{ "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 + ⭐ Star us on Github ⭐\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] Gemma 2 comes in 3 sizes: 2b, 9b and 27b. 2b uses 2 trillion tokens distilled from 27b!\n", "\n", "**[NEW] Try 2x faster inference in a free Colab for Gemma-2 2b Instruct [here](https://colab.research.google.com/drive/1i-8ESvtLRGNkkUQQr_-z_rcSAIo9c3lM?usp=sharing)**" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "2eSvM9zX_2d3" }, "outputs": [], "source": [ "%%capture\n", "!pip install unsloth\n", "# Also get the latest nightly Unsloth!\n", "!pip uninstall unsloth -y && pip install --upgrade --no-cache-dir --no-deps git+https://github.com/unslothai/unsloth.git\n", "\n", "# Install Flash Attention 2 for softcapping support\n", "import torch\n", "if torch.cuda.get_device_capability()[0] >= 8:\n", " !pip install --no-deps packaging ninja einops \"flash-attn>=2.6.3\"" ] }, { "cell_type": "markdown", "metadata": { "id": "r2v_X2fA0Df5" }, "source": [ "* We support Llama, Mistral, Phi-3, Gemma, Yi, DeepSeek, Qwen, TinyLlama, Vicuna, Open Hermes etc\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**] We make Gemma-2 9b / 27b **2x faster**! See our [Gemma-2 9b notebook](https://colab.research.google.com/drive/1vIrqH5uYDQwsJ4-OO3DErvuv4pBgVwk4?usp=sharing)\n", "* [**NEW**] To finetune and auto export to Ollama, try our [Ollama notebook](https://colab.research.google.com/drive/1WZDi7APtQ9VsvOrQSSC5DDtxq159j8iZ?usp=sharing)\n", "* [**NEW**] We make Mistral NeMo 12B 2x faster and fit in under 12GB of VRAM! [Mistral NeMo notebook](https://colab.research.google.com/drive/17d3U-CAIwzmbDRqbZ9NnpHxCkmXB6LZ0?usp=sharing)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 331, "referenced_widgets": [ "c97875e799204d779e18fba0b95fd5bf", "008bc528049245b4ab976672dde740f2", "6e3b7320bb8d435484f5fdc9853766fb", "77637a4b61604515afa02bb6c899488b", "95647d8f43764e6fa94471c74fe2f8dc", "d39f7e18537747e2b00926439bd748e8", "7b6d2a8cac69408ebde121c54b913057", "437a79d742cb4620833cdae592883628", "913b8372b289451d999adaeac262367d", "99e6f1ce7b6340b88cdf66fa746719ad", "947cfedcb67246cca93b745e56831657", "4c72c0615369427e962f09cc45097193", "7f4e45f683994acf8a600d2a33e7503e", "d5ba89c9819d441aafa1778c9cab9788", "432a1d5bdb7042b7aaa2ad50605caf25", "b1ecc3cd4544446a867283bc933dba62", "459ec24d06f647dd9603ab4b472596c3", "3901bcb1f3c140d98335d17abac3076f", "f6076a72476341209a19678eef002957", "010e7bd8b70f4949970bbe588d9d8e9c", "3b4dc22b60f5486683a4ec5205eddcb2", "39545235a26d42118f4a00bed04d9935", "ab8732869fc3471ea8e0487873e48e15", "ae46801c915847f8a68c2e682b171300", "ca4f9478be974521924da6d8fc711f4f", "b28a11d4e7014f58b2489d0af76d03a8", "a564d5a7e76c4911a3d77e133f6cade8", "f58cca528af84097a48cc83f9e3063c9", "0a114acdc8724a2a8cc0a68d08ce318c", "142127d9cb584c63a5ba49b39110980b", "800d6f7702df4df883b1c3165a069c72", "76679887c3694a19aaaa4f4a0c629d3c", "0fc02717997a4c3cbd4dd1af900878a5", "6057012b69af430f804e9ef0fcc85965", "19adcd80d3d347eab5726809cf63c4ec", "9f84e86a514c48cda94512d037604191", "664cb3195726421aad47fa1b510fabe0", "7a7f822959cb4b72826f308846fc22f2", "1d46432ba7384bf7bc88efa7d81d673b", "c0a1c389d5e542e7a204227649b2a901", "265c6115d6724fe28c2ef7d527b49d58", "705e34282c2e480e99549c413cd393b5", "cf8ec709d5744feba9c37036433d2e74", "920a8e0cf14f4df58d33ba222d0ada51", "a8e94a46887745b7b654aa12bc553ac1", "c68a47d0530e4c29900d3ba7de231ad7", "dd422569be2d4971942ad457c82f15fd", "82b5e99c882646148f6df799b4a7a28e", "bce0bdc86b3046d3baf53a9d1a7051ec", "64e589c742f54f8ea22795887b340a1f", "9577de7111c44525af1eb0891603dc08", "bfa500998f894eeb9a394af9269a1ae0", "a22d8797c1c24b4aa43a2ebaa403a50d", "f4c8b2aac3854a338b7edbce6b25a8b4", "53d4611a86854cb9986a1164e964b896", "a391fc6789a74ff3871c9dd840a72369", "ccc8605e5ca14c299e0d462802c5b1e9", "ae01cc62a80f49179bc4a05098faaa2b", "fe7620492e33468289f20487ffbbd59f", "ecd72e97fbbb45b9b88b9c0254cc657d", "f0c6d01928c84826a5af0e35b7d5105b", "c925385cb4f44bf78b85927fea4c59e5", "2e98eddcab764e9c80c4cfc764aeb2d3", "177f2b5ef6f34fad9d304a3c41da1648", "54297e8d76a1417a8472df2b151356fc", "0f04afd664df41a48ae463c63cfef2d3" ] }, "executionInfo": { "elapsed": 50568, "status": "ok", "timestamp": 1722441633360, "user": { "displayName": "Daniel Han-Chen", "userId": "17402123517466114840" }, "user_tz": 420 }, "id": "QmUBVEnvCDJv", "outputId": "62b52782-c29b-4344-bcca-ec56d5e9a617" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "🦥 Unsloth: Will patch your computer to enable 2x faster free finetuning.\n", "==((====))== Unsloth 2024.8: Fast Gemma2 patching. Transformers = 4.43.3.\n", " \\\\ /| GPU: Tesla T4. Max memory: 14.748 GB. Platform = Linux.\n", "O^O/ \\_/ \\ Pytorch: 2.3.1+cu121. CUDA = 7.5. CUDA Toolkit = 12.1.\n", "\\ / Bfloat16 = FALSE. FA [Xformers = 0.0.26.post1. FA2 = False]\n", " \"-____-\" Free Apache license: http://github.com/unslothai/unsloth\n", "Unsloth: Fast downloading is enabled - ignore downloading bars which are red colored!\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "c97875e799204d779e18fba0b95fd5bf", "version_major": 2, "version_minor": 0 }, "text/plain": [ "model.safetensors: 0%| | 0.00/2.22G [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", " # [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 `llama-3` template for ShareGPT datasets, try our conversational [notebook](https://colab.research.google.com/drive/1XamvWYinY6FOSX9GLvnqSjjsNflxdhNc?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": [ "8b65ab1fa061457e89f295e8a81e635b", "07648f1794c643b5a041db478062037d", "bb2326a055064e86917136d18cb90138", "a9094171050f48b4aaa89073343cb415", "9caa0460ae4547ff95d8b460637d906c", "b1d816037bc0495290f6eb1e5d53475e", "ce7c4b36ff3f42578b38e43329a4e10f", "5c305f1c06434aa5ac5875dea13800be", "84b864f1427743078be3db1acc40cf2b", "5e4d90d2e5a84a6ba6bd981be0f4f59b", "2cb4f9f802b7469bae027a13508e941f", "6b6fe8ee676c4dbeb48ee587c7be367a", "2d90d5ea73a14bc082ac8827efd61c63", "d3d46634cd3946778eeb81adb590a850", "b4ddd6e13d5642578e9dbe6aab79c770", "9364e0939cb2428fb0af37355d7da7ac", "4cc0c1b3630041f9bf6fcdcbdede61d8", "172346525ad14c69a7b32de06b1bc2b7", "d357bc92c10e43199e1df2bda9ba17ef", "892de30da5b042d394b89e69e7cac358", "f77bf9e9af0c42dfa31fa2a1cb89333b", "c0eb5ed4b4fe4947a6545da64eb7eaf8", "4a3ac24b2fbf45349c1a08a167a4fc8d", "f592325e32b14c1bb2a5204e71edf494", "b32e9d6c1ce04dffac373351d4de57b0", "edd99bbc941c4619b1e507ded1e707ce", "3b3e56e3de964d87968425991733b346", "c5e85605d69b47e4bcc96f0c0924eb2b", "6e5c583ad4c440ba94fed81149b609a3", "c62a63b0d7de4770b3a4939718561508", "b345d48ce9e54247abf2491d9c740ddc", "2e7e5cac9f09431c8df4f1ddc13db7d2", "6f2c75d006af439d9997fd07b7717dcd", "956ea575313b4306bed317ed3ea345eb", "1042959a5b104ff3a845e0242f20b498", "3992e3ec773546edb97b9083630a0fe9", "d20b7d7cdb864fd384806aebc1954cc3", "13e37792812241bf9aa2e989b527860d", "65499c1d4b134432ab375971fc83921a", "72fdd0fca68f4b53a7413cf64e6a8949", "a2ac7320880b42df8f5444b035eb7929", "4f352c4601834106b7c371ff26592d49", "b804fd8561674ad8ad766e0fd98a3dc6", "4e7eb7f65fb64ea6b0016788c6dd89de" ] }, "executionInfo": { "elapsed": 4513, "status": "ok", "timestamp": 1722441641775, "user": { "displayName": "Daniel Han-Chen", "userId": "17402123517466114840" }, "user_tz": 420 }, "id": "LjY75GoYUCB8", "outputId": "e65740c0-3ab8-4c01-b308-2a37cc9062d9" }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "8b65ab1fa061457e89f295e8a81e635b", "version_major": 2, "version_minor": 0 }, "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 60 steps to speed things up, but you can set `num_train_epochs=1` for a full run, and turn off `max_steps=None`. We also support TRL's `DPOTrainer`!" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 66, "referenced_widgets": [ "92d9648dd6bb4086ad9ce52db641bf87", "ebf2fb459fd44328962a64c2e592112e", "56020c41ce384da78bd36ac4264e9d1f", "336f8c09c4be4a9d86bf47a4f8c4d5a8", "a2c07f1aacee44aa8d70e023b9966a23", "5cb2b3bbb610485ebfd2c81488d2430a", "3e71a9d8abcc47759ddddd3a2ce074eb", "edf0c0af82e7471ab2436f4ad1682205", "b881f555d1be46bd9590d264cc5aa0e5", "50602814313447959aa0596a43d5090e", "d569f15f71c24d82b457084eb0eb5c4f" ] }, "executionInfo": { "elapsed": 34129, "status": "ok", "timestamp": 1722441675895, "user": { "displayName": "Daniel Han-Chen", "userId": "17402123517466114840" }, "user_tz": 420 }, "id": "95_Nn-89DhsL", "outputId": "95dd46be-edc4-4e2c-9a72-e41719229f5d" }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "92d9648dd6bb4086ad9ce52db641bf87", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Map (num_proc=2): 0%| | 0/51760 [00:00\n", " \n", " \n", " [60/60 03:16, Epoch 0/1]\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
StepTraining Loss
11.854400
22.406500
31.755700
41.990000
51.632200
61.651400
71.220900
81.359800
91.127900
101.266700
111.028200
121.021900
130.990900
141.156600
150.968200
160.961700
171.075100
181.302900
191.028400
200.925500
210.960100
220.975800
230.900400
241.033100
251.105800
261.113500
271.090900
280.939000
290.883100
300.941700
310.914600
320.920300
331.024900
340.868300
350.964300
360.908900
370.908300
380.806800
391.139700
401.216000
410.963900
420.984100
430.945700
440.923600
450.974400
460.971000
470.925200
481.234300
490.932400
501.085700
511.062500
520.971500
531.000000
541.244900
550.856600
561.071400
570.925000
580.845200
590.895700
600.958300

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "trainer_stats = trainer.train()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "cellView": "form", "colab": { "base_uri": "https://localhost:8080/" }, "executionInfo": { "elapsed": 14, "status": "ok", "timestamp": 1722441956792, "user": { "displayName": "Daniel Han-Chen", "userId": "17402123517466114840" }, "user_tz": 420 }, "id": "pCqnaKmlO1U9", "outputId": "dcb9b366-31c1-431a-cca7-1c13df6c237d" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "278.0636 seconds used for training.\n", "4.63 minutes used for training.\n", "Peak reserved memory = 7.684 GB.\n", "Peak reserved memory for training = 4.987 GB.\n", "Peak reserved memory % of max memory = 52.102 %.\n", "Peak reserved memory for training % of max memory = 33.815 %.\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!\n", "\n", "**[NEW] Try 2x faster inference in a free Colab for Llama-3.1 8b Instruct [here](https://colab.research.google.com/drive/1T-YBVfnphoVc8E2E854qF3jdia2Ll2W2?usp=sharing)**" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "executionInfo": { "elapsed": 20334, "status": "ok", "timestamp": 1722441977117, "user": { "displayName": "Daniel Han-Chen", "userId": "17402123517466114840" }, "user_tz": 420 }, "id": "kR3gIAX-SM2q", "outputId": "7a9619f3-3018-429d-df69-3b48a9a65303" }, "outputs": [ { "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 fibonnaci sequence is a sequence of numbers where each number is the sum of the two preceding ones. The sequence is defined as follows:\\n\\n1, 1, 2, 3, 5, 8, 13, 21, 34, 55, ']" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "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": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "executionInfo": { "elapsed": 9623, "status": "ok", "timestamp": 1722441986728, "user": { "displayName": "Daniel Han-Chen", "userId": "17402123517466114840" }, "user_tz": 420 }, "id": "e2pEuRb1r2Vg", "outputId": "2a5678ac-6c40-414a-d094-974c354a46e7" }, "outputs": [ { "name": "stdout", "output_type": "stream", "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", "The fibonnaci sequence is a sequence of numbers where each number is the sum of the two preceding ones. The sequence is defined as follows:\n", "\n", "1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89, 144, 233, 377, 610, 987, 1597, 2584, 4181, 6765, 10946, 1771\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": "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": { "colab": { "base_uri": "https://localhost:8080/" }, "executionInfo": { "elapsed": 1061, "status": "ok", "timestamp": 1722441987784, "user": { "displayName": "Daniel Han-Chen", "userId": "17402123517466114840" }, "user_tz": 420 }, "id": "upcOlWe7A1vc", "outputId": "84534cad-bca3-425d-90f1-d16c0295ae18" }, "outputs": [ { "data": { "text/plain": [ "('lora_model/tokenizer_config.json',\n", " 'lora_model/special_tokens_map.json',\n", " 'lora_model/tokenizer.model',\n", " 'lora_model/added_tokens.json',\n", " 'lora_model/tokenizer.json')" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.save_pretrained(\"lora_model\") # Local saving\n", "tokenizer.save_pretrained(\"lora_model\")\n", "# model.push_to_hub(\"your_name/lora_model\", token = \"...\") # Online saving\n", "# tokenizer.push_to_hub(\"your_name/lora_model\", token = \"...\") # Online saving" ] }, { "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": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "executionInfo": { "elapsed": 6126, "status": "ok", "timestamp": 1722441993895, "user": { "displayName": "Daniel Han-Chen", "userId": "17402123517466114840" }, "user_tz": 420 }, "id": "MKX_XKs_BNZR", "outputId": "7b791800-16fb-4a7b-84ca-9c23c349b150" }, "outputs": [ { "name": "stdout", "output_type": "stream", "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 tall tower in Paris?\n", "\n", "### Input:\n", "\n", "\n", "### Response:\n", "The Eiffel Tower is a famous tall tower in Paris, France. It is located in the 5th arrondissement of Paris and is one of the most recognizable landmarks in the world. The tower was built for the 1889 World's Fair and is 324 meters tall. It is made of iron and has 1,665 steps. The tower is a symbol of Paris and is a popular tourist attraction.\n" ] } ], "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 = 128)" ] }, { "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": null, "metadata": { "id": "yFfaXG0WsQuE" }, "outputs": [], "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\")" ] }, { "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": null, "metadata": { "id": "iHjt_SMYsd3P" }, "outputs": [], "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 = \"\")" ] }, { "cell_type": "markdown", "metadata": { "id": "TCv4vXHd61i7" }, "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.\n", "\n", "[**NEW**] To finetune and auto export to Ollama, try our [Ollama notebook](https://colab.research.google.com/drive/1WZDi7APtQ9VsvOrQSSC5DDtxq159j8iZ?usp=sharing)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "FqfebeAdT073" }, "outputs": [], "source": [ "# Save to 8bit Q8_0\n", "if False: model.save_pretrained_gguf(\"model\", tokenizer,)\n", "# Remember to go to https://huggingface.co/settings/tokens for a token!\n", "# And change hf to your username!\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 = \"\")\n", "\n", "# Save to multiple GGUF options - much faster if you want multiple!\n", "if False:\n", " model.push_to_hub_gguf(\n", " \"hf/model\", # Change hf to your username!\n", " tokenizer,\n", " quantization_method = [\"q4_k_m\", \"q8_0\", \"q5_k_m\",],\n", " token = \"\", # Get a token at https://huggingface.co/settings/tokens\n", " )" ] }, { "cell_type": "markdown", "metadata": { "id": "bDp0zNpwe6U_" }, "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).\n", "\n", "**[NEW] Try 2x faster inference in a free Colab for Llama-3.1 8b Instruct [here](https://colab.research.google.com/drive/1T-YBVfnphoVc8E2E854qF3jdia2Ll2W2?usp=sharing)**" ] }, { "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. Text completions like novel writing [notebook](https://colab.research.google.com/drive/1ef-tab5bhkvWmBOObepl1WgJvfvSzn5Q?usp=sharing)\n", "9. [**NEW**] We make Phi-3 Medium / Mini **2x faster**! See our [Phi-3 Medium notebook](https://colab.research.google.com/drive/1hhdhBa1j_hsymiW9m-WzxQtgqTH_NHqi?usp=sharing)\n", "10. [**NEW**] We make Gemma-2 9b / 27b **2x faster**! See our [Gemma-2 9b notebook](https://colab.research.google.com/drive/1vIrqH5uYDQwsJ4-OO3DErvuv4pBgVwk4?usp=sharing)\n", "11. [**NEW**] To finetune and auto export to Ollama, try our [Ollama notebook](https://colab.research.google.com/drive/1WZDi7APtQ9VsvOrQSSC5DDtxq159j8iZ?usp=sharing)\n", "12. [**NEW**] We make Mistral NeMo 12B 2x faster and fit in under 12GB of VRAM! [Mistral NeMo notebook](https://colab.research.google.com/drive/17d3U-CAIwzmbDRqbZ9NnpHxCkmXB6LZ0?usp=sharing)\n", "13. [**NEW**] Llama 3.1 8b, 70b and 405b is here! We make it 2x faster and use 60% less VRAM. [Llama 3.1 8b notebook](https://colab.research.google.com/drive/1Ys44kVvmeZtnICzWz0xgpRnrIOjZAuxp?usp=sharing)\n", "\n", "

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