{"cells":[{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":24045,"status":"ok","timestamp":1718653176908,"user":{"displayName":"CS437 TA","userId":"07185605754916893431"},"user_tz":0},"id":"N_iCvi1Sp3Zv","outputId":"4836b39c-7f31-4388-ff59-d2e6b8c9035d"},"outputs":[{"name":"stdout","output_type":"stream","text":["Mounted at /content/drive\n"]}],"source":["from google.colab import drive\n","drive.mount('/content/drive')"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Jvz4D8WUjJpA"},"outputs":[],"source":["%%capture\n","\n","# Installs Unsloth, Xformers (Flash Attention) and all other packages!\n","!pip install \"unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git\"\n","!pip install --no-deps xformers trl peft accelerate bitsandbytes\n","\n","from transformers import TextStreamer,TrainingArguments\n","from unsloth import FastLanguageModel,is_bfloat16_supported\n","from trl import SFTTrainer,SFTConfig\n","from datasets import load_dataset\n","import torch"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":300,"referenced_widgets":["9878db8196704a8b8f1b8811a91854dc","2c53d0735bfa40518a4eb7a6bea7bb7e","84134750260d4c99a7b6166cc1c79e32","1f2049bdaa0e471aa76384badba8b894","ff191223259044bdac3bd566656db250","29e2379b60804b9f8d46404bc2489c9f","1f9a42c85f6941b7b17c8064e738e78f","59059b11f5b1460d937737b5a6fbba79","88930f29c69a4e6ba53cfc8b9a744c4b","629348c9f012476e9ac2f313f06d6de4","b17bbc3602ea4e4f81a8f6ba905cb680","ce25def4185343f7a36598f9b1d49c19","28f19dddb170464791ac8ea2a1d40b83","a243c54328d2476db8cd716437b2839c","9d5a1fa0057242e89b640f82f464d324","066ee40327374152bc9c0045a79d8963","fd50c1ce3dd1429690508414b68ca698","222f42b1884f498282a908dd048019ea","eac89cbea1114c66a4de7152059bcab2","0b4dd09ca234425599bbc0e94e1d0714","0452a04d46f44abdb081e025861bd560","f4a7feb969f34e508c445ebc57b25c85","9acf2710074c4b8aadf75d189ab93059","2d10758841924ff4bd45c6cad043f386","3bbdf04de9de4042ab1bef8f4bb09cb4","c704261664eb4fcaa13ac4ccc106dabc","d5bd2ecf7e1545738b940e3637ef5091","361f0ac242c14df584dca247d3231514","4d9e0fda9f814dc188ba3d6953b20b76","679c25a17e794eed910e9e02e0ffc142","59399027e06e4da68190420305a7230c","e44b669966934211bde3cd80c6658485","7f7d96bd8f1d4accbe35141025ced300","a43d27445f0844078bffecb6a70bfe1c","dd837cebefe0402497014193b0e2ef45","11b3ee65d8b6492cb34d54d87603c291","dbc0a79d374745afaabb1af9b5b88db5","da0df9115e484835b9c4327ef3c2edcf","b001090346b641ce98692dde72ba15e0","c6835ee4eaaa489393dd53a72d2f033a","1ad53fca4c254bf5820c2088a8679024","dd917726bc2b4963bbdcd197e8d7caf0","aca3f0c456da44b9870807fe0d422bdd","f9db7a6694804cf39d2b4ff40e69be80","e1e37f22e99f40ccbbc813239bb8ce86","b10207b55f424137834d7683ac78866e","a4489b4903a847b78b189fabc379d669","31b0ecd257bc4cf39302a3558855975e","7cd070c59e8940138f7a109b209cd752","41ab65b2a4d84e51a3c51c5e672921ec","4c13c3be15764a539f244c96856fb465","8842a1acfe4c4737b1b5c57ef8a06df5","f58f81da8e6449c598646115ed67aabb","db10a3a5159b4a85812d33fca6de0ebf","225b0ee7250d444a9175a98485cc255b","836f2b72159c44ee8a3e97033f93ef32","eb5fa5f42fb64e108f06a163be7d6e0d","02df97a62a004b128a639fdd4da29819","45cb04e72d7c4557b2b81b70f102c429","973d792713064dd38003c6a48ce00c94","f9e446fd9dd54995a1ef80b195dce617","b60b6fdb04404311a9326b6769d0fe0a","df4e4df19efd4e2c845803f5ec42859c","f78ed58d49d64335bc40c795bef97e66","7236db2ca5bb4d12bc0cea3925fbf20b","5da2acb2abe04f70ad1a5f654be10a7d"]},"executionInfo":{"elapsed":24775,"status":"ok","timestamp":1718653256897,"user":{"displayName":"CS437 TA","userId":"07185605754916893431"},"user_tz":0},"id":"j7j0UmDOq0GD","outputId":"a8aaf0cb-c0bc-4fd9-8ca5-c651298c89ff"},"outputs":[{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"9878db8196704a8b8f1b8811a91854dc","version_major":2,"version_minor":0},"text/plain":["config.json: 0%| | 0.00/1.20k [00:00 0 ! Suggested 8, 16, 32, 64, 128\n"," modules_to_save=[\"lm_head\",\"embed_tokens\"],\n"," target_modules = [\"q_proj\", \"k_proj\", \"v_proj\", \"o_proj\",\n"," \"gate_proj\", \"up_proj\", \"down_proj\",\"lm_head\",\"embed_tokens\"], #\n"," lora_alpha = 16,\n"," lora_dropout = 0, # Supports any, but = 0 is optimized\n"," bias = \"none\", # Supports any, but = \"none\" is optimized\n"," # [NEW] \"unsloth\" uses 30% less VRAM, fits 2x larger batch sizes!\n"," use_gradient_checkpointing = \"unsloth\", # True or \"unsloth\" for very long context\n"," random_state = 3407,\n"," use_rslora = False, # We support rank stabilized LoRA\n"," loftq_config = None, # And LoftQ\n",")"]},{"cell_type":"markdown","metadata":{"id":"_e-6B4qGrRuz"},"source":["\n","### Data Preparation\n","We now use the [Alpaca dataset format](https://crfm.stanford.edu/2023/03/13/alpaca.html). The training data (API Call sequences can be found in training.json)\n","\n"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":81,"referenced_widgets":["7d539017449043cf8f7066e90770e7e5","399f2c9118ae47469e51490b58663e48","10871728b84b40c18db06db2a2b6f7bd","324df1e952694f668985f07d06641d42","3220ead654184109a934f2349ede93c5","f562a1fa56e2475b9e967f38b3ec0b01","288ce9fff09d4a0691067708f1e08ef4","1e62b747efe5459299cea10fd1070ace","7b01312f7a5040358230d474ad2ee834","93be168bc81040dcb34b35a2ef07b332","c7fe484f0c3a4559afcca9cee47d0129","61c4a0ac0c5445348102bae0c8f446a6","1e62b257108c4d6792ac74a5954bc5b2","5c134118dc474673b9502e8248d2f784","35796cd362c44dc29e5080cec32d799d","71da98d7ee4749978330371f36efb84b","a72db20cb6d74d6386f7376a0a7427ab","0639d0c6c2ad4158bad11b8d475e6d0b","7fd12d6e41524ab38125dd98be0de25e","6ad2dd9612f7446b9d22a8a67f2968d0","32d16670fccc4e909773b051dc2fe1b6","822ebd4604e54e1799b6b978ba1282f9"]},"executionInfo":{"elapsed":86651,"status":"ok","timestamp":1718653353977,"user":{"displayName":"CS437 TA","userId":"07185605754916893431"},"user_tz":0},"id":"v35nxK4ErPPH","outputId":"67155f7b-0f4f-4ece-a4eb-d5249db97079"},"outputs":[{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"7d539017449043cf8f7066e90770e7e5","version_major":2,"version_minor":0},"text/plain":["Generating train split: 0 examples [00:00, ? examples/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"61c4a0ac0c5445348102bae0c8f446a6","version_major":2,"version_minor":0},"text/plain":["Map: 0%| | 0/12564 [00:00\n","### Train the model\n","We are using Huggingface TRL's [`SFTTrainer`](https://huggingface.co/docs/trl/sft_trainer).\n","\n","We do 60 steps to speed things up, but you can set `num_train_epochs=1` for a full run, and turn off `max_steps=None`. We also support TRL's `DPOTrainer`!"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":215,"referenced_widgets":["76c520d200eb4f21b4e2b4cca59c2e19","a93cd9aff8ae4b0d9b64925d9820e072","70281bef78704bb3aabc49db6ba06d2d","cad9dc4eb59640f894130214dcfa6d49","66ceefc44baf4c908acf62adfcc6445c","2481a8f226214673a5e430fa74f9356a","7dab198fdd524d5894ef14800db68498","838b1c1f8f69402087bde02145a45508","5ca7ad94167c4c60bf173894535588ab","260af535e9b34a3eb95874d2a689dc89","c6a30a6355a34d5ea70f6d3605e0a727"]},"executionInfo":{"elapsed":198150,"status":"ok","timestamp":1718653552118,"user":{"displayName":"CS437 TA","userId":"07185605754916893431"},"user_tz":0},"id":"D3kp-pYjre6p","outputId":"f5141900-d858-4dd2-c112-22be971bc957"},"outputs":[{"name":"stderr","output_type":"stream","text":["/usr/local/lib/python3.10/dist-packages/trl/trainer/sft_trainer.py:269: UserWarning: You passed a `max_seq_length` argument to the SFTTrainer, the value you passed will override the one in the `SFTConfig`.\n"," warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/trl/trainer/sft_trainer.py:283: UserWarning: You passed a `dataset_num_proc` argument to the SFTTrainer, the value you passed will override the one in the `SFTConfig`.\n"," warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/trl/trainer/sft_trainer.py:307: UserWarning: You passed a `dataset_text_field` argument to the SFTTrainer, the value you passed will override the one in the `SFTConfig`.\n"," warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/multiprocess/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"76c520d200eb4f21b4e2b4cca59c2e19","version_major":2,"version_minor":0},"text/plain":["Map (num_proc=2): 0%| | 0/12564 [00:00\n"," \n"," \n"," [3023/3141 13:33:30 < 31:46, 0.06 it/s, Epoch 0.96/1]\n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," 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"],"text/plain":[""]},"metadata":{},"output_type":"display_data"}],"source":["trainer_stats = trainer.train()"]},{"cell_type":"markdown","metadata":{"id":"fzudUjyIsHGP"},"source":["\n","###Saving The Tuned Model###\n","We save the tuned model and push it to our hugging face account."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":360,"referenced_widgets":["75f79a6b43584f0f9fcac2fa6b00b2ea","08adb1c6d04e43b5b7e30babd04c8bc3","0830ee750ec4424ba889330975ee7b56","5c0290cb24a54d8d8f7c02a203152f1d","ae302aa0cb944c9cb5592d93125f41fe","744e850138d742018589d7240199a7c9","04a1ddad2f824dbc93f5892ff5b045d4","70a63d204fd3436eb7fc17ccfb6547a4","9a4f897a7bb940119e5a7e173d8bbf74","5f9778a3cf014bc8968190eb8d8669ee","f8834629931941d586e1f71c11b9447a","3fc8543688f5409dba8b098fbce2f5c2","6e65f1b5cc074941a216d9903a411a6c","110f2d4ad4764cb99f9fa661950e8578","d8e3f4047c684f70ae8b3d8bd827bd6e","01950d2e53aa44109c36c1b96b40ea13","6d039157c6294d959075a4622df29224","544d517ae11447b292d2cc5e5bff8904","927d011b8a1f4903bb603c633c9768ea","4e895885d51740a687d397563c3e6a09","fff03cdccf874928822cf8eb9566f51a","257e48407ed54480bad0386ea33e2559","d2327b09907641adb24c7a2438e39ca5","5106064467fb4e909511d3b748112594","adafc35203f04f7285c3bd33bbc863f1","2d1c220475764adb9ab0ebdb7ed22892","cb3b20510a8349baaaec6c20a983e382","495f78c02f7c4630a3a49c5b45856bd2","25bf89598e264e1ab52a7b8d0cd8b53f","29450847edfd4809acea6dab092d5ed8","cc02fd6de86d443984fc9234ec721c8b","0f0fb7c966344191a8d28b0e95283c18","9f804f587f774fd6ac8ee1f3b2c3669f","a8f112a57ae8467c8182da932ef1c07e","5591ceaea2d1459d8c9f6adf85dd4646","6bcc69e1b0c741e4aba7622d5e3cf855","70e5e147a9e24dfc9d1de5646896e6d6","eb775d7619ad4235b525dc6f435f8b3e","bd6c77daaf014ceca28cb7f2c2c102b7","64bf8009861d4be29bbee77aaefde11e","872a06dce16140ada474daff33b60b86","71a2f175b9de41339bf2078c1113d2d9","d10e3464e69a46a983b4c7388f914839","edd1449818294a8e8f0caee0b55993d6","b5425610578a4f9fac96446c7039b842","8d576589aeb547de95d52e22e5969846","35e1fa4bccb5413381503d380cb3cf63","f7cf055c6fe440738105f317b0d5e6b8","15a5d03201064d63bb7f5d2b5bacfdeb","08c93634745d43bea600f95adaa20690","3dac70e115ce45f58391197a58004879","1527de75f30548f79206c9b853403710","500c44f15aed4c39b39d3003d7d09e10","f71265898fd246919d28abba7b35ed0e","04cb8c864bbe4a288f173b9344fe17f7"]},"executionInfo":{"elapsed":324737,"status":"ok","timestamp":1718704615465,"user":{"displayName":"CS437 TA","userId":"07185605754916893431"},"user_tz":0},"id":"LoQXFoOysTug","outputId":"e0eb67e1-a8f5-4dfc-9c2c-1b00a7a049da"},"outputs":[{"name":"stdout","output_type":"stream","text":["Unsloth: Merging 4bit and LoRA weights to 16bit...\n","Unsloth: Will use up to 43.68 out of 83.48 RAM for saving.\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 32/32 [00:00<00:00, 46.97it/s]\n"]},{"name":"stdout","output_type":"stream","text":["Unsloth: Saving to organization with address EkmekE/LlamaCalls-16k\n","Unsloth: Saving tokenizer... Done.\n","Unsloth: Saving model... This might take 5 minutes for Llama-7b...\n","Unsloth: Saving to organization with address EkmekE/LlamaCalls-16k\n","Unsloth: Uploading all files... Please wait...\n"]},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"75f79a6b43584f0f9fcac2fa6b00b2ea","version_major":2,"version_minor":0},"text/plain":["model-00001-of-00004.safetensors: 0%| | 0.00/4.98G [00:00\"\n","model.push_to_hub_merged(\"LlamaCalls-16k\", tokenizer, save_method = \"merged_16bit\", token = token)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"R09-lG22vgp_"},"outputs":[],"source":["alpaca_prompt = \"\"\"You are a malware classifier. You get API Call sequences as input and give label as output. Your output should be one of the following: adware, crypto_miner, downloader, dropper, file_infector, flooder, installer, ransomware, spyware.\n","\n","### Instruction:\n","{}\n","\n","### Input:\n","{}\n","\n","### Response:\n","{}\"\"\"\n","EOS_TOKEN = tokenizer.eos_token # Must add EOS_TOKEN\n","def formatting_prompts_func(examples):\n"," instructions = examples[\"instruction\"]\n"," inputs = examples[\"input\"]\n"," outputs = examples[\"output\"]\n"," texts = []\n"," for instruction, input, output in zip(instructions, inputs, outputs):\n"," # Must add EOS_TOKEN, otherwise your generation will go on forever!\n"," text = alpaca_prompt.format(instruction, input, output) + EOS_TOKEN\n"," texts.append(text)\n"," return { \"text\" : texts, }\n","pass"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":81,"referenced_widgets":["b6658c0660614cf9aa02c21726788214","0d34644024684739846934126704f406","4fd8e42c9cc543d4b3ea2199c62a6007","435a4ce72c5b441c85f5f50423348b9e","e8f9aabf934d496f955a7fca628c210b","5509d13b52fb4affb8a07977acec39c0","73b25a1d4d6347829595a8daa41bc658","bf70935595f04d4db57545b62cb160e9","aacd09087b424d29832b15b2cd113146","637517aebfa44699b69d80e27c7cd850","ccf290df7b6a43298a0046958ff67428","ca4eae9b134e42f8a1c39ce1eb1ac363","a98d98be80d54bc8b9fc582c6eaf4461","abbb7c62648846f99d0e82771907d0e6","bc407ca40ce441daa1012c9729e484a5","01253c4fca584a08bc74e878a935cdbf","dfbde6e5a6984f7295adfb6941bea79d","d6747d3bc2aa421c88b9f10aeef49a76","b7bc2af658354c39b481c91d2e8c13ff","562c5028f9bf4230a529dd005bafcad7","b93aed341fc048e7bb55211ec417630a","84ce82fe46ca4601964ddf96d9b757d6"]},"executionInfo":{"elapsed":8407,"status":"ok","timestamp":1718704639902,"user":{"displayName":"CS437 TA","userId":"07185605754916893431"},"user_tz":0},"id":"0KvDAUUAvjqo","outputId":"38515aa0-c118-42f6-c3ac-8dd2167df498"},"outputs":[{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"b6658c0660614cf9aa02c21726788214","version_major":2,"version_minor":0},"text/plain":["Generating train split: 0 examples [00:00, ? examples/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"ca4eae9b134e42f8a1c39ce1eb1ac363","version_major":2,"version_minor":0},"text/plain":["Map: 0%| | 0/693 [00:00