{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "IqM-T1RTzY6C" }, "source": [ "### Text Completion / Raw Text Training\n", "This is a community notebook collaboration with [Mithex].\n", "\n", "We train on `Tiny Stories` (link [here](https://huggingface.co/datasets/roneneldan/TinyStories)) which is a collection of small stories. For example:\n", "```\n", "Once upon a time, there was a little car named Beep. Beep loved to go fast and play in the sun.\n", "Beep was a healthy car because he always had good fuel....\n", "```\n", "Instead of `Alpaca`'s Question Answer format, one only needs 1 column - the `\"text\"` column. This means you can finetune on any dataset and let your model act as a text completion model, like for novel writing.\n", "\n", "---\n", "\n", "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)." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "2eSvM9zX_2d3" }, "outputs": [], "source": [ "%%capture\n", "# Installs Unsloth, Xformers (Flash Attention) and all other packages!\n", "!pip install unsloth\n", "# Get latest Unsloth\n", "!pip install --upgrade --no-deps \"unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git\"" ] }, { "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", "* 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.\n", "* [**NEW**] We make Gemma 6 trillion tokens **2.5x faster**! See our [Gemma notebook](https://colab.research.google.com/drive/10NbwlsRChbma1v55m8LAPYG15uQv6HLo?usp=sharing)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 544, "referenced_widgets": [ "3ed22d570eca4c55acda46f55947d2cb", "a4eeea2321024e8ea0d872e229b40f81", "cb65527563fb4600b4d60b09c64435df", "dc398d957aba4892899ee208bd6fa22d", "b4a53654151547fa96723ef7c8da3484", "a8c8e1fe56ef43deb00e0bef0f9dcdad", "502a1cac537049309d0e30820d8d220c", "21842cb2c9a04fafbb8a2f0d8da5b3fa", "4ddd40f2a84948bbb1009293db575ce3", "fa7ab358f57944389e4aef77083fc92d", "10b409b3bd764087bb1c6db9bb333979", "0c71d78a478f41cfb12437c69a4ed20d", "762968575ed548ec8ce2e2fdcd6bc59f", "48ddb64d7d3049f1a0272b397f4ba080", "72ccc2902344429d88d43a51458d5907", "06bab0cd87e14572bd099372a8d87d76", "198789833531497fa89b4995eb95fc52", "437826fc9ce146f6ac3218f03a3cd7be", "b13145d2e0ad4cd7856187db795716bd", "0fa4dd75cd4f42eda0902a1fa987f146", "8aca26fe021546a88f7d712821b234ce", "b0ae80e0cdd84f0e8cbf23ff7553755a", "d8546dd6f2e34700a4b72f1a83eea43b", "e3f30cefa8d04b02bde03bcbcdaf882f", "2cb8cfa84a384e6fb3a661c111fcd07d", "a4a107882dff4b279712885724d8be9c", "5661993677244881b3c3f603ca740527", "e162658f9cb746bab7b825daf72c7523", "f4bb27b8cc1749ea925cd6a54a41f668", "b5f0c4231fbc45c6a9353f5cfc9f498e", "385b31462a22451ebcfbd5bc94a44832", "c7a3550b3d3e449882d391c6d0c0221c", "4670a594138049cb9c497642881b85d8", "fb2dce23ecf8434296e6bfa1c2753097", "0fe587911e654bce8725a5cba8c795ab", "9628a1434b2f4570adb825ae62fc1233", "967bde91e3194fff9b379e0a6c505ef0", "fb38ed04735c402f8f6192997e01daa1", "87c36d2642314cf8a6dc4ad6ea1e8574", "8d115968ebf74e17a6574ca798ad7f9b", "2cf83c59f7f94c4f9c00471e733f8c3a", "fe60666244f54d8ca5e5c083b7d98847", "0eca08912464456d971801f94e0e2b8e", "4a1ec309f7d14f2c85672b7924061c7c", "1de9f082e1fb41c6a63459bd3afde9de", "c0fba6fd7dea46109f3e0ab2b8ef1064", "4fd521fc906d461293bb4c3365a665ac", "82dcb625abf647f48e777551cc6a38a9", "04af61388cef4dd48d148fc0af337c88", "b107fa3dc9e84d7fa0899a483c9f1896", "c7d674313f37409d854c5dc410494e6b", "d4859507e29c45e9a1c44b38d1e5f4bc", "a9900c9bc268427ca7c80d6d6029bfb1", "efd9b0eb99d04cf4bfa41e708276c676", "f4428f4e47fb4e6f92ac56655e4f5bf2", "c70aa480f1b2459ba4a7ff890059be6f", "c86b2011ea954330aeeb057ba5514d34", "ec08134518b64559843274f98ec39476", "b3790c27c23b4bf4a66fd8ead5d3691c", "f8649e3bb204408989f2bcfd20caccc7", "d49a73b3cfd643818511739bdfe05ce5", "45bef6e65fa1422387fb832408b66f2f", "a9f91c6c31d64ed4bd5e65dc7f13df2e", "1586226a393d45849b9cda3da2e69df7", "702c588498dc4c73a44dc344a7224d0c", "9cbed74410eb4c25a422a0b0bbc4aaa6", "3ec18740c417409d8ead51b692822665", "ebb0fb8efac2407b99f8ecfe044b17ef", "c93d5c8c5ff442e086485dd2d35ab7ad", "c2cf375f71204296911b458abe7fbfbc", "c362c5ac202f49de8a2c509a48e6b526", "ed8e2731bcb6427aab2171a742d1e4fb", "43a3f23b1d30444e88154cdd354705a1", "6081ecae918a4bbbaff857b335c6931d", "e8194ceb9071472d8c61a78e5a4a7fcd", "5cbcd2bd34eb484884364bb20bfbf122", "319a78326460455d9958414fb2c94c1c" ] }, "id": "QmUBVEnvCDJv", "outputId": "9aa5630f-0e68-49c9-e28d-830071785da7" }, "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", "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: UserWarning: \n", "The secret `HF_TOKEN` does not exist in your Colab secrets.\n", "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n", "You will be able to reuse this secret in all of your notebooks.\n", "Please note that authentication is recommended but still optional to access public models or datasets.\n", " warnings.warn(\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "config.json: 0%| | 0.00/1.05k [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 = 16,\n", " lora_dropout = 0, # Supports any, but = 0 is optimized\n", " bias = \"none\", # Supports any, but = \"none\" is optimized\n", " use_gradient_checkpointing = True,\n", " random_state = 3407,\n", " max_seq_length = max_seq_length,\n", ")" ] }, { "cell_type": "markdown", "metadata": { "id": "vITh0KVJ10qX" }, "source": [ "\n", "### Data Prep\n", "We now use the Tiny Stories dataset from https://huggingface.co/datasets/roneneldan/TinyStories. We only sample the first 5000 rows to speed training up. We must add `EOS_TOKEN` or `tokenizer.eos_token` or else the model's generation will go on forever.\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)." ] }, { "cell_type": "code", "source": [ "from datasets import load_dataset\n", "dataset = load_dataset(\"roneneldan/TinyStories\", split = \"train[:5000]\")\n", "EOS_TOKEN = tokenizer.eos_token\n", "def formatting_func(example):\n", " return example[\"text\"] + EOS_TOKEN" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 328, "referenced_widgets": [ "a889f712c5fb469e8a044692704366aa", "b453fc4f82d2438aa8fae2a4d4e79e20", "d1c527d56d2b43ca8ec654924edcb742", "2ccff44085154c79875c69ca79837b46", "2892f5bcfc7842228cf16979da7d75be", "48fafe4d6a574733a7a2c11ca51a2aa1", "e72a3154df734f5e858ca290a70c386c", "9298c450eb174dccbf043800d24a3243", "38326403b67e437c992647aaec25296e", "3a41ea39d7ba4e5ab16aabeecdeb1f4f", "082d5dcfdac743efbe1a5c8ccf42f3e7", "ebf15a8545a74833ab850cb639aa2a54", "46e6822fc59a478992c76fe3d47c6035", "41e2b7c577f0430a9e4852a29deaeb38", "b189a4dd5e454242abf3987783b42636", "50a8eae1938c4d7c8a921e8404bad700", "ac645232041c430ab0e33c3a09ad3a48", "16bff0ca5ca84133a89994a3d9a54622", "3ed673a19a0a420cbe524c572d6e0ec5", "1fadddc9bae54158bb5a12072164642c", "a90b28a444bb471888ca4a1a32efa94b", "899e2ff126da421392b35500ccf3bf43", "bc74d0b7a71845818cf654ece7199bd7", "1a1eee04c0d14b3baab2bc354458a162", "0f78f378d596474b975f2b5f65f02c5f", "da5d5bd2d8344575bef993a6309c236b", "1407078564154f28896e4ad33bdd5263", "7211ba65f5134feebc16c7d506cc7c33", "1ae4013f8df3464c8f8cf9e8091500e9", "7799c4db6fab4b0993fef75e1a9919ec", "16369b80910f4787bb345a0a7fa56e9c", "176e421751fe4cd4964954d4df71ec71", "6efad100fcec451e8cba04bcd682f6e8", "e6c0d8d285d24b87a5d68eebfa40f9cd", "d3107bcb039544fe9a2a2a80a12c879b", "41f304664fde4aa6bd940fc3444a9256", "929e34b82bf54b2583764a6d569794a6", "7e503479984f4248995128dad11bf378", "87ef17bec186436a8e41df58ddd1c90c", "393bb8a05df2496b8d421374bee6563d", "2417f74964154db4ac0cf033fa9d435a", "ee8b1f9dab1a418e968f2aa3a94f0fb8", "84fa9164f08d4baa81cdd4bfc6612e5b", "2ca43adb25a046e7b52c381364110f5e", "4d798540f35f4de5876c699ba78d078e", "effeca09f4b24ce1ad873b76791c0552", "8ec057345c7a41819e1f8511c3a8afa4", "e129b98cb9cb4864872e6957a1b3995e", "fd35aca093a84f13be1b1341635470c7", "db17cb5af42e4e92b4b46f6f3cb00ef4", "7f8614a911324e2d9784a32c2999d996", "f464da5c2fd44a6d9e533cd9bba0a344", "620856679f144503afaabf36314b4476", "19457e74e02c46299967ab329d84c693", "9251fffd4bff44a18a63f42b6db04896", "9905ea6ba83046da8607fd5d5577976d", "08323848d9054c3a8a64095882069812", "0829b700a5774f81bc6561e79932ee62", "3f31c5856cea454f9f5fe8530a35189a", "1b0538b4055c4038aa8b77e80f163b67", "6a4a1d8b88fe4fde9d10853128a99eb5", "470329cd49374347bfa3b5a82c5f251b", "b99bc43602a9449e937060b65600ac98", "4b2976513b804f739e7ed0673de1c0e0", "445f836907a345319fe6152e5fd1542f", "6e84b35f527d4d4f9d5537e6ab530944", "382844cf5f9b49519352f536f6a5d820", "a4f22ccf32f247d9a83367467fd5c533", "c381909a0cfe4ebb88db3235c669a282", "f1b360fc217e494e9c6d92c32bfd92fa", "8a4a859047074b62acfdbf4487652ada", "24364d81ff6f4102b6adb94bfb3f6d1f", "471c5cb7fd8b4fe38e0d3f36b840d2c2", "58b43b2c3afb47d399bd84380088edc0", "42d4591515dc42cb8a51ca2790ba93a3", "e359855c2304459883d9311693b08e58", "c4ef6182a2ac4d8fb7e75fb3d0d8fdb2", "ad861040f60a44a7a7524f42b7a33a3b", "28cb0c0271a14cef8f379e9117631347", "a1c5cb75ea6a4c5eadcd9bb743bec9b7", "a6b5083af3d54116bfc485652896c2bf", "6c2d26ba0d444d1e8ac68b65532f04d8", "9bea6a976977438d81c459f8757a6ff6", "494eb01d3168432c857bb0e9b7e8f452", "9cfff887ed7245cba844a7a06bf0bb6d", "d404d4b516ee4d7fb1fab772a9b70f94", "40b09c098e944178b8a5dfadf5db4255", "2aa9fab58791435f895b722340b82431" ] }, "id": "yXt8Na97yRe7", "outputId": "aee589ee-cfd3-44b4-fdeb-f610fae1d88a" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "Downloading readme: 0%| | 0.00/1.02k [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 epoch 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": 49, "referenced_widgets": [ "792a780452044d7199c517d38ef84d11", "1c88a224e5ae4a8ca4b82795138787a4", "f34552e0f0e446e49f714331638bf5d9", "622466a9f2d74eef9d7cadcabff11130", "c15f2c0028504c59a0e8e9b27bfaf64a", "e83ab29d24be4f79aafd80633e035408", "7f3b95f1fb6e4a1d8b825197586bfbb6", "addb0064e45b4986887a834c65f6d336", "b2372ca1334040bc8b7505a3de0ceafc", "6133c7f61f084f8089a339fa2f5334fc", "b5ee524977374a7681448fb8e0501532" ] }, "id": "95_Nn-89DhsL", "outputId": "acaca9cc-81cc-45f1-bc17-20817c731a5e" }, "outputs": [ { "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": "792a780452044d7199c517d38ef84d11" } }, "metadata": {} } ], "source": [ "from trl import SFTTrainer\n", "from transformers import TrainingArguments\n", "\n", "trainer = SFTTrainer(\n", " model = model,\n", " train_dataset = dataset,\n", " dataset_text_field = \"text\",\n", " tokenizer = tokenizer,\n", " max_seq_length = max_seq_length,\n", " packing = True, # Packs short sequences together to save time!\n", " formatting_func = formatting_func,\n", " args = TrainingArguments(\n", " per_device_train_batch_size = 2,\n", " gradient_accumulation_steps = 4,\n", " warmup_ratio = 0.05,\n", " max_grad_norm = 1.0,\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", "id": "2ejIt2xSNKKp", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "51ffdd12-b8d5-49f2-f948-f16335aef092" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "GPU = Tesla T4. Max memory = 14.748 GB.\n", "4.625 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": { "id": "yqxqAZ7KJ4oL", "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "outputId": "054c2bd9-802e-4d75-c1c9-cdb376f1acf1" }, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "Unsloth: `use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/html": [ "\n", "
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11.542700
21.347800
31.492000
41.403900
51.325000
61.323100
71.201200
81.355400
91.419700
101.350200
111.292900
121.387500
131.398200
141.245400
151.309100
161.179700
171.293900
181.257600
191.338600
201.205900
211.315100
221.235100
231.215500
241.196200
251.200800
261.254000
271.243200
281.263800
291.268800
301.264300
311.201800
321.386700
331.182100
341.292800
351.237500
361.344800
371.086000
381.334600
391.074500
401.196200
411.209800
421.286500
431.136000
441.252000
451.235100
461.293600
471.102100
481.158700
491.226400
501.247100
511.164200
521.103500
531.082800
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" ] }, "metadata": {} } ], "source": [ "trainer_stats = trainer.train()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "cellView": "form", "colab": { "base_uri": "https://localhost:8080/" }, "id": "pCqnaKmlO1U9", "outputId": "8842533b-de11-4133-c6d1-a4749d3f6787" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "2922.33 seconds used for training.\n", "48.71 minutes used for training.\n", "Peak reserved memory = 7.852 GB.\n", "Peak reserved memory for training = 3.227 GB.\n", "Peak reserved memory % of max memory = 53.241 %.\n", "Peak reserved memory for training % of max memory = 21.881 %.\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!\n", "\n", "We first will try to see if the model follows the style and understands to write a story that is within the distribution of \"Tiny Stories\". Ie a story fit for a bed time story most likely.\n", "\n", "We select \"Once upon a time, in a galaxy, far far away,\" since it normally is associated with Star Wars." ] }, { "cell_type": "code", "source": [ "from transformers import TextIteratorStreamer\n", "from threading import Thread\n", "text_streamer = TextIteratorStreamer(tokenizer)\n", "import textwrap\n", "max_print_width = 100\n", "\n", "inputs = tokenizer(\n", "[\n", " \"Once upon a time, in a galaxy, far far away,\"\n", "]*1, return_tensors = \"pt\").to(\"cuda\")\n", "\n", "generation_kwargs = dict(\n", " inputs,\n", " streamer = text_streamer,\n", " max_new_tokens = 256,\n", " use_cache = True,\n", ")\n", "thread = Thread(target = model.generate, kwargs = generation_kwargs)\n", "thread.start()\n", "\n", "length = 0\n", "for j, new_text in enumerate(text_streamer):\n", " if j == 0:\n", " wrapped_text = textwrap.wrap(new_text, width = max_print_width)\n", " length = len(wrapped_text[-1])\n", " wrapped_text = \"\\n\".join(wrapped_text)\n", " print(wrapped_text, end = \"\")\n", " else:\n", " length += len(new_text)\n", " if length >= max_print_width:\n", " length = 0\n", " print()\n", " print(new_text, end = \"\")\n", " pass\n", "pass" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "RHm11vaQRt1U", "outputId": "768ebee8-7591-4157-d846-28f7a3e02e43" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ " Once upon a time, in a galaxy, far faraway, there was a little girl who loved to play with her \n", "toys. She had a lot of toys, but her favorite was a little green alien. The little girl loved to play \n", "with her alien, and she would take it everywhere with her.\n", "\n", "One day, the little girl was playing with \n", "her alien when she heard a loud noise. She looked around, but she couldn’t see anything. She was \n", "scared, so she ran to her mom.\n", "\n", "Her mom said, “Don’t be scared, sweetie. It’s just the wind.”\n", "\n", "The little \n", "girl was still scared, so her mom took her to the park. They played on the swings and the slide, and \n", "the little girl forgot all about the loud noise.\n", "\n", "When they got home, the little girl was still \n", "scared. She didn’t want to play with her alien anymore. Her mom said, “It’s okay, sweetie. You can play \n", "with your alien again tomorrow.”\n", "\n", "The next day, the little girl was happy again. She played with her \n", "alien all day long, and she didn’t hear any loud noises. She was happy and safe, and she loved her mom \n", "and her alien." ] } ] }, { "cell_type": "markdown", "source": [ "You can see it's not about Star Wars, but more similar in style to Tiny Stories! Let's try it again, but by disabling our finetuned LoRA adapters. We now expect it to be about Star Wars." ], "metadata": { "id": "UB_GzeHXVAwG" } }, { "cell_type": "code", "source": [ "from transformers import TextIteratorStreamer\n", "from threading import Thread\n", "text_streamer = TextIteratorStreamer(tokenizer)\n", "import textwrap\n", "max_print_width = 100\n", "\n", "inputs = tokenizer(\n", "[\n", " \"Once upon a time, in a galaxy, far far away,\"\n", "]*1, return_tensors = \"pt\").to(\"cuda\")\n", "\n", "generation_kwargs = dict(\n", " inputs,\n", " streamer = text_streamer,\n", " max_new_tokens = 256,\n", " use_cache = True,\n", ")\n", "\n", "with model.disable_adapter(), torch.inference_mode():\n", " thread = Thread(target = model.generate, kwargs = generation_kwargs)\n", " thread.start()\n", "\n", " length = 0\n", " for j, new_text in enumerate(text_streamer):\n", " if j == 0:\n", " wrapped_text = textwrap.wrap(new_text, width = max_print_width)\n", " length = len(wrapped_text[-1])\n", " wrapped_text = \"\\n\".join(wrapped_text)\n", " print(wrapped_text, end = \"\")\n", " else:\n", " length += len(new_text)\n", " if length >= max_print_width:\n", " length = 0\n", " print()\n", " print(new_text, end = \"\")\n", " pass\n", " pass" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "dZr6SsmDS1h5", "outputId": "e0cd6a02-e8b5-4f67-a863-a399146f4a5a" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ " Once upon a time, in a galaxy, far faraway, there was a young man who was a huge fan of Star \n", "Wars. He had seen all the movies, read all the books, and collected all the toys. He was so passionate \n", "about Star Wars that he decided to make a career out of it. He became a Star Wars expert, and he was \n", "hired by a major movie studio to work on the next Star Wars movie.\n", "\n", "The young man was thrilled to be \n", "working on the next Star Wars movie. He was given a top-secret script and was told to keep it a secret. He \n", "was also given a top-secret costume and was told to keep it a secret. He was given a top-secret set of \n", "props and was told to keep it a secret. He was given a top-secret set of special effects and was told to \n", "keep it a secret.\n", "\n", "The young man was so excited about working on the next Star Wars movie that he \n", "couldn’t wait to tell his friends and family. He told them all about the script, the costume, the props, \n", "and the special effects. He told them all about the movie, and he told them all about the movie.\n", "\n", "The \n", "young man was so excited about working on the next Star Wars movie that he couldn’t wait to tell" ] } ] }, { "cell_type": "markdown", "source": [ "And it's about Star Wars!" ], "metadata": { "id": "TyaSSiI9VLNR" } }, { "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. Llama 7b 2x faster [free Colab](https://colab.research.google.com/drive/1lBzz5KeZJKXjvivbYvmGarix9Ao6Wxe5?usp=sharing)\n", "3. TinyLlama 4x faster full Alpaca 52K in 1 hour [free Colab](https://colab.research.google.com/drive/1AZghoNBQaMDgWJpi4RbffGM1h6raLUj9?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. Gemma 6 trillion tokens is 2.5x faster! [free Colab](https://colab.research.google.com/drive/10NbwlsRChbma1v55m8LAPYG15uQv6HLo?usp=sharing)\n", "\n", "

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