{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "YlId6SITh8ph" }, "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "TZHREFURkx9F", "outputId": "3c586d9b-5250-45b4-b205-18cedf2cbb7e" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "/\n" ] } ], "source": [] }, { "cell_type": "code", "execution_count": 1, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "kUv_Qy_dh-Ik", "outputId": "5cdf5f76-702d-4c88-dff2-c781eda3f4ad" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Cloning into 'alpaca-lora'...\n", "remote: Enumerating objects: 299, done.\u001b[K\n", "remote: Counting objects: 100% (142/142), done.\u001b[K\n", "remote: Compressing objects: 100% (45/45), done.\u001b[K\n", "remote: Total 299 (delta 121), reused 101 (delta 97), pack-reused 157\u001b[K\n", "Receiving objects: 100% (299/299), 7.03 MiB | 4.60 MiB/s, done.\n", "Resolving deltas: 100% (183/183), done.\n" ] } ], "source": [ "# %%\n", "!git clone https://github.com/tloen/alpaca-lora.git" ] }, { "cell_type": "markdown", "metadata": { "id": "g5PKCTa_iAUq" }, "source": [ "\n", "\n", "# # Alpaca GPT-2" ] }, { "cell_type": "code", "source": [ "\n", "%cd alpaca-lora/" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "e14PzovIEZ2B", "outputId": "9fc6b6d1-d32f-4b71-9e7d-575ab610f47b" }, "execution_count": 2, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "/content/alpaca-lora\n" ] } ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 761, "referenced_widgets": [ "7835ab6d7dc94fcf827a2db768e42f64", "bd23dedad5fd4558a6dd26c4baef5f60", "747b8914e424424682938e2b773b2424", "e3cb2a03e1894b47aab02c6bdfd2c46c", "2e6b0bc5907b4afb8633360c462db5dc", "be33258d7dc74f718eb972cfe64094c1", "f6880ecdbe464ed8926ee94d2bc03434", 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StepTraining Loss
13.505800
23.589200
33.581400
43.499100
53.446200
63.463100
73.523100
83.504400
93.536200
103.503900
113.407800
123.393300
133.435200
143.501500
153.379000
163.408300
173.385700
183.351200
193.319400
203.318500
213.174300
223.284800
233.165500
243.162600
253.222300
263.156100
273.112700
282.980200
292.966500
302.978900
312.943500
322.979800
332.897400
342.844700
352.779500
362.761400
372.734000
382.656200
392.685300
402.639600
412.586300
422.522400
432.478000
442.511800
452.516400
462.412900
472.380500
482.263900
492.313200
502.256700
512.231000
522.109000
532.242600
542.147000
552.127600
562.075800
572.053100
582.060600
592.026200
602.050500
612.001800
621.939500
632.017000
641.955400
651.983100
661.888200
671.942800
681.955000
691.830700
701.889600
711.918400
721.819400
731.848000
741.874500
751.846700
761.925400
771.741800
781.873700
791.889100
801.823100
811.782900
821.819800
831.854300
841.802200
851.778000
861.755700
871.844600
881.778300
891.803900
901.841800
911.763200
921.800000
931.804400
941.804800
951.754500
961.796900
971.848600
981.746900
991.703900
1001.775400
1011.773300
1021.748300
1031.740100
1041.795300
1051.850800
1061.828400
1071.732500
1081.841300
1091.774700
1101.814700
1111.702000
1121.734600
1131.790700
1141.635100
1151.805300
1161.776300
1171.855000
1181.778700
1191.743800
1201.793500
1211.743100
1221.752300
1231.797400
1241.755000
1251.679400
1261.724200
1271.759600
1281.793600
1291.723000
1301.817600
1311.759500
1321.719300
1331.709900
1341.696500
1351.736700
1361.713900
1371.689000
1381.691400
1391.707600
1401.714700
1411.778000
1421.759700
1431.746300
1441.669000
1451.716300
1461.769200
1471.669500
1481.747200
1491.730200
1501.714300
1511.665400
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1531.733000
1541.740300
1551.650800
1561.637600
1571.729200
1581.719300
1591.689800
1601.681200
1611.750400
1621.706700
1631.762800
1641.646600
1651.660200
1661.701700
1671.765900
1681.605600
1691.772600
1701.814300
1711.759800
1721.718600
1731.639600
1741.713800
1751.755000
1761.771900
1771.714100
1781.737800
1791.724300
1801.674700
1811.639800
1821.661000
1831.736600
1841.655200
1851.765800
1861.707400
1871.705600
1881.686100
1891.716100
1901.691500
1911.640000
1921.716100
1931.680800
1941.698200
1951.714100
1961.775800
1971.679100
1981.676600
1991.710200
2001.721300
2011.699500
2021.688800
2031.750100
2041.687800
2051.703300
2061.679600
2071.611800
2081.687100
2091.663500
2101.738600
2111.674800
2121.742700
2131.701000
2141.727100
2151.639400
2161.662600
2171.738500
2181.622400
2191.718900
2201.668300
2211.686200
2221.684600
2231.694200
2241.667400
2251.679600
2261.652500
2271.747100
2281.713300
2291.689700
2301.735200
2311.629800
2321.638500
2331.737700
2341.673000
2351.753100
2361.729100
2371.713900
2381.811600
2391.632600
2401.666300
2411.667900
2421.659300
2431.738200
2441.711500
2451.629500
2461.741400
2471.674200
2481.685500
2491.641000
2501.646500
2511.651900
2521.704700
2531.668500
2541.641100
2551.618600
2561.678100
2571.645500
2581.666800
2591.672900
2601.708300
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2621.777200
2631.685300
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2651.716800
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2701.608900
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23.589200
33.581400
43.499100
53.446200
63.463100
73.523100
83.504400
93.536200
103.503900
113.407800
123.393300
133.435200
143.501500
153.379000
163.408300
173.385700
183.351200
193.319400
203.318500
213.174300
223.284800
233.165500
243.162600
253.222300
263.156100
273.112700
282.980200
292.966500
302.978900
312.943500
322.979800
332.897400
342.844700
352.779500
362.761400
372.734000
382.656200
392.685300
402.639600
412.586300
422.522400
432.478000
442.511800
452.516400
462.412900
472.380500
482.263900
492.313200
502.256700
512.231000
522.109000
532.242600
542.147000
552.127600
562.075800
572.053100
582.060600
592.026200
602.050500
612.001800
621.939500
632.017000
641.955400
651.983100
661.888200
671.942800
681.955000
691.830700
701.889600
711.918400
721.819400
731.848000
741.874500
751.846700
761.925400
771.741800
781.873700
791.889100
801.823100
811.782900
821.819800
831.854300
841.802200
851.778000
861.755700
871.844600
881.778300
891.803900
901.841800
911.763200
921.800000
931.804400
941.804800
951.754500
961.796900
971.848600
981.746900
991.703900
1001.775400
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1021.748300
1031.740100
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1071.732500
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1891.716100
1901.691500
1911.640000
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1941.698200
1951.714100
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1971.679100
1981.676600
1991.710200
2001.721300
2011.699500
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2051.703300
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2151.639400
2161.662600
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" ] }, "metadata": {} } ], "source": [ "trainer = Trainer(\n", " model=model,\n", " train_dataset=data[\"train\"],\n", " args=TrainingArguments(\n", " per_device_train_batch_size=MICRO_BATCH_SIZE,\n", " gradient_accumulation_steps=GRADIENT_ACCUMULATION_STEPS,\n", " warmup_steps=100,\n", " num_train_epochs=EPOCHS,\n", " learning_rate=LEARNING_RATE,\n", " fp16=True,\n", " logging_steps=1,\n", " output_dir=\"gpt2-alpaca\",\n", " save_total_limit=3,\n", " ),\n", " data_collator=transformers.DataCollatorForLanguageModeling(tokenizer, mlm=False),\n", ")\n", "model.config.use_cache = False\n", "trainer.train(resume_from_checkpoint=False)\n", "\n", "model.save_pretrained(\"gpt2-alpaca\")" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 568 }, "id": "8rPUnadrjq7H", "outputId": "49ea41e1-d216-4e83-acda-d4590f2c1b48" }, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n" ] }, { "output_type": "error", "ename": "RuntimeError", "evalue": "ignored", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 11\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 12\u001b[0m Response:\"\"\"\n\u001b[0;32m---> 13\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtext_generator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mPROMPT\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_length\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m200\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdo_sample\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtemperature\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.7\u001b[0m\u001b[0;34m,\u001b[0m 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_global_backward_hooks\n\u001b[1;32m 1193\u001b[0m or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1194\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1195\u001b[0m \u001b[0;31m# Do not call functions when jit is used\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1196\u001b[0m \u001b[0mfull_backward_hooks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnon_full_backward_hooks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/torch/nn/modules/sparse.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m 158\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 159\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 160\u001b[0;31m return F.embedding(\n\u001b[0m\u001b[1;32m 161\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mweight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpadding_idx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax_norm\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 162\u001b[0m self.norm_type, self.scale_grad_by_freq, self.sparse)\n", "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/torch/nn/functional.py\u001b[0m in \u001b[0;36membedding\u001b[0;34m(input, weight, padding_idx, max_norm, norm_type, scale_grad_by_freq, sparse)\u001b[0m\n\u001b[1;32m 2208\u001b[0m \u001b[0;31m# remove once script supports set_grad_enabled\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2209\u001b[0m \u001b[0m_no_grad_embedding_renorm_\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mweight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_norm\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnorm_type\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2210\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0membedding\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mweight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m 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(when checking argument for argument index in method wrapper__index_select)" ] } ], "source": [ "#!pip install transformers\n", "from transformers import GPT2Tokenizer, GPT2LMHeadModel, pipeline\n", "device = torch.device(\"cuda\")\n", "model.cuda()\n", "text_generator = pipeline(\"text-generation\", model=model, tokenizer=tokenizer)\n", "\n", "PROMPT = \"\"\"Below is an instruction that describes a task. Write a response that appropriately completes the request.\n", "\n", "Instruction:\n", "Tell me something about alpacas.\n", "\n", "Response:\"\"\"\n", "result = text_generator(PROMPT, max_length=200, do_sample=True, temperature=0.7, top_k=0, top_p=0.95)\n", "\n", "print(result[0]['generated_text'])\n", "\n", "\n", "\n", "\n" ] }, { "cell_type": "code", "source": [ "from huggingface_hub import notebook_login\n", "\n", "notebook_login()" ], "metadata": { "id": "CUGnn14MVZo7", "outputId": "521a6312-9060-4449-bc02-87247e822d8f", "colab": { "base_uri": "https://localhost:8080/", "height": 443, "referenced_widgets": [ "00075ac284e64a6ebf736057951006c5", "0dfd3195bbe74018bb9c25e7f83f5122", "e0025684846e4767ae64804620404d52", "9f9518d9f32a4b4598c0a36890ed495e", "f58ae604b573463f8c4a53ad843641cb", "fe7147915ae94f60908f61bc6d5e9522", "2efc71ec450b43288bd401a0353ff8c0", "fbd5dc4c4ec34fc286532575fcee9d17", "b33801f279974f06a781bea9cd1b374f", "87071be8de0042a19f0d556bfef7fb89", "e145c10cca084ed490c70231bb7572d0", "156e613615164f1ebe457ef115d51411", "1a2d4ceb3a15469b808cdc592ec4fcf3", "7e93f83c87884bd38266d2f6df1bffa8", "e1500d3bf1d84c0b93d2456f0f90aee1", "185040f0977c4f06bb97cc8818289de0", "f5fb72ca0afd4c9db13401a63ecf3740" ] } }, "execution_count": 18, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Token is valid.\n", "Your token has been saved to /root/.cache/huggingface/token\n", "Login successful\n" ] } ] }, { "cell_type": "code", "source": [ "model.push_to_hub(\"edz3/gpt2-alpaca\", use_auth_token=True)" ], "metadata": { "id": "uY96uC-jVnWA", "outputId": "85d1f4f5-72ca-41e3-d035-e1da572d49ea", "colab": { "base_uri": "https://localhost:8080/", "height": 198, "referenced_widgets": [ "0e6de190c7014b5f95dbfc6194ee5738", "782dd1da460b4ed8ae7b35303abaaaae", "ef38cc109b3b45149b56465b601a7c7b", "247d0b8cff98474c9e45efed11ebbf1d", "e2be61b4c69045409535a0016b5d0ea0", "b576c06cde954700ad1e8e239f4a87d1", "51814ff454d844e9a65a94a2af30ed80", 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