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working replicate demo
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Dan
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**King Abdullah University of Science and Technology**
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<a href='https://minigpt-4.github.io'><img src='https://img.shields.io/badge/Project-Page-Green'></a> <a href='https://arxiv.org/abs/2304.10592'><img src='https://img.shields.io/badge/Paper-Arxiv-red'></a> <a href='https://huggingface.co/spaces/Vision-CAIR/minigpt4'><img src='https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue'></a> <a href='https://huggingface.co/Vision-CAIR/MiniGPT-4'><img src='https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Model-blue'></a> [](https://colab.research.google.com/drive/1OK4kYsZphwt5DXchKkzMBjYF6jnkqh4R?usp=sharing) [](https://www.youtube.com/watch?v=__tftoxpBAw&feature=youtu.be)
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<a href='https://minigpt-4.github.io'><img src='https://img.shields.io/badge/Project-Page-Green'></a> <a href='https://arxiv.org/abs/2304.10592'><img src='https://img.shields.io/badge/Paper-Arxiv-red'></a> <a href='https://huggingface.co/spaces/Vision-CAIR/minigpt4'><img src='https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue'></a> <a href='https://huggingface.co/Vision-CAIR/MiniGPT-4'><img src='https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Model-blue'></a> [](https://colab.research.google.com/drive/1OK4kYsZphwt5DXchKkzMBjYF6jnkqh4R?usp=sharing) [](https://www.youtube.com/watch?v=__tftoxpBAw&feature=youtu.be) <a href="https://replicate.com/daanelson/minigpt-4"><img src="https://replicate.com/daanelson/minigpt-4/badge"></a>
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## News
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27
cog.yaml
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27
cog.yaml
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build:
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gpu: true
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cuda: "11.3"
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system_packages:
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- "libgl1-mesa-glx"
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- "libglib2.0-0"
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python_version: "3.8"
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python_packages:
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- "torch==1.12.1"
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- "torchvision"
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- "transformers==4.28.1"
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- "gradio==3.28.1"
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- "omegaconf==2.1.2"
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- "iopath"
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- "timm==0.6.13"
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- "webdataset==0.2.48"
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- "opencv-python==4.7.0.72"
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- "tensorizer"
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- "decord==0.6.0"
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- "sentencepiece"
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run:
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- "echo 'deb [signed-by=/usr/share/keyrings/cloud.google.gpg] https://packages.cloud.google.com/apt cloud-sdk main' | tee -a /etc/apt/sources.list.d/google-cloud-sdk.list"
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- "curl https://packages.cloud.google.com/apt/doc/apt-key.gpg | apt-key --keyring /usr/share/keyrings/cloud.google.gpg add -"
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- "apt-get update && apt-get install google-cloud-cli"
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predict: "predict.py:Predictor"
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predict.py
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predict.py
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from cog import BasePredictor, Input, Path
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from minigpt4.common.config import Config
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import torch
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import argparse
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from PIL import Image
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from minigpt4.models import MiniGPT4
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from minigpt4.common.registry import registry
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from minigpt4.conversation.conversation import Chat, CONV_VISION
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import os
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# setting cache directory
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os.environ["TORCH_HOME"] = "/src/model_cache"
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class Predictor(BasePredictor):
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def setup(self):
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args = argparse.Namespace()
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args.cfg_path = "/src/eval_configs/minigpt4_eval.yaml"
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args.gpu_id = 0
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args.options = []
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config = Config(args)
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model = MiniGPT4.from_config(config.model_cfg).to("cuda")
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vis_processor_cfg = config.datasets_cfg.cc_sbu_align.vis_processor.train
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vis_processor = registry.get_processor_class(
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vis_processor_cfg.name
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).from_config(vis_processor_cfg)
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self.chat = Chat(model, vis_processor, device="cuda")
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def predict(
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self,
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image: Path = Input(description="Image to discuss"),
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prompt: str = Input(description="Prompt for mini-gpt4 regarding input image"),
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num_beams: int = Input(
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description="Number of beams for beam search decoding",
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default=3,
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ge=1,
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le=10,
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),
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temperature: float = Input(
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description="Temperature for generating tokens, lower = more predictable results",
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default=1.0,
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ge=0.01,
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le=2.0,
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),
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top_p: float = Input(
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description="Sample from the top p percent most likely tokens",
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default=0.9,
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ge=0.0,
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le=1.0,
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),
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repetition_penalty: float = Input(
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description="Penalty for repeated words in generated text; 1 is no penalty, values greater than 1 discourage repetition, less than 1 encourage it.",
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default=1.0,
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ge=0.01,
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le=5,
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),
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max_new_tokens: int = Input(
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description="Maximum number of new tokens to generate", ge=1, default=3000
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),
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max_length: int = Input(
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description="Total length of prompt and output in tokens",
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ge=1,
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default=4000,
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),
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) -> str:
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img_list = []
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image = Image.open(image).convert("RGB")
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with torch.inference_mode():
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chat_state = CONV_VISION.copy()
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self.chat.upload_img(image, chat_state, img_list)
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self.chat.ask(prompt, chat_state)
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answer = self.chat.answer(
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conv=chat_state,
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img_list=img_list,
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num_beams=num_beams,
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temperature=temperature,
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max_new_tokens=max_new_tokens,
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max_length=max_length,
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top_p=top_p,
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repetition_penalty=repetition_penalty,
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)
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return answer[0]
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