Upload example_inference.ipynb
Browse files- example_inference.ipynb +890 -0
example_inference.ipynb
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| 1 |
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{
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| 2 |
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"nbformat": 4,
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| 3 |
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"nbformat_minor": 0,
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| 4 |
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"metadata": {
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| 5 |
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"colab": {
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| 6 |
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"Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in /usr/local/lib/python3.10/dist-packages (from torchvision->timm) (9.4.0)\n",
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"Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch>=1.7->timm) (2.1.3)\n",
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"Installing collected packages: timm\n",
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| 757 |
+
"Successfully installed timm-0.9.7\n"
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+
]
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+
}
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+
]
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+
},
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| 762 |
+
{
|
| 763 |
+
"cell_type": "code",
|
| 764 |
+
"source": [
|
| 765 |
+
"!git clone https://github.com/syncdoth/RetNet"
|
| 766 |
+
],
|
| 767 |
+
"metadata": {
|
| 768 |
+
"colab": {
|
| 769 |
+
"base_uri": "https://localhost:8080/"
|
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+
},
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+
"id": "HTRD6CdEO-vW",
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+
"outputId": "aac318db-2314-4f98-fa3a-4fbe3d0ae4e9"
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+
},
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| 774 |
+
"execution_count": 3,
|
| 775 |
+
"outputs": [
|
| 776 |
+
{
|
| 777 |
+
"output_type": "stream",
|
| 778 |
+
"name": "stdout",
|
| 779 |
+
"text": [
|
| 780 |
+
"Cloning into 'RetNet'...\n",
|
| 781 |
+
"remote: Enumerating objects: 340, done.\u001b[K\n",
|
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+
"remote: Counting objects: 100% (135/135), done.\u001b[K\n",
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| 783 |
+
"remote: Compressing objects: 100% (41/41), done.\u001b[K\n",
|
| 784 |
+
"remote: Total 340 (delta 113), reused 95 (delta 94), pack-reused 205\u001b[K\n",
|
| 785 |
+
"Receiving objects: 100% (340/340), 202.75 KiB | 7.24 MiB/s, done.\n",
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"Resolving deltas: 100% (202/202), done.\n"
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]
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+
}
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+
]
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},
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{
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+
"cell_type": "code",
|
| 793 |
+
"execution_count": 6,
|
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+
"metadata": {
|
| 795 |
+
"colab": {
|
| 796 |
+
"base_uri": "https://localhost:8080/",
|
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+
"height": 117,
|
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+
"referenced_widgets": [
|
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+
"754146b195d540208987dfbfb62b1e42",
|
| 800 |
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"71baddbb1b0b4593abd31da8f3a7db46",
|
| 801 |
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"1f9127a98eb44a459bfc2afcb9d56f08",
|
| 802 |
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"a3cd941beb884d40bd68c59f911ddbd1",
|
| 803 |
+
"f2de1572030d4cf1a769e569efeae650",
|
| 804 |
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"0e71b6b8eeec4408a5b536e9b22db760",
|
| 805 |
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"4e2c7c7e15234e4db599221802879bac",
|
| 806 |
+
"8dce0bcb78744bb1ad9cad131ccf2dce",
|
| 807 |
+
"dca10a5d095b4061925aa7a68c21809e",
|
| 808 |
+
"0c958f8b9a9f484db6cbc3f5bfc695e8",
|
| 809 |
+
"d0347e27d359452483e0eaf9dcf0679f",
|
| 810 |
+
"46a18ba44e1545449006c308ea555e96",
|
| 811 |
+
"08bd2d6fcd5f490a8954966ea59ae04f",
|
| 812 |
+
"ff958ad6f50b4a598ff87023e2845335",
|
| 813 |
+
"5e73a13aecfd4288b2629ef758576452",
|
| 814 |
+
"e2c87b8d624f4bcaa498f16fefbb0f1c",
|
| 815 |
+
"5f31ce08dc3b41118bf500c31730b621",
|
| 816 |
+
"9858845bd97f4f3ca946638919bc8bf1",
|
| 817 |
+
"60963f8bd5bd4ae2821ebdbede702b81",
|
| 818 |
+
"9999eaa6381946748302ee1ef98bd588",
|
| 819 |
+
"23346e7a33fd444494bfd54dfff2b495",
|
| 820 |
+
"2480073d215a485693e98298f7547da8"
|
| 821 |
+
]
|
| 822 |
+
},
|
| 823 |
+
"id": "RRH_EDOFMgGq",
|
| 824 |
+
"outputId": "a9c49753-9240-4454-ac5a-656c6d4efa2e"
|
| 825 |
+
},
|
| 826 |
+
"outputs": [
|
| 827 |
+
{
|
| 828 |
+
"output_type": "display_data",
|
| 829 |
+
"data": {
|
| 830 |
+
"text/plain": [
|
| 831 |
+
"Downloading model.safetensors: 0%| | 0.00/1.62G [00:00<?, ?B/s]"
|
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+
],
|
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+
"application/vnd.jupyter.widget-view+json": {
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"version_major": 2,
|
| 835 |
+
"version_minor": 0,
|
| 836 |
+
"model_id": "754146b195d540208987dfbfb62b1e42"
|
| 837 |
+
}
|
| 838 |
+
},
|
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+
"metadata": {}
|
| 840 |
+
},
|
| 841 |
+
{
|
| 842 |
+
"output_type": "display_data",
|
| 843 |
+
"data": {
|
| 844 |
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"text/plain": [
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"Downloading (โฆ)neration_config.json: 0%| | 0.00/270 [00:00<?, ?B/s]"
|
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],
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"application/vnd.jupyter.widget-view+json": {
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+
"version_major": 2,
|
| 849 |
+
"version_minor": 0,
|
| 850 |
+
"model_id": "46a18ba44e1545449006c308ea555e96"
|
| 851 |
+
}
|
| 852 |
+
},
|
| 853 |
+
"metadata": {}
|
| 854 |
+
},
|
| 855 |
+
{
|
| 856 |
+
"output_type": "stream",
|
| 857 |
+
"name": "stdout",
|
| 858 |
+
"text": [
|
| 859 |
+
"Generating...\n",
|
| 860 |
+
"็ฎใ่ฆใใใจใๅใฏใใใใใไฝใ่ตทใใใฆๅฏใฆใใใ ใใใใฃ!!ใ ๅฃฐใซๅๅฟใใใฎใงๅฃฐใฎๆนใ่ฆใใใใใจใใใซใฏ่ฆ่ฆใใฎใชใๅฅณๆง\n"
|
| 861 |
+
]
|
| 862 |
+
}
|
| 863 |
+
],
|
| 864 |
+
"source": [
|
| 865 |
+
"from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig\n",
|
| 866 |
+
"\n",
|
| 867 |
+
"MODEL_NAME = \"p1atdev/LightNovel-Intro-RetNet-400M\"\n",
|
| 868 |
+
"\n",
|
| 869 |
+
"device = \"cuda\"\n",
|
| 870 |
+
"\n",
|
| 871 |
+
"tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)\n",
|
| 872 |
+
"model = AutoModelForCausalLM.from_pretrained(\n",
|
| 873 |
+
" MODEL_NAME,\n",
|
| 874 |
+
" trust_remote_code=True,\n",
|
| 875 |
+
").to(device)\n",
|
| 876 |
+
"gen_config = GenerationConfig.from_pretrained(MODEL_NAME)\n",
|
| 877 |
+
"gen_config.max_new_tokens = 32\n",
|
| 878 |
+
"\n",
|
| 879 |
+
"inputs = tokenizer(\"็ฎใ่ฆใใใจใ\", return_tensors=\"pt\", add_special_tokens=False).to(device)\n",
|
| 880 |
+
"\n",
|
| 881 |
+
"print(\"Generating...\")\n",
|
| 882 |
+
"\n",
|
| 883 |
+
"result = model.generate(**inputs, generation_config=gen_config)\n",
|
| 884 |
+
"\n",
|
| 885 |
+
"print(tokenizer.decode(result[0], skip_special_tokens=True))\n",
|
| 886 |
+
"# ็ฎใ่ฆใใใจใ่ฆ็ฅใใฌ็ฉบ้ใซๅฑ
ใใ ใใ......?ใ ๆใใใใใชๅฃฐใๅบใใใจใซ้ๅๆใๆใใใ็ขบใใๆฐไปใใฐ็งใฏ\n"
|
| 887 |
+
]
|
| 888 |
+
}
|
| 889 |
+
]
|
| 890 |
+
}
|