_kwargs) |
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t.start() |
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partial_text = "" |
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for new_text in streamer: |
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partial_text += new_text |
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yield partial_text |
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yield partial_text |
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demo = gr.ChatInterface( |
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fn=chat, |
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examples=[ |
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["Explain quantum physics in 5 words or less:"], |
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["Question: What do you call a bear with no teeth?\nAnswer:"], |
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], |
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additional_inputs_accordion=gr.Accordion( |
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label="⚙️ Parameters", open=False, render=False |
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), |
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additional_inputs=[ |
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gr.Slider( |
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minimum=0, maximum=1, step=0.1, value=0.9, label="Temperature", render=False |
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), |
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gr.Checkbox(label="Sampling", value=True), |
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gr.Slider( |
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minimum=128, |
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maximum=4096, |
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step=1, |
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value=512, |
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label="Max new tokens", |
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render=False, |
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), |
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], |
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stop_btn="Stop Generation", |
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title="Chat with Microsoft PHI", |
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description=DESCRIPTION, |
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) |
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demo.launch() |
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