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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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BitsAndBytesConfig, |
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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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token = os.environ["HF_TOKEN"] |
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quantization_config = BitsAndBytesConfig( |
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load_in_4bit=True, bnb_4bit_compute_dtype=torch.float16 |
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) |
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model = AutoModelForCausalLM.from_pretrained( |
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"microsoft/Phi-3-mini-128k-instruct", quantization_config=quantization_config, token=token,trust_remote_code=True |
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) |
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tok = AutoTokenizer.from_pretrained("microsoft/Phi-3-mini-128k-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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@spaces.GPU(duration=60) |
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def chat(message, history, temperature,do_sample, max_tokens): |
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start_time = time.time() |
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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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first_token_time = None |
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for new_text in streamer: |
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if not first_token_time: |
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first_token_time = time.time() - start_time |
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partial_text += new_text |
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yield partial_text |
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total_time = time.time() - start_time |
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tokens = len(tok.tokenize(partial_text)) |
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tokens_per_second = tokens / total_time if total_time > 0 else 0 |
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timing_info = f"\n\nTime taken to first token: {first_token_time:.2f} seconds\nTokens per second: {tokens_per_second:.2f}" |
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yield partial_text + timing_info |
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demo = gr.ChatInterface( |
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fn=chat, |
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examples=[["Write me a poem about Machine Learning."]], |
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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 LLMs", |
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description="Now Running [microsoft/Phi-3-mini-128k-instruct](https://huggingface.com/microsoft/Phi-3-mini-128k-instruct) in 4bit" |
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) |
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demo.launch() |
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