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null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": "" } } } } }, "cells": [ { "cell_type": "markdown", "metadata": { "id": "view-in-github", "colab_type": "text" }, "source": [ "\"Open" ] }, { "cell_type": "markdown", "source": [ "## Aria in Transformers" ], "metadata": { "id": "RDVhrVyerMcE" } }, { "cell_type": "markdown", "source": [ "Aria, all-powerful vision language model by Rhymes AI is now available in transformers! It's a 25.6B model that takes around 32 GB VRAM as is and requires around 35GB VRAM (in total) for inference when used with bfloat16 precision, so A100 with 40GB on Colab is enough to run it. You can also opt for lower precision with 4/8-bit quantization which we're demonstrating in this notebook.\n", "\n", "As of Dec 11 the model is merged to transformers main but not yet released so we install the latest transformers." ], "metadata": { "id": "U3kOd63orQ_z" } }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "IhF6znFdqjfH", "outputId": "0e1a2fcb-343c-421e-e4ab-27e41f3d3bd3" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ " Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n", " Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n", " Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m69.1/69.1 MB\u001b[0m \u001b[31m32.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.0/3.0 MB\u001b[0m \u001b[31m85.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[?25h Building wheel for transformers (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n" ] } ], "source": [ "!pip install -q git+https://github.com/huggingface/transformers.git bitsandbytes accelerate" ] }, { "cell_type": "markdown", "source": [ "We can load the model using `AriaForConditionalGeneration`, and processor with `AriaProcessor` class. We put a `LOAD_4BIT` flag here for your convenience to load in 4-bit to save memory." ], "metadata": { "id": "Z3vlNVParKxM" } }, { "cell_type": "code", "source": [ "import requests\n", "import torch\n", "from PIL import Image\n", "from transformers import AriaForConditionalGeneration, AriaProcessor, BitsAndBytesConfig\n", "\n", "LOAD_4BIT = False\n", "\n", "bnb_config = BitsAndBytesConfig(\n", " load_in_4bit=True,\n", " bnb_4bit_compute_dtype=torch.bfloat16,\n", " bnb_4bit_use_double_quant=True)\n", "\n", "model = AriaForConditionalGeneration.from_pretrained(\"rhymes-ai/Aria\", device_map=\"auto\",\n", " quantization_config=bnb_config if LOAD_4BIT else None,\n", " torch_dtype=torch.bfloat16)\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 720, "referenced_widgets": [ "32658bb9aeca462286fd73f4d803dad5", "a58fece50f944faeb1336f08b3f5fbdb", "6cdf95f1f9994c9db2b2fc247ea5ea98", "ac5d7152f50d45ce8bb293bca99e2a1e", "d8a080bf5fde48f4b1b640b84c9eaa70", 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`HF_TOKEN` does not exist in your Colab secrets.\n", "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n", "You will be able to reuse this secret in all of your notebooks.\n", "Please note that authentication is recommended but still optional to access public models or datasets.\n", " warnings.warn(\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "config.json: 0%| | 0.00/1.22k [00:00