{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "IqM-T1RTzY6C" }, "source": [ "To run this, press \"*Runtime*\" and press \"*Run all*\" on a **free** Tesla T4 Google Colab instance!\n", "
\n", " \n", " \n", " Join Discord if you need help + support us if you can!\n", "
\n", "\n", "To install Unsloth on your own computer, follow the installation instructions on our Github page [here](https://github.com/unslothai/unsloth#installation-instructions---conda).\n", "\n", "You will learn how to do [data prep](#Data), how to [train](#Train), how to [run the model](#Inference), & [how to save it](#Save) (eg for Llama.cpp).\n", "\n", "**[NEW] Llama-3 8b is trained on a crazy 15 trillion tokens! Llama-2 was 2 trillion.**" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "id": "2eSvM9zX_2d3" }, "outputs": [], "source": [ "%%capture\n", "import torch\n", "major_version, minor_version = torch.cuda.get_device_capability()\n", "# Must install separately since Colab has torch 2.2.1, which breaks packages\n", "!pip install \"unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git\"\n", "if major_version >= 8:\n", " # Use this for new GPUs like Ampere, Hopper GPUs (RTX 30xx, RTX 40xx, A100, H100, L40)\n", " !pip install --no-deps packaging ninja einops flash-attn xformers trl peft accelerate bitsandbytes\n", "else:\n", " # Use this for older GPUs (V100, Tesla T4, RTX 20xx)\n", " !pip install --no-deps xformers trl peft accelerate bitsandbytes\n", "pass\n", "# Llama 3 Video Tutorial https://www.youtube.com/watch?v=aQmoog_s8HE" ] }, { "cell_type": "markdown", "metadata": { "id": "r2v_X2fA0Df5" }, "source": [ "* We support Llama, Mistral, CodeLlama, TinyLlama, Vicuna, Open Hermes etc\n", "* And Yi, Qwen ([llamafied](https://huggingface.co/models?sort=trending&search=qwen+llama)), Deepseek, all Llama, Mistral derived archs.\n", "* We support 16bit LoRA or 4bit QLoRA. Both 2x faster.\n", "* `max_seq_length` can be set to anything, since we do automatic RoPE Scaling via [kaiokendev's](https://kaiokendev.github.io/til) method.\n", "* [**NEW**] With [PR 26037](https://github.com/huggingface/transformers/pull/26037), we support downloading 4bit models **4x faster**! [Our repo](https://huggingface.co/unsloth) has Llama, Mistral 4bit models." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 393, "referenced_widgets": [ "6cbe43cecb164685a0ed856b6a8fbe4f", "b4931b20365041dfbdc8dda72c90f009", "c3de3c18251e4bafbebc5d7df3bd5470", "8e12cd2b2bf946a486283cd9cfdcbbaf", "ced2d2c2a69e482f9bb26dab7a2c6a8d", "898dc083d84048db9077572c14948d38", "9eea6540d80b4549a271649ee80a234d", "ccd6777b2d9047a09da82a1e79f1d180", "a8a68ec67cfb4ec98da9af09bf44f413", "5c98d9c881314e409c22ccf792bd1cec", "458032fc6a8940b7811b1d30a849b072", "df2602d780da4b5fbddd20e2ab1a2a1d", "6fba031afa3249649d41e9144a72ed06", "f4fa473826574bc8b9640b142290d0b0", "f761835b538e4dcba077b1bdbe49a4ef", "a3ce0fcc31074f248e4de8e9be607c59", "a7019cb2153c4d1880c147f5753fb2bc", "8631c12d43a142f2acc35fa947379d1c", "74f5cdbcd52a435da4ab5cbf91320dda", "5afa2cd95f7a4a4ea0e3f21355cc6e53", "e74eea87bfe8457682d10e97ae1e5b79", "0695c60ea79d4cf58cf954f54d7d5966", "7ef329225c99438ca012ee2d3a2e1a60", "717af3ac31fc4026b8eadd69ad5982f5", "d90260e8ed4a42b8aa847c836197e190", "30818f7b13a04b7b9b844519b3b1c77e", "89cc69e908e64c4088ac289c0561a8ec", "e62cebaea960494e89cc49b61a04918c", "ece06699765f4467b2ab88fbc80bdb36", "5e1afc00d22e498c820f248b04b50f94", "545011174bcf4581a4c70e0788ac1586", "1947451d96bd4a2880b6e95cfb583731", "456d62ce69964b088812df46bad5b135", "3e49d1129328456f9f9d41d65bbef69b", "49dce0f561dc47d2b99bf1708ec916e7", "1075e013b2bf41cc91fc9aebb705413f", "448c906245304072bc5a6afd76a05e80", "48a0254bec2d4b08b36ca3f90c999df7", "846c5ada48834839b7352f745a0c4ec4", "31a606d0661140e1a6e11028f28c768c", "4fc908e3887146d3bd134a709b53dee8", "0b1bd83044f748b4af0602a869770073", "1146d7dd8e184b09b11479448d336e7b", "30c5556abec34ca3b4ea5e9d3bb66b5b", "b5a61139ae1642298ece35a2c5a427c3", "30c9c878e1ad49b890b85cbb6d6d5fd1", "1cd6e0ff28f04fbe97024d792f083746", "411d0368da4943fc95fdf7f83e91f27b", "e8bb83f34d494c7a8c11edc29aaad79d", "d209ad757a684fb09dbbe8df3e234378", "7b32498aec244ebeb6078ef9a4c9e34e", "4b8224baf073496084f09c13d1619586", "0d09d00b672944ad996cc4fc5f6ed68f", "e116d96636cd4e998505db3bee83c4b4", "b577ee5a66e74e2f9981ad805a5d427d", "7717c837ff184f84a25fdec8533e015d", "060579ccfb804abe8de1aa7d18882c05", "d8bf190189014b9f874c93709fe8fb8a", "96d0c970809b43f8bf3262eb27f7f6dc", "9a9bd3f3c8ba474d8b0dd312e41a618b", "6e3ba9b222d74519af144c632b3c697e", "f1e0b81605d4498b98b7b6eaab76c3e1", "704d920f02e542d781c331b3a74bd39d", "7b8a16e3b0024aca945092aa03beac36", "4a75e8a4a6a344b2bea080091d3c964f", "e3233b6189804badaf93441835141f9f" ] }, "id": "QmUBVEnvCDJv", "outputId": "88f00356-b425-40f5-dbab-da7f8a53953d" }, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "Unsloth: You passed in `meta-llama/Meta-Llama-3-8B-Instruct` and `load_in_4bit = True`.\n", "We shall load `unsloth/llama-3-8b-Instruct-bnb-4bit` for 4x faster loading.\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "config.json: 0%| | 0.00/1.15k [00:00.\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "model.safetensors: 0%| | 0.00/5.70G [00:00 0 ! Suggested 8, 16, 32, 64, 128\n", " target_modules = [\"q_proj\", \"k_proj\", \"v_proj\", \"o_proj\",\n", " \"gate_proj\", \"up_proj\", \"down_proj\",],\n", " lora_alpha = 5,\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": "vITh0KVJ10qX" }, "source": [ "\n", "### Data Prep\n", "We now use the Alpaca dataset from [yahma](https://huggingface.co/datasets/yahma/alpaca-cleaned), which is a filtered version of 52K of the original [Alpaca dataset](https://crfm.stanford.edu/2023/03/13/alpaca.html). You can replace this code section with your own data prep.\n", "\n", "**[NOTE]** To train only on completions (ignoring the user's input) read TRL's docs [here](https://huggingface.co/docs/trl/sft_trainer#train-on-completions-only).\n", "\n", "**[NOTE]** Remember to add the **EOS_TOKEN** to the tokenized output!! Otherwise you'll get infinite generations!\n", "\n", "If you want to use the `ChatML` template for ShareGPT datasets, try our conversational [notebook](https://colab.research.google.com/drive/1Aau3lgPzeZKQ-98h69CCu1UJcvIBLmy2?usp=sharing).\n", "\n", "For text completions like novel writing, try this [notebook](https://colab.research.google.com/drive/1ef-tab5bhkvWmBOObepl1WgJvfvSzn5Q?usp=sharing)." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 430, "referenced_widgets": [ "e98d116cb3444f569c238343d1e92940", "1c5625afb7c24822bff01f19f085f1ba", "7a13cff838704c48bf6241b902f6ea87", "2ac0122274ee48e9979bdde6125ccfc5", "e1646dccd863400f912d5178bd395dc6", "d119bb90875548ceb52c4d6a044b3696", "be7b18fc1dc0473b9b1e89f69aff504b", "5b7ae0b2436741879087f91ab796ac35", "e413810c19fa49d19e30f98aaa597b60", "b3e57d9dcbef4d4a954914931182c70a", "216c5b7aaaa84118b5e33a0261faf292", "447273c8ed9c41a69f08764591da9f09", "6ed4188f8b6d45ef808319fac0e74f80", "dfe233817df143018bfb2a783aa949f9", "7684a14106bb41ecb5c038e884ac4b86", "5cea014c20744936b72ad185176f211b", "5fa36d2171e94827a84d92b3dfc2f5e5", "309c0121a4034cfeac47cddf1e5c613e", "f1b2aa7909c94114abc5082a82e57a8e", "0a2b0d3111eb478f845390787f98239f", "a131ced9e5254d5793877373b47dfc34", "82319263c46749de9f2264961d7639f4", "54cdb7b774e244dd9705361bdf9f7ca8", "f3d23075bbfd4828960adc0d9f812c03", "37b90a637de64a54a36f8b10a6f8ef42", "8df66edd2f6a4179ac28e4a2ff267a13", "b449e7510b2147448e0ab55a69333823", "af4e158145c0448186e28d5aecd6dc5f", "a4739d889b504ebe8f40997efe4c1d92", "18947ce2d2b6455cbca30e247b5265b6", "482b2cc8a7ab4f8cbd3c882a3ef76729", "4fed868a96e2467aac49d40ba7694ffb", "2901be2c33e44adb94aefafd34791665", "3ca30ad8d3ef4ef9970ebfb3b44b09bc", "6171c07978fd41ff81f87fbbf6d9b458", "26fb51fd90a640d489640d88d243fb2a", "c157e33d178c46e0ba8038290cf1c8eb", "d68532bcd8ba42efadf9dbc870daca60", "b449176bedde4125a2d4ff9cd4c719c8", "f8055b6a9eb24a76ad232ec113c6dc07", "922b3d90079744d2818975755b4e65e7", "b2f382e302e943e7bd291217f8374c52", "819f77c294dc4fe9bfa67296b5c32504", "426528a186054b0d9599ba7b77c66e33", "7c3c5e5608574c52a3a35b5d8f7bc146", "1a853588c1e1454192a14fb5a0d4b9bb", "94ea893d417a470698cd0e811fa238a9", "b5f5541dcd1a4c8589753eddd38aa501", "a85cf3c48cf34f23ab4c109f345a9f17", "046c6f93a5554defa607fcf553cc3074", "f3718d7bef424ec99bbfafa0b8d696ff", "1617915dc24343d693c8f058dd3a5484", "8f65867834a647ffbe9c10048f18ef80", "cbb0e2105ff64d2d8c8bdf00331bb40d", "961bb7f356cf4c729bfe726aabfbf4aa", "1e5f773bdab84ade9d36236d22db23ca", "556cbe868bca402c8595b2ffadf9b077", "fb9ea04b340c4003b56d75c5caa0e595", "225e22a4f2cd43cba37e138f8ba8066e", "2066b5b667d7448cb77aabd08bc9b4b5", "09776162afe0467484e6d3c17cc86333", "4ef17590cc5f42f5ba560adabdbaa96a", "8d910e8d447e435aba63cbcb21425241", "728a07bf822b4647b8db3ee72eb500c8", "6d8b17a21f7b48649b0b4cbe11edebc8", "75e27120c2cd4aaa89b8295fda6bf3b5", "2d433cb87d1d460fb31384078df0f6d4", "9a4ba1d9db124bee90a9457b8f418602", "c734cbc3e4864d7fb82e53e215df7359", "840f1e5d099a46ebb21037df7874aa49", "5996441add1148c7b7972096e7addc42", "6bfbefbf97d94382b1c721f22cdf2784", "288c130754794bc793db4c03740c202f", "5e98fa1996a044bca05a10ca9b8f12d4", "17324eb4a1314dc88611f2a49abc7352", "ffd15e308b714f728a8a741e2ea48455", "249c639392374376a1fefc8743274721", "5d258a46e2554272a39ecc0bd2b4e4b2", "854a32b3318b4082a8a73c53d8157a90", "7946622c35e046ccaa39629ed5cb40ea", "1b3b06e73002453cb9a22edaaac13b9e", "afce33831d084edea8ab7531b2bc55f9", "eecf95ad35764153b0a6e4b42c30a3c4", "097269fcb3bb4648a15b2c508412a287", "addc3795692643618dc3c71cfe81b703", "37e2ce5b6aad4eb4bac359df7cd3fb95", "99cabe5edea140d484a5673ccb0056fe", "4efe21830c35434a96a2fac98019f1b4", "1b4ecc8a9eae42d3ac5363a11c42189f", "59002a9e117640b481de1af80401f2eb", "e3e187d12f9b47beb736c2342559d439", "fd721c60e69343558c0831c4e7aa1b38", "16a716b2406c415fbf107891033807b9", "7cf897b83b4a461a963dec7e47dfaa9b", "c8e2f4962c4e4b6b9134c52856c79b57", "ab8d065a297946d4901bcc546c82bc34", "38e0c29334744251a208fa8e5c463b7f", "3b50ab19386c43f9a1a2a1db4c0ac91b", "c840673e8c6847c396d409de513716d8", "842a1d1d044c405cb7510f092bcc8363", "d021476594ac40d5a5ef6bd080343027", "4d2b4cb8ccb14b42bb0e986e4b3a61b3", "0d8593ccf36b41b093d9b2a7b3993dbf", "373ad2b6f39c4e83a2ee9e4fc51f868d", "fe9aa7c72edd4998b4558374550792f7", "8c54ed45bf034a5091d28d5f1812e291", "93a34deba3614c959661ca29ad19005b", "68523f3b7ee04a80a90651849b781ec5", "7ac490e0b6614a92818f7e118540023b", "90bdf9297ac94697a0eba15a125022c4" ] }, "id": "LjY75GoYUCB8", "outputId": "a3651ced-3b47-4f45-a767-8a146d5f204e" }, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.10/dist-packages/datasets/load.py:1486: FutureWarning: The repository for zjunlp/Mol-Instructions contains custom code which must be executed to correctly load the dataset. You can inspect the repository content at https://hf.co/datasets/zjunlp/Mol-Instructions\n", "You can avoid this message in future by passing the argument `trust_remote_code=True`.\n", "Passing `trust_remote_code=True` will be mandatory to load this dataset from the next major release of `datasets`.\n", " warnings.warn(\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "Downloading builder script: 0%| | 0.00/7.34k [00:00\n", "### Train the model\n", "Now let's use Huggingface TRL's `SFTTrainer`! More docs here: [TRL SFT docs](https://huggingface.co/docs/trl/sft_trainer). 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": 5, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 124, "referenced_widgets": [ "6334bb08605c4e5384ce42418b81dd62", "e9aef030346140e689a71af45ca29e0d", "a65307a2e7604ec58ebba832f5fc433b", "0b86c760e0dd4e29acf60dae7af71fcb", "ca65331d5b26471f8f717500e2ce2c02", "4f45eed47bfa484e928160327ac88a01", "9bfe52e790954329a4fd49710906e832", "f9aee8e971594078bef4f2e05c2e7d5e", "89555eab6a224e548a21ec71141c661b", "be3fcacec7dd43a09572770eb2a02e93", "2ea846eea90f46f381d60326070b58fd" ] }, "id": "95_Nn-89DhsL", "outputId": "44c60daa-6916-4e1e-d1ce-914d0ad85a33" }, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "/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" ] }, { "output_type": "display_data", "data": { "text/plain": [ "Map (num_proc=2): 0%| | 0/298319 [00:00" ], "text/html": [ "\n", "
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StepTraining Loss
11.968600
22.185600
32.230400
41.842200
51.912200
62.123900
71.675900
81.511800
91.492900
101.226400
111.185600
121.319300
131.079800
141.062400
150.938500
160.876700
170.963200
180.846000
190.896200
200.874700
210.818700
220.826700
230.824500
240.843200
250.664400
260.837700
270.669300
280.716500
290.843400
300.706800
310.700300
320.730400
330.718100
340.698700
350.645700
360.706900
370.640600
380.587100
390.737500
400.599000
410.572600
420.648200
430.555400
440.663600
450.746300
460.609500
470.592900
480.655800
490.585200
500.604800
510.590100
520.564600
530.622700
540.589200
550.562700
560.583600
570.494400
580.510000
590.582800
600.710000
610.578300
620.622300
630.566800
640.600500
650.527600
660.518200
670.749600
680.552700
690.531300
700.523900
710.673200
720.763300
730.484900
740.508300
750.462800
760.650700
770.520600
780.542200
790.481000
800.460600
810.506100
820.532600
830.493200
840.511200
850.539200
860.532600
870.537600
880.469000
890.553700
900.486300
910.451300
920.467900
930.505500
940.602200
950.529700
960.504000
970.445800
980.507200
990.505500
1000.521300
1010.570400
1020.523900
1030.479500
1040.532500
1050.607400
1060.460900
1070.538400
1080.463400
1090.431400
1100.532500
1110.619000
1120.442200
1130.626200
1140.506700
1150.495600
1160.509600
1170.488200
1180.485500
1190.463000
1200.525300
1210.593600
1220.468400
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1240.451400
1250.593200
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1280.475300
1290.459400
1300.492400
1310.558800
1320.564000
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1350.454500
1360.524000
1370.515500
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1390.516500
1400.440200
1410.415000
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1440.448100
1450.563400
1460.407900
1470.451900
1480.460500
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1500.461100
1510.454800
1520.457000
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1600.506100
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1710.490700
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1750.573400
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1780.510000
1790.474100
1800.445900
1810.542800
1820.434800
1830.465800
1840.450000
1850.425200
1860.387100
1870.590400
1880.487600
1890.444500
1900.404000
1910.491900
1920.490600
1930.550200
1940.444000
1950.637300
1960.672200
1970.495500
1980.730700
1990.491500
2000.439800
2010.501800
2020.515000
2030.532200
2040.516600
2050.403500
2060.478000
2070.435000
2080.466300
2090.533600
2100.487100
2110.503600
2120.394500
2130.552900
2140.463500
2150.459000
2160.575100
2170.386300
2180.585200
2190.423800
2200.503100
2210.480000
2220.476000
2230.446500
2240.477000
2250.491800
2260.458400
2270.684100
2280.504800
2290.388200
2300.387600
2310.652200
2320.372800
2330.391700
2340.372100
2350.495400
2360.463100
2370.382900
2380.402600
2390.370500
2400.455200
2410.589800
2420.459600
2430.370900
2440.422000
2450.490200
2460.501900
2470.474500
2480.464900
2490.463100
2500.422600
2510.358600
2520.398100
2530.455000
2540.440400
2550.406500
2560.416900
2570.410600
2580.528900
2590.463200
2600.465700
2610.506200
2620.395000
2630.584700
2640.471600
2650.488600
2660.460000
2670.378700
2680.561300
2690.449400
2700.475200
2710.504100
2720.370200
2730.389200
2740.417500
2750.369600
2760.352100
2770.414400
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2790.523200
2800.513200
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2830.455300
2840.436700
2850.521000
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2870.688200
2880.491800
2890.517700
2900.447800
2910.493800
2920.416500
2930.471900
2940.456700
2950.510000
2960.362200
2970.402800
2980.424600
2990.361800
3000.525800
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" ] }, "metadata": {} } ], "source": [ "trainer_stats = trainer.train()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 0 }, "id": "pCqnaKmlO1U9", "outputId": "609dc165-c33f-48ea-ba27-93a2c2a309c8" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "712.032 seconds used for training.\n", "11.87 minutes used for training.\n", "Peak reserved memory = 10.775 GB.\n", "Peak reserved memory for training = 5.316 GB.\n", "Peak reserved memory % of max memory = 27.234 %.\n", "Peak reserved memory for training % of max memory = 13.436 %.\n" ] } ], "source": [ "#@title Show final memory and time stats\n", "used_memory = round(torch.cuda.max_memory_reserved() / 1024 / 1024 / 1024, 3)\n", "used_memory_for_lora = round(used_memory - start_gpu_memory, 3)\n", "used_percentage = round(used_memory /max_memory*100, 3)\n", "lora_percentage = round(used_memory_for_lora/max_memory*100, 3)\n", "print(f\"{trainer_stats.metrics['train_runtime']} seconds used for training.\")\n", "print(f\"{round(trainer_stats.metrics['train_runtime']/60, 2)} minutes used for training.\")\n", "print(f\"Peak reserved memory = {used_memory} GB.\")\n", "print(f\"Peak reserved memory for training = {used_memory_for_lora} GB.\")\n", "print(f\"Peak reserved memory % of max memory = {used_percentage} %.\")\n", "print(f\"Peak reserved memory for training % of max memory = {lora_percentage} %.\")" ] }, { "cell_type": "markdown", "metadata": { "id": "ekOmTR1hSNcr" }, "source": [ "\n", "### Inference\n", "Let's run the model! You can change the instruction and input - leave the output blank!" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 0 }, "id": "kR3gIAX-SM2q", "outputId": "3ea21ee3-9230-43f1-bdd2-03e7403db886" }, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n" ] }, { "output_type": "execute_result", "data": { "text/plain": [ "['<|begin_of_text|>Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\\n\\n### Instruction:\\nContinue the fibonnaci sequence.\\n\\n### Input:\\n1, 1, 2, 3, 5, 8\\n\\n### Response:\\n13\\n\\n### Reason:\\nThe sequence is a Fibonacci sequence, where each number is the sum of the two preceding ones, usually starting with 0 and 1. The sequence is: 0, 1, 1, 2, 3, 5, 8, 13, 21,']" ] }, "metadata": {}, "execution_count": 9 } ], "source": [ "# alpaca_prompt = Copied from above\n", "FastLanguageModel.for_inference(model) # Enable native 2x faster inference\n", "inputs = tokenizer(\n", "[\n", " alpaca_prompt.format(\n", " \"Continue the fibonnaci sequence.\", # instruction\n", " \"1, 1, 2, 3, 5, 8\", # input\n", " \"\", # output - leave this blank for generation!\n", " )\n", "], return_tensors = \"pt\").to(\"cuda\")\n", "\n", "outputs = model.generate(**inputs, max_new_tokens = 64, use_cache = True)\n", "tokenizer.batch_decode(outputs)" ] }, { "cell_type": "markdown", "metadata": { "id": "CrSvZObor0lY" }, "source": [ " You can also use a `TextStreamer` for continuous inference - so you can see the generation token by token, instead of waiting the whole time!" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 0 }, "id": "e2pEuRb1r2Vg", "outputId": "b27fca9b-b617-4643-8dec-fe8a4128778c" }, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "<|begin_of_text|>Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n", "\n", "### Instruction:\n", "Continue the fibonnaci sequence.\n", "\n", "### Input:\n", "1, 1, 2, 3, 5, 8\n", "\n", "### Response:\n", "13\n", "\n", "### Reason:\n", "The sequence is a Fibonacci sequence, where each number is the sum of the two preceding ones, usually starting with 0 and 1. The sequence is: 0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89, 144,... The sequence is named after the Italian mathematician Leonardo Fibonacci, who introduced it in the 13th century as a solution to a problem involving the growth of a population of rabbits. The sequence is often used to describe the growth of populations, the branching of\n" ] } ], "source": [ "# alpaca_prompt = Copied from above\n", "FastLanguageModel.for_inference(model) # Enable native 2x faster inference\n", "inputs = tokenizer(\n", "[\n", " alpaca_prompt.format(\n", " \"Continue the fibonnaci sequence.\", # instruction\n", " \"1, 1, 2, 3, 5, 8\", # input\n", " \"\", # output - leave this blank for generation!\n", " )\n", "], return_tensors = \"pt\").to(\"cuda\")\n", "\n", "from transformers import TextStreamer\n", "text_streamer = TextStreamer(tokenizer)\n", "_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 128)" ] }, { "cell_type": "code", "source": [ "# alpaca_prompt = Copied from above\n", "FastLanguageModel.for_inference(model) # Enable native 2x faster inference\n", "inputs = tokenizer(\n", "[\n", " alpaca_prompt.format(\n", " \"What is a famous bridge in San Francisco bay area?\", # instruction\n", " \"\", # input\n", " \"\", # output - leave this blank for generation!\n", " )\n", "], return_tensors = \"pt\").to(\"cuda\")\n", "\n", "from transformers import TextStreamer\n", "text_streamer = TextStreamer(tokenizer)\n", "_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 128)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 0 }, "id": "Z5tWI7ivVjMg", "outputId": "352a6415-c44f-4a7e-d885-558136283641" }, "execution_count": 11, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "<|begin_of_text|>Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n", "\n", "### Instruction:\n", "What is a famous bridge in San Francisco bay area?\n", "\n", "### Input:\n", "\n", "\n", "### Response:\n", "The Golden Gate Bridge is a suspension bridge that spans the Golden Gate strait, which is the opening of San Francisco Bay into the Pacific Ocean. It connects the city of San Francisco, California, to Marin County, California. The bridge is painted a distinctive shade of orange color, known as \"International Orange,\" which was chosen to make the bridge more visible in the foggy conditions that are common in the area. The Golden Gate Bridge is one of the most iconic and photographed bridges in the world, and it is considered an engineering marvel. It was completed in 1937 and has been declared one of the Wonders of the Modern World\n" ] } ] }, { "cell_type": "code", "source": [ "# alpaca_prompt = Copied from above\n", "FastLanguageModel.for_inference(model) # Enable native 2x faster inference\n", "inputs = tokenizer(\n", "[\n", " alpaca_prompt.format(\n", " \"What is a famous university in San Francisco bay area?\", # instruction\n", " \"\", # input\n", " \"\", # output - leave this blank for generation!\n", " )\n", "], return_tensors = \"pt\").to(\"cuda\")\n", "\n", "from transformers import TextStreamer\n", "text_streamer = TextStreamer(tokenizer)\n", "_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 128)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 0 }, "id": "9fJZQcwlVlBq", "outputId": "5b136e7c-ab0e-424e-f6f4-a5786b1535de" }, "execution_count": 12, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "<|begin_of_text|>Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n", "\n", "### Instruction:\n", "What is a famous university in San Francisco bay area?\n", "\n", "### Input:\n", "\n", "\n", "### Response:\n", "The University of California, Berkeley is a public research university located in Berkeley, California. It is the oldest and one of the most prestigious institutions in the University of California system. The university is known for its academic excellence, research opportunities, and strong programs in a wide range of fields. It is also known for its social and political activism, and has been a hub for the counterculture and social movements of the 1960s and 1970s. The university is located in the San Francisco Bay Area, and is easily accessible by public transportation. It is a popular choice for students from all over the world, and is known\n" ] } ] }, { "cell_type": "code", "source": [ "# alpaca_prompt = Copied from above\n", "FastLanguageModel.for_inference(model) # Enable native 2x faster inference\n", "inputs = tokenizer(\n", "[\n", " alpaca_prompt.format(\n", " \"What are the DNA bases?\", # instruction\n", " \"\", # input\n", " \"\", # output - leave this blank for generation!\n", " )\n", "], return_tensors = \"pt\").to(\"cuda\")\n", "\n", "from transformers import TextStreamer\n", "text_streamer = TextStreamer(tokenizer)\n", "_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 128)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 0 }, "id": "1TPeJxviVls8", "outputId": "8e6bb881-95ce-45d2-ba4f-0b9197673dff" }, "execution_count": 13, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "<|begin_of_text|>Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n", "\n", "### Instruction:\n", "What are the DNA bases?\n", "\n", "### Input:\n", "\n", "\n", "### Response:\n", "The DNA bases are adenine (A), guanine (G), cytosine (C), and thymine (T). The DNA bases are the building blocks of DNA, and they are arranged in a specific sequence to form the genetic code. The DNA bases are paired in a complementary manner to form the double helix structure of DNA. The pairing of the DNA bases is as follows: adenine (A) pairs with thymine (T), and guanine (G) pairs with cytosine (C). This pairing is crucial for the stability and function of DNA. The DNA bases are also involved in the transmission\n" ] } ] }, { "cell_type": "code", "source": [ "# alpaca_prompt = Copied from above\n", "FastLanguageModel.for_inference(model) # Enable native 2x faster inference\n", "inputs = tokenizer(\n", "[\n", " alpaca_prompt.format(\n", " \"What are all of the types of bonds found in DNA?\", # instruction\n", " \"\", # input\n", " \"\", # output - leave this blank for generation!\n", " )\n", "], return_tensors = \"pt\").to(\"cuda\")\n", "\n", "from transformers import TextStreamer\n", "text_streamer = TextStreamer(tokenizer)\n", "_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 128)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 0 }, "id": "GlPUCSprVnRK", "outputId": "90dc22cd-9f53-40d1-8ab8-7c5a72aea36b" }, "execution_count": 14, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "<|begin_of_text|>Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n", "\n", "### Instruction:\n", "What are all of the types of bonds found in DNA?\n", "\n", "### Input:\n", "\n", "\n", "### Response:\n", "The DNA molecule is composed of two strands of nucleotides that are covalently bonded together. The nucleotides are arranged in a specific sequence that contains the genetic information necessary for the development and function of an organism. The nucleotides are composed of a sugar molecule called deoxyribose, a phosphate group, and a nitrogenous base. The nitrogenous bases are adenine (A), guanine (G), cytosine (C), and thymine (T). The sugar and phosphate molecules are linked together by phosphodiester bonds, which form the backbone of the DNA molecule. The nitrogenous bases project inward\n" ] } ] }, { "cell_type": "code", "source": [ "# alpaca_prompt = Copied from above\n", "FastLanguageModel.for_inference(model) # Enable native 2x faster inference\n", "inputs = tokenizer(\n", "[\n", " alpaca_prompt.format(\n", " \"What are the reaction names to create DNA?\", # instruction\n", " \"\", # input\n", " \"\", # output - leave this blank for generation!\n", " )\n", "], return_tensors = \"pt\").to(\"cuda\")\n", "\n", "from transformers import TextStreamer\n", "text_streamer = TextStreamer(tokenizer)\n", "_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 128)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 0 }, "id": "NelWwdJjVpqR", "outputId": "ca86e05b-16e4-4651-d7b1-5f4bcf7c3640" }, "execution_count": 15, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "<|begin_of_text|>Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n", "\n", "### Instruction:\n", "What are the reaction names to create DNA?\n", "\n", "### Input:\n", "\n", "\n", "### Response:\n", "The molecule is a DNA.\n", "\n", "### Description:\n", "The molecule is a DNA. It is a DNA. It is a DNA. It is a DNA. It is a DNA. It is a DNA. It is a DNA. It is a DNA. It is a DNA. It is a DNA. It is a DNA. It is a DNA. It is a DNA. It is a DNA. It is a DNA. It is a DNA. It is a DNA. It is a DNA. It is a DNA. It is a DNA. It is a DNA. It is a DNA. It is a DNA. It is a\n" ] } ] }, { "cell_type": "code", "source": [ "# alpaca_prompt = Copied from above\n", "FastLanguageModel.for_inference(model) # Enable native 2x faster inference\n", "inputs = tokenizer(\n", "[\n", " alpaca_prompt.format(\n", " \"What is the structure for adenine?\", # instruction\n", " \"\", # input\n", " \"\", # output - leave this blank for generation!\n", " )\n", "], return_tensors = \"pt\").to(\"cuda\")\n", "\n", "from transformers import TextStreamer\n", "text_streamer = TextStreamer(tokenizer)\n", "_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 128)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 0 }, "id": "FQw11BVmrV-f", "outputId": "a8126950-a136-493a-f75f-bbdd10c8b5a7" }, "execution_count": 16, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "<|begin_of_text|>Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n", "\n", "### Instruction:\n", "What is the structure for adenine?\n", "\n", "### Input:\n", "\n", "\n", "### Response:\n", "[C][C][N][C][=C][C][=C][Ring1][=Branch1]C\n", "\n", "### Description:\n", "Adenine is a purine base that is the parent compound of the purine nucleosides and nucleotides. It is a member of purines and a member of pyrimidines. It is a conjugate base of a adeninium. It is a conjugate acid of an adenine(1-).\n", "\n", "### References:\n", "1. PubChem. 2. ChemIDplus. \n", "### Saving, loading finetuned models\n", "To save the final model as LoRA adapters, either use Huggingface's `push_to_hub` for an online save or `save_pretrained` for a local save.\n", "\n", "**[NOTE]** This ONLY saves the LoRA adapters, and not the full model. To save to 16bit or GGUF, scroll down!" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "id": "upcOlWe7A1vc" }, "outputs": [], "source": [ "model.save_pretrained(\"lora_model\") # Local saving\n", "# model.push_to_hub(\"Your-Model-Name\", organization=\"kevinkawchak\", token = \"Your HF writable token\", private=True) # ONLY saves the LoRA adapters" ] }, { "cell_type": "code", "source": [ "if False:\n", " from unsloth import FastLanguageModel\n", " model, tokenizer = FastLanguageModel.from_pretrained(\n", " model_name = \"lora_model\", # YOUR MODEL YOU USED FOR TRAINING\n", " max_seq_length = max_seq_length,\n", " dtype = dtype,\n", " load_in_4bit = load_in_4bit,\n", " )\n", " FastLanguageModel.for_inference(model) # Enable native 2x faster inference\n", "# alpaca_prompt = You MUST copy from above!\n", "inputs = tokenizer(\n", "[\n", " alpaca_prompt.format(\n", " \"What is a famous tall tower in Paris?\", # instruction\n", " \"\", # input\n", " \"\", # output - leave this blank for generation!\n", " )\n", "], return_tensors = \"pt\").to(\"cuda\")\n", "outputs = model.generate(**inputs, max_new_tokens = 128, use_cache = True)\n", "tokenizer.batch_decode(outputs)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 0 }, "id": "RYh575LYOG5J", "outputId": "03297f11-18be-4a2f-be04-5700a9440e4f" }, "execution_count": 18, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n" ] }, { "output_type": "execute_result", "data": { "text/plain": [ "[\"<|begin_of_text|>Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\\n\\n### Instruction:\\nWhat is a famous tall tower in Paris?\\n\\n### Input:\\n\\n\\n### Response:\\nThe Eiffel Tower is a famous tall tower in Paris. It was built for the 1889 World's Fair and is one of the most recognizable landmarks in the world. It is 324 meters tall and is made of iron. It was designed by Gustave Eiffel and his company. It is a popular tourist destination and is often visited by millions of people each year. It is also a symbol of French culture and engineering. It is located in the Champ de Mars park in the 7th arrondissement of Paris. It is a very popular tourist destination and is often visited by millions of people each year. It\"]" ] }, "metadata": {}, "execution_count": 18 } ] }, { "cell_type": "markdown", "metadata": { "id": "AEEcJ4qfC7Lp" }, "source": [ "Now if you want to load the LoRA adapters we just saved for inference, set `False` to `True`:" ] }, { "cell_type": "markdown", "metadata": { "id": "QQMjaNrjsU5_" }, "source": [ "You can also use Hugging Face's `AutoModelForPeftCausalLM`. Only use this if you do not have `unsloth` installed. It can be hopelessly slow, since `4bit` model downloading is not supported, and Unsloth's **inference is 2x faster**." ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "id": "yFfaXG0WsQuE" }, "outputs": [], "source": [ "## If using model.save_pretrained(\"lora_model\") # Local saving\n", "if False:\n", " # I highly do NOT suggest - use Unsloth if possible\n", " from peft import AutoPeftModelForCausalLM\n", " from transformers import AutoTokenizer\n", " model = AutoPeftModelForCausalLM.from_pretrained(\n", " \"lora_model\", # YOUR MODEL YOU USED FOR TRAINING\n", " load_in_4bit = load_in_4bit,\n", " )\n", " tokenizer = AutoTokenizer.from_pretrained(\"lora_model\")" ] }, { "cell_type": "markdown", "metadata": { "id": "f422JgM9sdVT" }, "source": [ "### Saving to float16 for VLLM\n", "\n", "We also support saving to `float16` directly. Select `merged_16bit` for float16 or `merged_4bit` for int4. We also allow `lora` adapters as a fallback. Use `push_to_hub_merged` to upload to your Hugging Face account! You can go to https://huggingface.co/settings/tokens for your personal tokens." ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "id": "iHjt_SMYsd3P", "colab": { "base_uri": "https://localhost:8080/", "height": 704, "referenced_widgets": [ "6ff92caa71e9479e9d93fbf8c87bb18d", "e6a731c33b5b498e83d1ea18644d703f", "e900a44f76524f5187370adfbdf511fd", "526205e307d44f4fba2e14b19e30a82a", "e93c418d853e4715bbefc1e39070e23c", "a41d763c7a6348a498cca95310e07a2e", "943b5f5110074577874af3ec251c9080", "979a80380c3549dc907369488674357d", "c64624b07bb54dba94fd68946d3f2210", "8c1c45979929447490988d8fd36503f2", "48516c34b3964b248ef4b7cd05a16484", "c2802a5c1313452bb007bd6397cfc450", "3e1098ab73bf45578bfb3c89bf6befa7", "51a1b2b57cc54b50a19d51194833dd22", "96d0061fa15c435c81588a0ed9dc9ee4", "1d657cd92ea4408c8493b33765a4e13e", "a28628fec96f4067b398c0b5c704545b", "6b368b6639f2470986f1600683bee6e3", "15c9417c0cb64a09a4b36c8d5d0a84c1", "95fb61bed5134e96a4efdad291dd1944", "c8ea84b00f314f9ea04b2361d9127a6c", "c8934468a7774286a29db3cff3d5fc99", "78aa3f5e4db34dc7ad26bf0e7e12c859", "0c658f9c416f40b8a90b39df3b9bb98f", "0978430d2522498f9144addb8cfef55c", "6c2a306827b14e4e84d75f1310407c6b", "63b45a3a285548a6826bdb9b33a00238", "c4f7208251ae424db4dc269f271d56d6", "50bea783492b409ea131878c680175ba", "2c4d5429b32e493fb2b9afda8941820c", "b86e4a8517f14520bbfd36378b50ec0a", "9c3d87ef9014441194f009471f5cac52", "ddd87b11c91244a5a37254ebf5c19d0c", "5e7c0f02ea044a43864af5f4d3ef593f", "17021618a6ed41a5879e318f0e003e92", "0a159c5369644c09aeb895e8b0409b89", "b612a2dcd4734b4aa07f9e5fce593449", "d11dab015caa4790b026b3de5a87b5ce", "21f8de5e4935468abda10eb93e040ead", "4069a6e559724e27ab97b5d2ea5117bb", "e45554733ddb4dff8702e2f34f168021", "f476518ba73342bfbbee9134aec731b7", "4198a15980b84e43b2ac537f844badb0", "d0e266c66d0c43b9bcdba401f2c2550d", "d618bb01d1af4a908b22e3f5d49911bf", "73aa68493cad47eb98c8df770f08b884", "e9b4718173b5439b8fc54ab7df8fe06f", "3a4975647d104c2fb73f26e05d5bc403", "d3fce84d68be4989ac53a85cd060600b", "f5093b216e5b4c3ca22cbaf913b13d47", "7d6fee3051af4eb096ffab714dfb573e", "79b747086e1d405c8ff68bcf02e43678", "278801d957914bdd81236111e8f7c056", "72ebcccf9f8649cea6d876d6da7d87e4", "748de31a00fa4e44b9a241ee230a2d48", "47e19f14375e4281a72d8b7c17bba6c2", "c1e8d3edae9f49e5bd9ac214fedf251c", "590e2d592bf04349abc0df3637145f94", "c51f6d9fc1b340aeaa6a0f3043f2ced6", "8efc424ca25e4912ab77bfbe84cfd825", "d6fd9ed571934663be7ec5df5de21997", "50e566859e664c72ac98c245e196599c", "e3d08cbf9f92424e95baac40ff07d598", "a1b7fd7dbcd04c86b9713e944acd559b", "7fadcce7759742dca97094d7f1526008", "bb2e07098e2941eea79782f9b03483f7", "4759c100f53943d8937bf50fce3c14ec", "367e9dd17b2e4d60a4684180d0b9d719", "ff9ff71826924a4fa4062b99b5aea5ec", "b38a5834a7504899bb694d342be19c1b", "bf77e608ad184fbdaeb81de10a87a92e", "2ea1236b4c1e46628c610bd94ec506d3", "dcce1009478c464086483eb2cb7ea9a2", "a7786c67847a458d937ba0d48c2f9bf8", "f816da56e959459e9ef0a716469b03a7", "8a757896ce3c48389baa53e94bf6fef6", "6934b827642b40b183cec24f956651e6", "16df07af8d15452daec2fbc66108a7ce", "05b398a78e4644d09d81d6a90c4bc9bd", "33fdbbfd5a924717aba5720d77ca7b88", "7a834af849484fc1a573350d98e3115e", "d90746dea4cb406295d908c311b809da", "bdcf13c1de3842bbbc25310ec2bde884", "751f5f851389409eb4696f56ad90e781", "ed325f920433432cb7592fcb340cf7fc", "ce541ed3f9844a1e990bc1b30807db5c", "df878f4f9c4d46dba7b5865848d1075a", "40651784c22f4b38940da97814c1df70", "8e3deba3066b43eb80841c4dbcd6cb5a", "37cbe2940d124502a8e1370ff7b6c6bf", "60204b938d3e45bb9e0f9acdc52c210f", "5b7fece81c774d8681c2cef75832eefa", "cefb35de9a834137959fd4d2c07e8b3f", "6126960a74304eab8de32793be33ae93", "37916e4fd30744e8b344c52d16e2c29a", "4c1922bf92e0407c97166bb638849907", "a376879575a844d099879dc0cb1559bf", "62f7677452fe4869b4965952482d8702", "9c219fc495c5430082a7047e5a0664ec", "e6095c6034ec4679b49b999d80064f63", "2243ac683194423ebfccddbc4076cc92", "81de3346a33a486d9eddc016903b492c", "75efde6b3a444c9cbb4663929207b3a9", "788bb3eceed54721bbae7651c32ef846", "2ddd2288083d4c11b7f8ff37a17f37f9", "b0ded5b332c0430b81fbe34d8b2898ef", "1ce9bf88b83549beac7113f96afa8668", "f1bc7a310ea84fb595a63a6cf4b50f87", "eff4d25831bb407886e1be11a4a80e56", "0311abe1f72744eba4d97eeac5d25404" ] }, "outputId": "beff48d6-275b-4d81-9521-00ad79eba950" }, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "Unsloth: Kaggle/Colab has limited disk space. We need to delete the downloaded\n", "model which will save 4-16GB of disk space, allowing you to save on Kaggle/Colab.\n", "Unsloth: Will remove a cached repo with size 5.7G\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "Unsloth: Merging 4bit and LoRA weights to 16bit...\n", "Unsloth: Will use up to 61.34 out of 83.48 RAM for saving.\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "100%|██████████| 32/32 [00:00<00:00, 65.84it/s]\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "Unsloth: Saving to organization with address kevinkawchak/Meta-Llama-3-8B-Instruct-Molecule16\n", "Unsloth: Saving tokenizer... Done.\n", "Unsloth: Saving model... This might take 5 minutes for Llama-7b...\n", "Unsloth: Saving to organization with address kevinkawchak/Meta-Llama-3-8B-Instruct-Molecule16\n", "Unsloth: Uploading all files... Please wait...\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "model-00001-of-00004.safetensors: 0%| | 0.00/4.98G [00:00\n", " \n", " \n", " Support our work if you can! Thanks!\n", "" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "id": "zmdJjv6-GarM" }, "outputs": [], "source": [ "# from google.colab import runtime\n", "# runtime.unassign()" ] } ], "metadata": { "accelerator": "GPU", "colab": { "gpuType": "A100", "machine_shape": "hm", "provenance": [] }, "kernelspec": { "display_name": "Python 3", "name": "python3" }, "language_info": { "name": "python" }, "widgets": { "application/vnd.jupyter.widget-state+json": { "6cbe43cecb164685a0ed856b6a8fbe4f": { "model_module": "@jupyter-widgets/controls", "model_name": "HBoxModel", "model_module_version": "1.5.0", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HBoxView", "box_style": "", "children": [ "IPY_MODEL_b4931b20365041dfbdc8dda72c90f009", 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