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examples for downloading data and for testing the model
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,159 @@ | ||
| { | ||
| "cells": [ | ||
| { | ||
| "cell_type": "markdown", | ||
| "id": "f1cd554b-2318-41d1-969f-46d7cafd1b40", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "# Testing the model\n", | ||
| "Here we just test our fine-tuned model." | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 1, | ||
| "id": "be1df990-5cbf-4f3e-9059-9fae25f8d3ae", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "def prompt_hf(request, model=\"haesleinhuepf/gemma-2b-it-bia-proof-of-concept2\"):\n", | ||
| " global prompt_hf\n", | ||
| " import transformers\n", | ||
| " import torch\n", | ||
| " \n", | ||
| " if prompt_hf._pipeline is None: \n", | ||
| " prompt_hf._pipeline = transformers.pipeline(\n", | ||
| " \"text-generation\", model=model, model_kwargs={\"torch_dtype\": torch.bfloat16}, device_map=\"auto\",\n", | ||
| " max_new_tokens=200\n", | ||
| " )\n", | ||
| " \n", | ||
| " return prompt_hf._pipeline(request)[0]['generated_text']\n", | ||
| "prompt_hf._pipeline = None" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 2, | ||
| "id": "958d16ef-f3fa-412f-be38-76101d63a2e5", | ||
| "metadata": {}, | ||
| "outputs": [ | ||
| { | ||
| "data": { | ||
| "application/vnd.jupyter.widget-view+json": { | ||
| "model_id": "ade3065e46914ab5b27eac86d175a10a", | ||
| "version_major": 2, | ||
| "version_minor": 0 | ||
| }, | ||
| "text/plain": [ | ||
| "Downloading shards: 0%| | 0/2 [00:00<?, ?it/s]" | ||
| ] | ||
| }, | ||
| "metadata": {}, | ||
| "output_type": "display_data" | ||
| }, | ||
| { | ||
| "name": "stderr", | ||
| "output_type": "stream", | ||
| "text": [ | ||
| "`config.hidden_act` is ignored, you should use `config.hidden_activation` instead.\n", | ||
| "Gemma's activation function will be set to `gelu_pytorch_tanh`. Please, use\n", | ||
| "`config.hidden_activation` if you want to override this behaviour.\n", | ||
| "See https://github.com/huggingface/transformers/pull/29402 for more details.\n" | ||
| ] | ||
| }, | ||
| { | ||
| "data": { | ||
| "application/vnd.jupyter.widget-view+json": { | ||
| "model_id": "adc6bf6e815348649fd315678923a671", | ||
| "version_major": 2, | ||
| "version_minor": 0 | ||
| }, | ||
| "text/plain": [ | ||
| "Loading checkpoint shards: 0%| | 0/2 [00:00<?, ?it/s]" | ||
| ] | ||
| }, | ||
| "metadata": {}, | ||
| "output_type": "display_data" | ||
| }, | ||
| { | ||
| "name": "stderr", | ||
| "output_type": "stream", | ||
| "text": [ | ||
| "Some parameters are on the meta device device because they were offloaded to the cpu.\n", | ||
| "C:\\Users\\rober\\miniconda3\\envs\\genai-gpu\\Lib\\site-packages\\transformers\\models\\gemma\\modeling_gemma.py:482: UserWarning: 1Torch was not compiled with flash attention. (Triggered internally at C:\\cb\\pytorch_1000000000000\\work\\aten\\src\\ATen\\native\\transformers\\cuda\\sdp_utils.cpp:555.)\n", | ||
| " attn_output = torch.nn.functional.scaled_dot_product_attention(\n" | ||
| ] | ||
| }, | ||
| { | ||
| "data": { | ||
| "text/markdown": [ | ||
| "Write Python code for cropping an image in X and Y to coordinates 10-20 and 30-50 respectively.\n", | ||
| "\n", | ||
| "```python\n", | ||
| "import cv2\n", | ||
| "\n", | ||
| "# Load the image\n", | ||
| "image = cv2.imread(\"image.jpg\")\n", | ||
| "\n", | ||
| "# Crop the image\n", | ||
| "cropped_image = image[10:20, 30:50]\n", | ||
| "\n", | ||
| "# Save the cropped image\n", | ||
| "cv2.imwrite(\"cropped_image.jpg\", cropped_image)\n", | ||
| "```\n", | ||
| "\n", | ||
| "**Explanation:**\n", | ||
| "\n", | ||
| "* `cv2.imread(\"image.jpg\")` loads the image from the file \"image.jpg\".\n", | ||
| "* `image[10:20, 30:50]` crops the image by specifying the coordinates of the top-left and bottom-right corners of the crop.\n", | ||
| "* `cv2.imwrite(\"cropped_image.jpg\", cropped_image)` saves the cropped image to the file \"cropped_image.jpg\".\n", | ||
| "\n", | ||
| "**Note:**\n", | ||
| "\n", | ||
| "* The `[10:20, 30:50]` coordinates represent the height" | ||
| ], | ||
| "text/plain": [ | ||
| "<IPython.core.display.Markdown object>" | ||
| ] | ||
| }, | ||
| "metadata": {}, | ||
| "output_type": "display_data" | ||
| } | ||
| ], | ||
| "source": [ | ||
| "from IPython.display import Markdown, display\n", | ||
| "result = prompt_hf(\"Write Python code for cropping an image in X and Y to coordinates 10-20 and 30-50\")\n", | ||
| "display(Markdown(result))" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "id": "d0a04961-d213-47d2-a218-8eacd242a35d", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [] | ||
| } | ||
| ], | ||
| "metadata": { | ||
| "kernelspec": { | ||
| "display_name": "Python 3 (ipykernel)", | ||
| "language": "python", | ||
| "name": "python3" | ||
| }, | ||
| "language_info": { | ||
| "codemirror_mode": { | ||
| "name": "ipython", | ||
| "version": 3 | ||
| }, | ||
| "file_extension": ".py", | ||
| "mimetype": "text/x-python", | ||
| "name": "python", | ||
| "nbconvert_exporter": "python", | ||
| "pygments_lexer": "ipython3", | ||
| "version": "3.11.9" | ||
| } | ||
| }, | ||
| "nbformat": 4, | ||
| "nbformat_minor": 5 | ||
| } |
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