{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "8LKIA_qnVKOz" }, "source": [ "To run this, press \"Runtime\" and press \"Run all\" on a **free** Tesla T4 Google Colab instance!\n", "
\n", " \n", " \n", " Join our Discord if you need help!\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", "**[NOTE]** TinyLlama was trained on 2048 max tokens. With Unsloth, we can arbitrarily set the sequence length we want via `max_seq_length=4096`. We do RoPE Scaling internally to magically extend the maximum context size!" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "2eSvM9zX_2d3" }, "outputs": [], "source": [ "%%capture\n", "import torch\n", "major_version, minor_version = torch.cuda.get_device_capability()\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 \"unsloth[colab-ampere] @ git+https://github.com/unslothai/unsloth.git\"\n", "else:\n", " # Use this for older GPUs (V100, Tesla T4, RTX 20xx)\n", " !pip install \"unsloth[colab] @ git+https://github.com/unslothai/unsloth.git\"\n", "pass" ] }, { "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": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 474, "referenced_widgets": [ "188ee79c6ef44ae896a93c0184780f41", "155663a36c2d4949a6bba5ee2b4224b1", "5e48035e021b40aea0317f856b842379", "d9a92404c9f74425ac0cff862001b168", "56d1a29a8e724aa98b0e9b2d8c995713", "94a7e3827ab241dbb75d3d816354c694", "c21c74c7956d425c9049fb1cdc8b7d07", "49de6fa3e53e4259b87caabcb1cc50b2", "b53a34cdb16a4b8eb482b91f407b688a", "982fd1d338e148b2ba66050e03897dbb", "2be4a7d986c44fd4a222dc2f1d7b9918", "c8fd063c9d9c4270b3080677fdd92632", "cd700122445641279b905116cc3b73a1", "e2901aa31b3e4ba39ba138468af5c95b", "20fcb167b6c04792b627f7d13da5db94", "2c35f51a2e9e439f9757d27a4e8cfb5a", "4b7057f7b1aa4e9ea73e2aff19d5d7f5", "66917e3680214990a27525ee10ef0259", "6cdb22652f1f47648c8dfe632e7e87b5", "02b75597dc7c4d1cbf3c51b1fddd3ec6", "008b398b553c47e1a3c9bedac5644642", "78ef61071c244b86b20227e775267aa0", "e517fa5b72d2455eb39101c368165b38", "d532a4ba00d7488ead1366f690a28c41", "ff667ca8001e44b0a6146daf3de94e55", "49ac5705579c4035b864db8d06a3efd6", "800461f0e86545e6a99fbb868e028a27", "70df82f25eeb4f9f83778c3c4cab7ddd", "9986a56c71b4451ea852e49e34ebd2fd", "ed980dda0e504e29ae8ca06e1535073b", "680c3c2c0ea24be0b7a29ad770f28abd", "112fd48addfb42cba27b1939375d5e4c", "08ddb073da9b4a4981e5452df0e80c5a", "5579f451f9bc490fa924455dcf6351f0", "92d80dfad00e4b0abfa3054752385c21", "95ed136481974ccd84eb0488ed5c328d", "d52459dc6f224b169a49d1cbd2d92f50", "e73ae12084e848a8aa82e73c5efa9d85", "6324e9afb3b64beaa84a80622e4aa4af", "fe63f948843240c184084e159a8feef3", "78dcd43379e64fb994fc5d8ad392cd32", "6d258790472640f493b26790298aa366", "9eb629533a45449ea819ed32c2a35a41", "5adbfe3e9c424429851258c009d72f26", "5e683f7a34e74032a2543ce2db6d9ceb", "5d20dbac1cd74e378902f7f6ef71e1f5", "323cca9bfd6a4342b614043b95b8f722", "b792c64d4aed4f90938c58344961d32c", "56109c78c5454216af1f1e1910bee352", "a8a15bf2150246999bf268f0731f9e43", "6ffc5ef1a24249bfbaea21fbc8990298", "254643fe17ec4613bc26de5b3330c7f1", "9725fce622b845b192fbb319f01f56d8", "ee2af32e73d542f9b089d761eed9d801", "138e959064c04ff6a2b6d3a4eb0f6359", "083e0fb519b843e48d283a638e8a6dcb", "56bc8dce7dfb44789f0edb09df8dd450", "64d035fbc33f417d970f39c5aa14c93f", "437886691cdf40d4861575b7b91feb89", "8809b53bd5f74f228c0ab6df9a918796", "c2628e20f1154acd94bbcb282fd6554d", "8680fbf7a67940e7ac6f6d59873ef4e9", "12634ac7d05e490493d5c068087ff978", "0d950e93a67e4402949fce40ba6097b6", "103ed45882c3450a8bd9e55940d88c50", "6abff53f5ed74b8ab7bfe981547d44b4", "acedd5f384554c108b806e7ceef84382", "78f3ba544cdb4e36b76f3a0fe4d79075", "876f9871caac4fba8c6a0f86f98e2314", "4b2bf0e58403475a8b201e87cf3efb4b", "68a8b3c46c15441d9a67d1b578c64f1d", "a06afe98f7fa4d6a99a2defe9c7828cc", "063e74555fbb4ab381cdcbcabc37461e", "1cffaffc53624ffab7ce7a5830fcad07", "47255152beba4690b21cce344d30a548", "4e7766fa9a1048bcacaf4cea16f24d90", "ffa5224eb65142d3a3cd053fd43c14b7" ] }, "id": "QmUBVEnvCDJv", "outputId": "532355ad-a380-4841-d692-c69e9dd97c90" }, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.10/dist-packages/unsloth/__init__.py:67: UserWarning: CUDA is not linked properly.\n", "We shall run `ldconfig /usr/lib64-nvidia` to try to fix it.\n", " warnings.warn(\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "config.json: 0%| | 0.00/1.09k [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 = 32,\n", " lora_dropout = 0, # Currently only supports dropout = 0\n", " bias = \"none\", # Currently only supports bias = \"none\"\n", " use_gradient_checkpointing = False, # @@@ IF YOU GET OUT OF MEMORY - set to True @@@\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": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 145, "referenced_widgets": [ "a681577f4a00440496f2ee70e2f1198b", "c64f215f76c345c2aa1b98d23364ece6", "d32436f7cb7c49a88c2fa867add54367", "17e0b9c3b30e4d9ab34f684b0f0c8865", "93e320b8f6f242d3805ef65c81f33718", "d930f897415947ada08ce063f9cb8bed", "54e400aa31c0492fbbfd0d291eb9dd7f", "594b86fdbc6c4ac582aee52d0099a590", "d6b6c9420c644d42b314ac7599bd75b8", "f68e5c7237cd4260be1644f4a33ba4b9", "f5fb96b55ae943d5b0a12cef560be493", "de996974999a4ebbb8a11a8889b2c683", "030a45a01bab49d29a6058e4f1f3f9ba", "b4b50aea923441d88530edd555673a0e", "e138d8341f094d60a05f2124f4d9fc50", "2ffed33dbe52437591ed87c34f87eb61", "d3498bc5c493472e9d785129ca8293de", "08f540a5520b4fa5811a6c229231ab5f", "7806f5a3a9404bbcbcedfaf8d827a35c", "1223ef92bfd542aaa7f789b8bd799609", "f5ee29b85e664ef180df1d1a60db8223", "b4ac02ab0c814609824a3405d6e31206", "8375b238b9084bceb04c8d67e148f9dc", "a07890ec0fd740d3a84a8aae4979355a", "204dcfe6d8404137b8cb0ac57f82cb08", "becf56b2d70542b1bc330f09bb8c6174", "4dcaad14afbf4a2791d230239ff01b3c", "b51201dc59ab47aea72930e60625d2cb", "553c2e573f5947b4b5bdcb8b2168f016", "4172136737eb4cf48ce6ee5aa566224a", "6d3d3a38139147dfae6ce54b2adcbbf6", "5a55d70b2d0543d082603c586787b9ac", "b815a7d8a9c84a088537c8f3549e8930", "12f020de361f41128fac402f89be26d8", "4eca6c289693485ba95ee9badb47389f", "83736d38ebea43e3b9c58ce36ef1351e", "385e918a99614ca5af1f3a058aae0d31", "ece8d196afd24a0cae98b6233f7e13cf", "af8a211d04f647bfb90cd72ed16bed05", "22e26e8fcc344cd9a9a76a1e2c8bbf53", "f097c1e989394c7f8a226f08ff7a6e9a", "12e46295371a46bc94e7632a49917e60", "82b55fbb2ba74150900dddf1143e54f0", "c6e634b3cd924881a2ac9745fe32cd2f" ] }, "id": "LjY75GoYUCB8", "outputId": "3970582b-742e-41c7-813d-27b978b49c29" }, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "Downloading readme: 0%| | 0.00/11.6k [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 1 full epoch which makes Alpaca run in 80ish minutes! We also support TRL's `DPOTrainer`! See our DPO tutorial on a free Google Colab instance [here](https://colab.research.google.com/drive/15vttTpzzVXv_tJwEk-hIcQ0S9FcEWvwP?usp=sharing)." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 211, "referenced_widgets": [ "47b91d628c824007ba2a899908543fc4", "544c9ffdfd6346d19865bf34d43e26a3", "0a88fd16d72442198803c4c082bdd5ab", "e245c09fe14a49929a8274fdc356fd25", "5cb07c2ef3be4a9f90cac8a53565040f", "93cfec82c4e24cd8bbf6d0044c92f9a4", "7aea5b2b4cb944efae3dc1de39b72f83", "444156136b864dd4896c9f83135772f6", "0da54be740b943c38a4aa805692b35f2", "3e5a6d4cd4bb45879ed67ab7d9cbcc16", "22a3640c1e344b5c922e488a9a2feaca" ] }, "id": "95_Nn-89DhsL", "outputId": "0be04edc-0aff-481c-a52f-fce8283ac502" }, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "PyTorch: setting up devices\n", "The default value for the training argument `--report_to` will change in v5 (from all installed integrations to none). In v5, you will need to use `--report_to all` to get the same behavior as now. You should start updating your code and make this info disappear :-).\n", "loading file tokenizer.model from cache at /root/.cache/huggingface/hub/models--unsloth--tinyllama-bnb-4bit/snapshots/fc56510003ea9d49362400b8a362345150802c31/tokenizer.model\n", "loading file tokenizer.json from cache at /root/.cache/huggingface/hub/models--unsloth--tinyllama-bnb-4bit/snapshots/fc56510003ea9d49362400b8a362345150802c31/tokenizer.json\n", "loading file added_tokens.json from cache at None\n", "loading file special_tokens_map.json from cache at /root/.cache/huggingface/hub/models--unsloth--tinyllama-bnb-4bit/snapshots/fc56510003ea9d49362400b8a362345150802c31/special_tokens_map.json\n", "loading file tokenizer_config.json from cache at /root/.cache/huggingface/hub/models--unsloth--tinyllama-bnb-4bit/snapshots/fc56510003ea9d49362400b8a362345150802c31/tokenizer_config.json\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "Generating train split: 0 examples [00:00, ? examples/s]" ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "47b91d628c824007ba2a899908543fc4" } }, "metadata": {} }, { "output_type": "stream", "name": "stderr", "text": [ "Using auto half precision backend\n" ] } ], "source": [ "from trl import SFTTrainer\n", "from transformers import TrainingArguments\n", "from transformers.utils import logging\n", "logging.set_verbosity_info()\n", "\n", "trainer = SFTTrainer(\n", " model = model,\n", " tokenizer = tokenizer,\n", " train_dataset = dataset,\n", " dataset_text_field = \"text\",\n", " max_seq_length = max_seq_length,\n", " dataset_num_proc = 2,\n", " packing = True, # Packs short sequences together to save time!\n", " args = TrainingArguments(\n", " per_device_train_batch_size = 2,\n", " gradient_accumulation_steps = 4,\n", " warmup_ratio = 0.1,\n", " num_train_epochs = 1,\n", " learning_rate = 2e-5,\n", " fp16 = not torch.cuda.is_bf16_supported(),\n", " bf16 = torch.cuda.is_bf16_supported(),\n", " logging_steps = 1,\n", " optim = \"adamw_8bit\",\n", " weight_decay = 0.1,\n", " lr_scheduler_type = \"linear\",\n", " seed = 3407,\n", " output_dir = \"outputs\",\n", " ),\n", ")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "cellView": "form", "colab": { "base_uri": "https://localhost:8080/" }, "id": "2ejIt2xSNKKp", "outputId": "b58a5395-e0e3-43eb-b5dc-fd656d571a3d" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "GPU = Tesla T4. Max memory = 14.748 GB.\n", "0.879 GB of memory reserved.\n" ] } ], "source": [ "#@title Show current memory stats\n", "gpu_stats = torch.cuda.get_device_properties(0)\n", "start_gpu_memory = round(torch.cuda.max_memory_reserved() / 1024 / 1024 / 1024, 3)\n", "max_memory = round(gpu_stats.total_memory / 1024 / 1024 / 1024, 3)\n", "print(f\"GPU = {gpu_stats.name}. Max memory = {max_memory} GB.\")\n", "print(f\"{start_gpu_memory} GB of memory reserved.\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "id": "yqxqAZ7KJ4oL", "outputId": "985d469c-eff6-4d90-b0df-7e187efc0cf7" }, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "***** Running training *****\n", " Num examples = 3,000\n", " Num Epochs = 1\n", " Instantaneous batch size per device = 2\n", " Total train batch size (w. parallel, distributed & accumulation) = 8\n", " Gradient Accumulation steps = 4\n", " Total optimization steps = 375\n", " Number of trainable parameters = 25,231,360\n", "You're using a LlamaTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/html": [ "\n", "
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StepTraining Loss
12.280100
22.243700
32.245500
42.243600
52.302000
62.287400
72.283900
82.144600
92.237200
102.245700
112.286200
122.281900
132.204400
142.158000
152.278200
162.168600
172.147400
182.119500
192.122100
202.099000
212.072200
222.066000
232.073800
242.000600
252.043400
261.992100
272.020800
281.972900
291.984700
301.991100
311.973500
321.964000
331.896200
341.872200
351.862700
361.855800
371.792600
381.849300
391.745700
401.734800
411.646200
421.701200
431.705900
441.702300
451.650300
461.601100
471.639200
481.633000
491.612800
501.638400
511.593000
521.549500
531.500600
541.511000
551.489900
561.526300
571.447300
581.454200
591.454100
601.482700
611.403600
621.416300
631.422400
641.380600
651.425800
661.379000
671.404900
681.415200
691.362400
701.368600
711.412700
721.388100
731.352500
741.365600
751.332100
761.363200
771.379200
781.328400
791.289600
801.340600
811.325800
821.277400
831.294000
841.292500
851.285600
861.272400
871.223900
881.296300
891.313700
901.285300
911.290900
921.232500
931.242800
941.240500
951.227000
961.198800
971.224400
981.271700
991.205700
1001.251400
1011.207000
1021.249500
1031.225200
1041.228000
1051.191200
1061.255500
1071.194300
1081.184900
1091.182600
1101.191200
1111.250900
1121.213200
1131.146200
1141.177700
1151.217800
1161.245500
1171.154900
1181.205400
1191.155000
1201.176500
1211.152200
1221.203300
1231.194100
1241.222000
1251.153100
1261.172800
1271.191600
1281.215200
1291.207100
1301.200800
1311.177500
1321.140600
1331.141500
1341.146600
1351.122800
1361.152900
1371.190700
1381.154700
1391.183800
1401.160500
1411.096000
1421.124700
1431.121000
1441.182000
1451.144500
1461.182200
1471.151000
1481.152600
1491.224500
1501.116600
1511.149500
1521.162200
1531.099100
1541.119100
1551.142200
1561.188800
1571.135000
1581.159000
1591.125200
1601.183500
1611.123200
1621.139300
1631.129700
1641.111700
1651.093400
1661.139300
1671.125600
1681.100800
1691.137200
1701.087700
1711.052200
1721.153600
1731.132200
1741.127100
1751.125800
1761.120600
1771.123200
1781.148700
1791.128300
1801.154800
1811.101900
1821.150900
1831.085300
1841.152900
1851.141800
1861.090200
1871.167600
1881.109800
1891.059100
1901.071300
1911.111100
1921.146600
1931.125800
1941.082200
1951.112300
1961.159800
1971.097600
1981.107900
1991.114800
2001.103600
2011.082200
2021.080200
2031.103900
2041.120600
2051.106400
2061.123900
2071.118700
2081.070800
2091.096500
2101.107800
2111.084000
2121.138900
2131.082100
2141.101900
2151.080700
2161.124300
2171.082500
2181.098300
2191.089400
2201.090600
2211.109700
2221.084900
2231.062900
2241.090400
2251.119900
2261.122600
2271.106500
2281.068300
2291.148000
2301.120300
2311.051000
2321.115600
2331.070800
2341.124800
2351.071500
2361.083000
2371.081800
2381.045200
2391.127600
2401.120100
2411.089800
2421.173400
2431.100400
2441.107100
2451.121700
2461.037800
2471.103900
2481.112700
2491.134600
2501.108800
2511.100000
2521.056000
2531.089300
2541.078600
2551.084300
2561.073000
2571.062500
2581.110800
2591.074600
2601.151400
2611.104700
2621.099700
2631.084100
2641.077900
2651.078400
2661.046300
2671.042800
2681.090200
2691.042600
2701.112700
2711.115300
2721.125800
2731.092600
2741.115700
2751.101700
2761.081200
2771.094400
2781.057900
2791.060400
2801.133200
2811.053900
2821.102300
2831.075500
2841.115100
2851.029300
2861.038500
2871.055000
2881.110800
2891.116200
2901.050500
2911.073800
2921.075900
2931.131600
2941.141400
2951.140300
2961.096600
2971.079900
2981.061700
2991.091000
3001.075700
3011.123300
3021.098200
3031.118200
3041.089600
3051.102400
3061.078900
3071.074900
3081.037700
3091.069100
3101.071500
3111.088100
3121.074900
3131.101600
3141.097200
3151.041500
3161.117300
3171.065400
3181.090700
3191.095100
3201.108600
3211.089400
3221.083800
3231.059400
3241.091200
3251.063700
3261.045400
3271.042900
3281.136300
3291.086600
3301.067300
3311.053600
3321.096500
3331.104300
3341.040800
3351.090800
3361.069800
3371.025400
3381.030300
3391.049500
3401.081800
3411.090200
3421.065400
3431.068600
3441.111300
3451.091700
3461.062700
3471.090900
3481.099900
3491.037900
3501.108200
3511.102100
3521.085800
3531.054900
3541.087700
3551.103500
3561.063600
3571.080600
3581.095600
3591.054600
3601.056100
3611.093400
3621.044400
3631.052700
3641.051300
3651.060500
3661.063400
3671.044900
3681.108100
3691.074500
3701.038300
3711.047300
3721.072900
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" ] }, "metadata": {} }, { "output_type": "stream", "name": "stderr", "text": [ "\n", "\n", "Training completed. Do not forget to share your model on huggingface.co/models =)\n", "\n", "\n" ] } ], "source": [ "trainer_stats = trainer.train()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "cellView": "form", "colab": { "base_uri": "https://localhost:8080/" }, "id": "pCqnaKmlO1U9", "outputId": "ecb21ea9-b0f5-48ab-9145-5baedb68a203" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "5034.6413 seconds used for training.\n", "83.91 minutes used for training.\n", "Peak reserved memory = 13.508 GB.\n", "Peak reserved memory for training = 12.629 GB.\n", "Peak reserved memory % of max memory = 91.592 %.\n", "Peak reserved memory for training % of max memory = 85.632 %.\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": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "kR3gIAX-SM2q", "outputId": "2283bb0c-d568-4c92-8352-094ba12eaf06" }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "[' 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:\\nThe fibonacci sequence is a sequence of numbers that can be generated by adding the previous two numbers and then subtracting the previous number from the previous number. The first number in the sequence is 1, and the second number is 1. The third number in the sequence is 1 + 1 = 2, the fourth number is 1 + 2 = 3, and so on. The sequence can be continued by adding 1 + 1 = 2, 1 + 2 = 3, and so on.']" ] }, "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", "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!" ], "metadata": { "id": "V2otZJcevdpZ" } }, { "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", " \"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)" ], "metadata": { "id": "QYvyvuj5vd7H" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "uMuVrWbjAzhc" }, "source": [ "\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": null, "metadata": { "id": "upcOlWe7A1vc" }, "outputs": [], "source": [ "model.save_pretrained(\"lora_model\") # Local saving\n", "# model.push_to_hub(\"your_name/lora_model\", token = \"...\") # Online saving" ] }, { "cell_type": "markdown", "source": [ "Now if you want to load the LoRA adapters we just saved for inference, set `False` to `True`:" ], "metadata": { "id": "3CgqR2B0vmCt" } }, { "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", "\n", "# alpaca_prompt = You MUST copy from above!\n", "\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", "\n", "from transformers import TextStreamer\n", "text_streamer = TextStreamer(tokenizer)\n", "_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 64)" ], "metadata": { "id": "Yle1gGB3vmWK" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "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**." ], "metadata": { "id": "8m76iItmvni0" } }, { "cell_type": "code", "source": [ "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\")" ], "metadata": { "id": "wcMqKxzcvouj" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "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." ], "metadata": { "id": "xwCTbEUavpoC" } }, { "cell_type": "code", "source": [ "# Merge to 16bit\n", "if False: model.save_pretrained_merged(\"model\", tokenizer, save_method = \"merged_16bit\",)\n", "if False: model.push_to_hub_merged(\"hf/model\", tokenizer, save_method = \"merged_16bit\", token = \"\")\n", "\n", "# Merge to 4bit\n", "if False: model.save_pretrained_merged(\"model\", tokenizer, save_method = \"merged_4bit\",)\n", "if False: model.push_to_hub_merged(\"hf/model\", tokenizer, save_method = \"merged_4bit\", token = \"\")\n", "\n", "# Just LoRA adapters\n", "if False: model.save_pretrained_merged(\"model\", tokenizer, save_method = \"lora\",)\n", "if False: model.push_to_hub_merged(\"hf/model\", tokenizer, save_method = \"lora\", token = \"\")" ], "metadata": { "id": "gJKx0osWvqzz" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "### GGUF / llama.cpp Conversion\n", "To save to `GGUF` / `llama.cpp`, we support it natively now! We clone `llama.cpp` and we default save it to `q8_0`. We allow all methods like `q4_k_m`. Use `save_pretrained_gguf` for local saving and `push_to_hub_gguf` for uploading to HF.\n", "\n", "Some supported quant methods (full list on our [Wiki page](https://github.com/unslothai/unsloth/wiki#gguf-quantization-options)):\n", "* `q8_0` - Fast conversion. High resource use, but generally acceptable.\n", "* `q4_k_m` - Recommended. Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q4_K.\n", "* `q5_k_m` - Recommended. Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q5_K." ], "metadata": { "id": "mhc9u6HAvr3b" } }, { "cell_type": "code", "source": [ "# Save to 8bit Q8_0\n", "if False: model.save_pretrained_gguf(\"model\", tokenizer,)\n", "if False: model.push_to_hub_gguf(\"hf/model\", tokenizer, token = \"\")\n", "\n", "# Save to 16bit GGUF\n", "if False: model.save_pretrained_gguf(\"model\", tokenizer, quantization_method = \"f16\")\n", "if False: model.push_to_hub_gguf(\"hf/model\", tokenizer, quantization_method = \"f16\", token = \"\")\n", "\n", "# Save to q4_k_m GGUF\n", "if False: model.save_pretrained_gguf(\"model\", tokenizer, quantization_method = \"q4_k_m\")\n", "if False: model.push_to_hub_gguf(\"hf/model\", tokenizer, quantization_method = \"q4_k_m\", token = \"\")" ], "metadata": { "id": "2_TmxAoavvYW" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "Now, use the `model-unsloth.gguf` file or `model-unsloth-Q4_K_M.gguf` file in `llama.cpp` or a UI based system like `GPT4All`. You can install GPT4All by going [here](https://gpt4all.io/index.html)." ], "metadata": { "id": "SvFui8YuvZ1R" } }, { "cell_type": "markdown", "metadata": { "id": "Zt9CHJqO6p30" }, "source": [ "And we're done! If you have any questions on Unsloth, we have a [Discord](https://discord.gg/u54VK8m8tk) channel! If you find any bugs or want to keep updated with the latest LLM stuff, or need help, join projects etc, feel free to join our Discord!\n", "\n", "Some other links:\n", "1. Zephyr DPO 2x faster [free Colab](https://colab.research.google.com/drive/15vttTpzzVXv_tJwEk-hIcQ0S9FcEWvwP?usp=sharing)\n", "2. Mistral 7b 2x faster [free Colab](https://colab.research.google.com/drive/1Dyauq4kTZoLewQ1cApceUQVNcnnNTzg_?usp=sharing)\n", "3. Llama 7b 2x faster [free Colab](https://colab.research.google.com/drive/1lBzz5KeZJKXjvivbYvmGarix9Ao6Wxe5?usp=sharing)\n", "4. CodeLlama 34b 2x faster [A100 on Colab](https://colab.research.google.com/drive/1y7A0AxE3y8gdj4AVkl2aZX47Xu3P1wJT?usp=sharing)\n", "5. Mistral 7b [free Kaggle version](https://www.kaggle.com/code/danielhanchen/kaggle-mistral-7b-unsloth-notebook)\n", "6. We also did a [blog](https://huggingface.co/blog/unsloth-trl) with 🤗 HuggingFace, and we're in the TRL [docs](https://huggingface.co/docs/trl/main/en/sft_trainer#accelerate-fine-tuning-2x-using-unsloth)!\n", "7. `ChatML` for ShareGPT datasets, [conversational notebook](https://colab.research.google.com/drive/1Aau3lgPzeZKQ-98h69CCu1UJcvIBLmy2?usp=sharing)\n", "8. Text completions like novel writing [notebook](https://colab.research.google.com/drive/1ef-tab5bhkvWmBOObepl1WgJvfvSzn5Q?usp=sharing)\n", "\n", "

\n", " \n", " \n", " Support our work if you can! Thanks!\n", "
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