{ "cells": [ { "cell_type": "markdown", "source": [ "To run this, press \"Runtime\" and press \"Run all\" on a **free** Tesla T4 Google Colab instance!\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)." ], "metadata": { "id": "IqM-T1RTzY6C" } }, { "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\n", "\n", "!pip install \"git+https://github.com/huggingface/transformers.git\" # Native 4bit loading works!" ] }, { "cell_type": "markdown", "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." ], "metadata": { "id": "r2v_X2fA0Df5" } }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 544, "referenced_widgets": [ "21fd9c71893a4a99b96e79f39213af35", "99c5700859124f37be1156508a685e28", "5d9562bd69f64f2bb0f3a7b271af3d9e", "7adea9776e2348619d345699778531c4", "3f5189d724e04588b3782479297877ce", "20651e17aea349bdbc4f0d67522d4577", "04b1e656bedb4b5bb0388d7239ef01cb", "5151bb9352074dbc965d3503ed94e5c5", "4431246852364e3990f46df5186f5cc7", "f215b46454e44fb5a474c765e23888ac", "acf89e72380c4e388dc72e2684a4bea1", "27f30a48f4f04b06bbc392989c99304d", "709c7148397d4bffb855dd2cfa3ae28f", "53bd9b2299754baa89c805d52efb1229", "e742e883d9e342bfb5d855b996e20c35", "9d554c1d3d894ee8b0057d3b629a1375", "ab0c5847e0064fa8905d4cecbc3fdb24", "f41dfa9cdccd4dd193d794b314113608", "c3c30bca2f034056b19c562c13d2d624", "6113ac3a4f2d4620b47dcadd3b8ced7a", "979028afa83f4fbd88dc7af54f2ea346", "1a76dd74171e4275a858bc778cb09413", "bea3f4e007fb4f8d92766ec2455e1c17", "a2f6355eeae340b6b29ee901c1d26a42", "958fd55cc70b404da1f44177aa86f91d", "dbba49dde33745a68a1d2e3d783cfa64", "2bb22f43844c4f91ab3264a736976842", "830f4ba2895749c3859931beb0789c6f", "d23bc863c5aa4d55aa7b7ee89f9e1967", "aab98b960b784260a9fbe7722dcc48be", "eaf252cf95d148619e198128fa6176c1", "5bc33740d9dc45d9959676268cc403f1", "e84fd33e8af149199191917d41f73995", "d2945f43455245c491b3cafc6f6b463b", "b2a60ed3dc304f6193bd08e2a085510c", "93224ff8b9aa4adda5cfb72e97f31e9f", "c2dade9e7951426087f570c912765220", "f4477f83b0ad4a7e999b7673e77df34a", "1066f1cd381640f3a50a6e3f5eb69310", "8a0571e322084e559e8ea6cf2dccdec2", "8e5f466a39c240c6932b15f90d3198c2", "5dadd8af5a974cea83c078bd692570d8", "d92d6d3ec5b14c6fa29224838a41dfbf", "7fdec40cd5dd4b76b1793a1d2f1127ef", "e939fbb3ee754005af82be95a89463b8", "d3a9ec5bcdfb47cab61068f27fea8752", "c10f33f754384f0e91bf06f07966d06e", "37c9eff35f32495c826264b85dd7c62a", "cafced817e064faabc5b9ddc89d2ab09", "d52d7c537c8b4c8b99440a6e148b69c7", "79f4d6c4daeb485098f9ed2c1f11cc2a", "5a3e2433453b416e8504e09bc16f8263", "3bcd7c150f2b4afaada356efdfee3924", "3e102a5c4ed246e89bb8fbe13078d573", "499406e283f7496ba857bed6a50d4d2b", "4f7a08f57ddf4824b5e82d04ebbeb96c", "7cbb0a91cf7b4d198f1773d8c63c2788", "4f3254087468482fac59300a6b5bb9e9", "268fb5e00f5a47189bc8210079c81587", "5caa3a3253c14b12a86b49b6551ea092", "4f94c5fc491c446490a58228479ca9fa", "7f21b2a6e4824a4eadf60f6a29b08c9a", "fe705ea4feb4453286e85c12d7d6b94c", "51a5e3f6174643899c76a0e956c44557", "e114ba4cc5c9487287c5d86f7d1bf286", "ba7c9655843b44a0a8837dcf71053a70", "b24b629c56774be2a728f3c7f18f4360", "69c702c247bb42799203eccb9f26d8bf", "32ff881c6b0a4f088023cc6f7947c7bd", "cc73abb46ce6419aac339959b74a1b2e", "fcf0b55acc4d4baea800e43bc24e4542", "8d12fc38cec64f8eb727d15d96de6fe8", "82d1f7fdf00d40c7adea470e917ceff0", "4f97a49627a645d2a6a55496bc6c7072", "18d3cc93c137498bbc21b0c70953da88", "b4aa410b9bb54acdbde194f186aecc34", "d1be9d94bcab40cab73ef3cc1b371c1f" ] }, "id": "QmUBVEnvCDJv", "outputId": "c8350926-5143-40ce-fb35-2bffbe499cac" }, "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, ?B/s]" ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "21fd9c71893a4a99b96e79f39213af35" } }, "metadata": {} }, { "output_type": "stream", "name": "stderr", "text": [ "==((====))== Unsloth: Fast Mistral patching release 2024.1\n", " \\\\ /| GPU: Tesla T4. Max memory: 14.748 GB\n", "O^O/ \\_/ \\ CUDA capability = 7.5. Xformers = 0.0.22.post7. FA = False.\n", "\\ / Pytorch version: 2.1.0+cu121. CUDA Toolkit = 12.1\n", " \"-____-\" bfloat16 = FALSE. Platform = Linux\n", "\n", "You passed `quantization_config` to `from_pretrained` but the model you're loading already has a `quantization_config` attribute. The `quantization_config` attribute will be overwritten with the one you passed to `from_pretrained`.\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "model.safetensors: 0%| | 0.00/4.13G [00:00, ?B/s]" ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "27f30a48f4f04b06bbc392989c99304d" } }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "generation_config.json: 0%| | 0.00/116 [00:00, ?B/s]" ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "bea3f4e007fb4f8d92766ec2455e1c17" } }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "tokenizer_config.json: 0%| | 0.00/971 [00:00, ?B/s]" ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "d2945f43455245c491b3cafc6f6b463b" } }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "tokenizer.model: 0%| | 0.00/493k [00:00, ?B/s]" ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "e939fbb3ee754005af82be95a89463b8" } }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "tokenizer.json: 0%| | 0.00/1.80M [00:00, ?B/s]" ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "4f7a08f57ddf4824b5e82d04ebbeb96c" } }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "special_tokens_map.json: 0%| | 0.00/438 [00:00, ?B/s]" ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "b24b629c56774be2a728f3c7f18f4360" } }, "metadata": {} } ], "source": [ "from unsloth import FastLanguageModel\n", "import torch\n", "max_seq_length = 2048 # Choose any! We auto support RoPE Scaling internally!\n", "dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+\n", "load_in_4bit = True # Use 4bit quantization to reduce memory usage. Can be False.\n", "\n", "# 4bit pre quantized models we support for 4x faster downloading!\n", "fourbit_models = [\n", " \"unsloth/mistral-7b-bnb-4bit\",\n", " \"unsloth/llama-2-7b-bnb-4bit\",\n", " \"unsloth/llama-2-13b-bnb-4bit\",\n", " \"unsloth/codellama-34b-bnb-4bit\",\n", " \"unsloth/tinyllama-bnb-4bit\",\n", "]\n", "\n", "model, tokenizer = FastLanguageModel.from_pretrained(\n", " model_name = \"unsloth/mistral-7b-bnb-4bit\", # \"unsloth/mistral-7b\" for 16bit loading\n", " max_seq_length = max_seq_length,\n", " dtype = dtype,\n", " load_in_4bit = load_in_4bit,\n", " # token = \"hf_...\", # use one if using gated models like meta-llama/Llama-2-7b-hf\n", ")" ] }, { "cell_type": "markdown", "source": [ "We now add LoRA adapters so we only need to update 1 to 10% of all parameters!" ], "metadata": { "id": "SXd9bTZd1aaL" } }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "6bZsfBuZDeCL", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "ac6dcf4f-ecfb-41f2-8bcf-c63bc171453b" }, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "Unsloth 2024.1 patched 32 layers with 32 QKV layers, 32 O layers and 32 MLP layers.\n" ] } ], "source": [ "model = FastLanguageModel.get_peft_model(\n", " model,\n", " r = 16, # Choose any number > 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", "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)." ], "metadata": { "id": "vITh0KVJ10qX" } }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "LjY75GoYUCB8", "colab": { "base_uri": "https://localhost:8080/", "height": 145, "referenced_widgets": [ "a8a8adf4394f4ea4be00c9853c2a3ee5", "aca3ffe3a48741418e6ad9832b4c3834", "581c364e9f8c44648d9e8808e1923028", "7c7a322585654ac6a7bde6488caa2b30", "60468642068c4c1f930517ec6c9dd399", "9bc911e88d9240b79aa19c5351664dc9", "d78e367940154413a7310304db0a7d89", "3f614e47d3164d4fbc5235c44ab14680", "e0625e2e1e98435c8bb5d2f6838396f4", "4890d11383634ed7b88fd29abc79bceb", "5202446fa7c341d4af217e46b7be79e4", "4eac852431744e40b7dcf9ecca1e1fd1", "a066366a6c7a4771a6d5e718fd92f88f", "70a536f16d6c431b98b7ef12801d33a2", "ff6f563aef26414f829868f1ba6f29c7", "5242f675a9d940eb83c5dc6ce08417dd", "a576ce5ff14a4f1a8a78a4fad7caf2ed", "aa5794124ec74a29bce5663bbae3b4ec", "beaa198d254e4521b795d4cff4b4df6e", "f7146f6127a2489cb4c02b714d67f783", "0b8cff5afa7242c1b817df3fd509cd04", "423df2448502498a85107a594562cea9", "c57f151c22ca4bb49c25613b06036b00", "19efa2caad3e477185a566bad2d2d6d5", "3f6f62974c1a4163ad2dc437aa705e7f", "706f7cda0ebd47fb88fea4bc75db6761", "15aeeaa33efd4933b52e234a8ce9b98f", "97dfc903d38944458f094f5d7a79e4ee", "ce44aae35b1642bda5d76a9ab5b635f9", "e6de9852ddb9426ea43edaeb79569b00", "96fda888005f4934ac13e68182bf71cf", "5ff8951b0da7438097cc58b509a97f4a", "35e25cff723e4dcfab3eeb891699d67f", "cc4186d840ae4644a45d330480b15322", "abb7a90a330e42b491fe4a1ca6b10bfc", "ca7a0ff127f84bedaad3009d6c9fa0fb", "7d0a83dbc8774bd0832aced31e6c8901", "3066b61c1e9d4c4ba8cbab9cd106e8c9", "146cbaafb0114bce94b306cd96b958e8", "f55c54affd70418b8c3a5a22603d4320", "c61e4cb0a3994bab91dd20bc02ed18ad", "f8233de76001468bbf94151f069c2503", "86643f0ef91740db9b4bffe394f709d4", "1744a4f1b0164c10a54a9b912b6149cc" ] }, "cellView": "form", "outputId": "72d478d8-c49d-4bbb-ef19-caae4d0f3895" }, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "Downloading readme: 0%| | 0.00/11.6k [00:00, ?B/s]" ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "a8a8adf4394f4ea4be00c9853c2a3ee5" } }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "Downloading data: 0%| | 0.00/44.3M [00:00, ?B/s]" ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "4eac852431744e40b7dcf9ecca1e1fd1" } }, "metadata": {} }, { "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": "c57f151c22ca4bb49c25613b06036b00" } }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "Map: 0%| | 0/51760 [00:00, ? examples/s]" ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "cc4186d840ae4644a45d330480b15322" } }, "metadata": {} } ], "source": [ "#@title Alpaca dataset preparation code\n", "alpaca_prompt = \"\"\"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", "{}\n", "\n", "### Input:\n", "{}\n", "\n", "### Response:\n", "{}\"\"\"\n", "\n", "def formatting_prompts_func(examples):\n", " instructions = examples[\"instruction\"]\n", " inputs = examples[\"input\"]\n", " outputs = examples[\"output\"]\n", " texts = []\n", " for instruction, input, output in zip(instructions, inputs, outputs):\n", " text = alpaca_prompt.format(instruction, input, output)\n", " texts.append(text)\n", " return { \"text\" : texts, }\n", "pass\n", "\n", "from datasets import load_dataset\n", "dataset = load_dataset(\"yahma/alpaca-cleaned\", split = \"train\")\n", "dataset = dataset.map(formatting_prompts_func, batched = True,)" ] }, { "cell_type": "markdown", "source": [ "\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`!" ], "metadata": { "id": "idAEIeSQ3xdS" } }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "95_Nn-89DhsL", "colab": { "base_uri": "https://localhost:8080/", "height": 177, "referenced_widgets": [ "15a93404b92749c883a8d12457f26c8b", "fb8c3976bab449378f593c98fbb39ee2", "a6a8d886daee4efca0367499a4017bf2", "10e360552c7449bd883a92609d7fa0c6", "4877845709dd408892e890dfb960cc43", "ad01c612601d4397baa49462ea9a072e", "aa3b6b80b097473abf5a668122c6fe53", "f32eaa45bf984a8297966aa51cfeecaa", "6085d90e846a4f35aab61d8b69e792c1", "42a87fc42c434a29bf3b4fefaa7303b9", "5ec7290c9829407eb9118e7e80b361ab", "2e59b8ebd1d746a6a3fc712792b113a9", "581aa9bfab964c34a321614bd6fe5f24", "cc56c401fdb24eac97eac7a2ebb02941", "12773f1437de4ad59231d2fc8f3e07c0", "dbcee7f6de434667877d8a77122f2eec", "6efad27478a848a79741a57f42b69569", "521938814b4b4ee8a294313e9ecce8cf", "d90435a8d6b344f39e4334791015b7fa", "a66f92cd6a8446d985eccfc0c4f755a1", "8a691a6099e3475e91d7eac4c05adcea", "4ae0fa029fe9450994b3ff0c067748dd", "6dd689dc12d04fba8398b026658710ab", "e9151dd6e8214242b2160e6669411605", "9aae260518204f4cbb2bb311ca63465a", "97cb0c3e4b714794b31c9f632395585f", "41454da47d524b238d57707d0adec739", "f642257584994fc8912725796fe8fb05", "e02eb8727d92467bb059c8d2167b9ab5", "7d44a58f494b454d9252275f02dc82bc", "d2edb8443f76499f9b77650905060ec1", "6279562bf9e545bdbe69d7138a14ce96", "e7ed16effd4e4ebab99eff7ed31f345e", "24ba4d106a9441d99564c4686d271e08", "3d339c599ea94d3687731f7028e30217", "accc31937ca341c4814552a6718bf1e0", "ff0284561a9147929a844112d99eb3ed", "c6af98fea280457e92e73be95b5283d2", "7f2c7b33282d409ea560de7d1a707ad3", "ea40866a478d430880435a9d52543b88", "6b84ee03c9504488a9c20a41097d18be", "fe3f069852624450a384348a611141f2", "95d18e87dac747da80b248f8a3d1ce64", "6ee77e3cb845462d9fcae86d55277fad", "900a9076b00d4bcebf40c97eeec7ebe9", "0c9c1f29c76b48f0b5db845c93835d69", "bbc7cbdac1b246b4ab777becfc19aae8", "114373f9b133498da03f0f73dcc2a066", "3b766d8d98bb43ceb844f571d41a2ddb", "e937e9cbd01f4287befd13aeaade6712", "1a033034e75f421f9e546beec5b4d77c", "814be9f3d9444d96b4c5e51472fd4fb8", "51efbd9d8a0d45b9b53cb4cf467954ac", "6bf5fa33ab64465b9fe087c22719bdc6", "d24cc25d7973414890b15e262f29e418" ] }, "outputId": "41800a9c-69d0-4e0e-d3e0-48f3598f0071" }, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "tokenizer_config.json: 0%| | 0.00/971 [00:00, ?B/s]" ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "15a93404b92749c883a8d12457f26c8b" } }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "tokenizer.model: 0%| | 0.00/493k [00:00, ?B/s]" ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "2e59b8ebd1d746a6a3fc712792b113a9" } }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "tokenizer.json: 0%| | 0.00/1.80M [00:00, ?B/s]" ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "6dd689dc12d04fba8398b026658710ab" } }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "special_tokens_map.json: 0%| | 0.00/438 [00:00, ?B/s]" ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "24ba4d106a9441d99564c4686d271e08" } }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "Map (num_proc=4): 0%| | 0/51760 [00:00, ? examples/s]" ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "900a9076b00d4bcebf40c97eeec7ebe9" } }, "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", " max_seq_length = max_seq_length,\n", " dataset_num_proc = 4,\n", " args = TrainingArguments(\n", " per_device_train_batch_size = 2,\n", " gradient_accumulation_steps = 4,\n", " warmup_steps = 5,\n", " max_steps = 60,\n", " learning_rate = 2e-4,\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 = 0.01,\n", " lr_scheduler_type = \"linear\",\n", " seed = 3407,\n", " output_dir = \"outputs\",\n", " ),\n", ")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "2ejIt2xSNKKp", "colab": { "base_uri": "https://localhost:8080/" }, "cellView": "form", "outputId": "a4f3a340-d70e-495b-a175-390ac434ca91" }, "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": "8d77683b-0314-410a-e455-90fa2e2a094c" }, "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": [ "Step | \n", "Training Loss | \n", "
---|---|
1 | \n", "1.404300 | \n", "
2 | \n", "1.703200 | \n", "
3 | \n", "1.146300 | \n", "
4 | \n", "1.176100 | \n", "
5 | \n", "0.986200 | \n", "
6 | \n", "1.002200 | \n", "
7 | \n", "1.033700 | \n", "
8 | \n", "0.888100 | \n", "
9 | \n", "0.942000 | \n", "
10 | \n", "0.934500 | \n", "
11 | \n", "0.897300 | \n", "
12 | \n", "0.932300 | \n", "
13 | \n", "0.819300 | \n", "
14 | \n", "0.985400 | \n", "
15 | \n", "0.821500 | \n", "
16 | \n", "0.880600 | \n", "
17 | \n", "1.007600 | \n", "
18 | \n", "0.720200 | \n", "
19 | \n", "0.588100 | \n", "
20 | \n", "0.726600 | \n", "
21 | \n", "0.887900 | \n", "
22 | \n", "0.922300 | \n", "
23 | \n", "0.750000 | \n", "
24 | \n", "0.746500 | \n", "
25 | \n", "0.894900 | \n", "
26 | \n", "0.739500 | \n", "
27 | \n", "0.863900 | \n", "
28 | \n", "0.863300 | \n", "
29 | \n", "0.784500 | \n", "
30 | \n", "0.747500 | \n", "
31 | \n", "0.793200 | \n", "
32 | \n", "0.749500 | \n", "
33 | \n", "0.856100 | \n", "
34 | \n", "0.856500 | \n", "
35 | \n", "0.749200 | \n", "
36 | \n", "0.754000 | \n", "
37 | \n", "0.761600 | \n", "
38 | \n", "0.802100 | \n", "
39 | \n", "0.889300 | \n", "
40 | \n", "0.929700 | \n", "
41 | \n", "0.734500 | \n", "
42 | \n", "0.596400 | \n", "
43 | \n", "0.696700 | \n", "
44 | \n", "0.665600 | \n", "
45 | \n", "0.964900 | \n", "
46 | \n", "0.574200 | \n", "
47 | \n", "0.749800 | \n", "
48 | \n", "0.758600 | \n", "
49 | \n", "0.713500 | \n", "
50 | \n", "0.957900 | \n", "
51 | \n", "0.828400 | \n", "
52 | \n", "0.712900 | \n", "
53 | \n", "0.837600 | \n", "
54 | \n", "0.924600 | \n", "
55 | \n", "0.717400 | \n", "
56 | \n", "0.951900 | \n", "
57 | \n", "0.744900 | \n", "
58 | \n", "0.889500 | \n", "
59 | \n", "0.772800 | \n", "
60 | \n", "0.881800 | \n", "
"
]
},
"metadata": {}
}
],
"source": [
"trainer_stats = trainer.train()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "pCqnaKmlO1U9",
"cellView": "form",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "8164562f-9c1d-4d1c-a596-ebdfb95001a1"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"480.792 seconds used for training.\n",
"8.01 minutes used for training.\n",
"Peak reserved memory = 6.826 GB.\n",
"Peak reserved memory for training = 2.201 GB.\n",
"Peak reserved memory % of max memory = 46.284 %.\n",
"Peak reserved memory for training % of max memory = 14.924 %.\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",
"source": [
"\n",
"### Inference\n",
"Let's run the model! You can change the instruction and input - leave the output blank!"
],
"metadata": {
"id": "ekOmTR1hSNcr"
}
},
{
"cell_type": "code",
"source": [
"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",
"]*1, return_tensors = \"pt\").to(\"cuda\")\n",
"\n",
"outputs = model.generate(**inputs, max_new_tokens = 128, use_cache = True)\n",
"tokenizer.batch_decode(outputs)"
],
"metadata": {
"id": "kR3gIAX-SM2q",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "a14cd344-4807-4389-f357-bbd5a6661d67"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py:1547: UserWarning: You have modified the pretrained model configuration to control generation. This is a deprecated strategy to control generation and will be removed soon, in a future version. Please use and modify the model generation configuration (see https://huggingface.co/docs/transformers/generation_strategies#default-text-generation-configuration )\n",
" warnings.warn(\n"
]
},
{
"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 next number in the Fibonacci sequence is 13. The Fibonacci sequence is a series of numbers where each number is the sum of the two numbers before it. The sequence starts with 0 and 1, and each subsequent number is the sum of the two numbers before it. The sequence continues as follows: 0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89, 144, 233, 377, 61']"
]
},
"metadata": {},
"execution_count": 9
}
]
},
{
"cell_type": "markdown",
"source": [
"\n",
"### Saving, loading finetuned models\n",
"To save the final model, either use Huggingface's `push_to_hub` for an online save or `save_pretrained` for a local save."
],
"metadata": {
"id": "uMuVrWbjAzhc"
}
},
{
"cell_type": "code",
"source": [
"model.save_pretrained(\"lora_model\") # Local saving\n",
"# model.push_to_hub(\"your_name/lora_model\") # Online saving"
],
"metadata": {
"id": "upcOlWe7A1vc"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"Now if you want to load the LoRA adapters we just saved to use for inference, UNCOMMENT the commented parts, and copy paste the below to a new instance.\n",
"\n",
"**[HELP]** If `unsloth/mistral-7b-bnb-4bit` errors out, try `unsloth/mistral-7b`"
],
"metadata": {
"id": "AEEcJ4qfC7Lp"
}
},
{
"cell_type": "code",
"source": [
"from peft import PeftModel\n",
"\n",
"### **** UNCOMMENT BELOW FOR INFERENCE **** ###\n",
"# from unsloth import FastLanguageModel\n",
"# model, tokenizer = FastLanguageModel.from_pretrained(\n",
"# model_name = \"unsloth/mistral-7b\" # 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",
"# model = PeftModel.from_pretrained(model, \"lora_model\")\n",
"### **** UNCOMMENT ABOVE FOR INFERENCE **** ###\n",
"\n",
"alpaca_prompt = \"\"\"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",
"{}\n",
"\n",
"### Input:\n",
"{}\n",
"\n",
"### Response:\n",
"{}\"\"\"\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",
"]*1, return_tensors = \"pt\").to(\"cuda\")\n",
"\n",
"outputs = model.generate(**inputs, max_new_tokens = 128, use_cache = True)\n",
"tokenizer.batch_decode(outputs)"
],
"metadata": {
"id": "MKX_XKs_BNZR",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "9a6df427-0e12-45fe-9467-78e2095346ce"
},
"execution_count": null,
"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:\\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, France. It is an iron lattice tower located on the Champ de Mars and is one of the most recognizable structures in the world. It was built in 1889 as the entrance arch to the 1889 World's Fair and was designed by Gustave Eiffel. The tower stands at a height of 324 meters (1,063 feet) and is the tallest structure in Paris. It is a popular tourist attraction and is one of the most visited monuments in the world.\"]"
]
},
"metadata": {},
"execution_count": 11
}
]
},
{
"cell_type": "code",
"source": [
"#@title Code for conversion to GGUF\n",
"def colab_quantize_to_gguf(save_directory, quantization_method = \"q4_k_m\"):\n",
" from transformers.models.llama.modeling_llama import logger\n",
" import os\n",
"\n",
" logger.warning_once(\n",
" \"Unsloth: `colab_quantize_to_gguf` is still in development mode.\\n\"\\\n",
" \"If anything errors or breaks, please file a ticket on Github.\\n\"\\\n",
" \"Also, if you used this successfully, please tell us on Discord!\"\n",
" )\n",
"\n",
" # From https://mlabonne.github.io/blog/posts/Quantize_Llama_2_models_using_ggml.html\n",
" ALLOWED_QUANTS = \\\n",
" {\n",
" \"q2_k\" : \"Uses Q4_K for the attention.vw and feed_forward.w2 tensors, Q2_K for the other tensors.\",\n",
" \"q3_k_l\" : \"Uses Q5_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else Q3_K\",\n",
" \"q3_k_m\" : \"Uses Q4_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else Q3_K\",\n",
" \"q3_k_s\" : \"Uses Q3_K for all tensors\",\n",
" \"q4_0\" : \"Original quant method, 4-bit.\",\n",
" \"q4_1\" : \"Higher accuracy than q4_0 but not as high as q5_0. However has quicker inference than q5 models.\",\n",
" \"q4_k_m\" : \"Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q4_K\",\n",
" \"q4_k_s\" : \"Uses Q4_K for all tensors\",\n",
" \"q5_0\" : \"Higher accuracy, higher resource usage and slower inference.\",\n",
" \"q5_1\" : \"Even higher accuracy, resource usage and slower inference.\",\n",
" \"q5_k_m\" : \"Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q5_K\",\n",
" \"q5_k_s\" : \"Uses Q5_K for all tensors\",\n",
" \"q6_k\" : \"Uses Q8_K for all tensors\",\n",
" \"q8_0\" : \"Almost indistinguishable from float16. High resource use and slow. Not recommended for most users.\",\n",
" }\n",
"\n",
" if quantization_method not in ALLOWED_QUANTS.keys():\n",
" error = f\"Unsloth: Quant method = [{quantization_method}] not supported. Choose from below:\\n\"\n",
" for key, value in ALLOWED_QUANTS.items():\n",
" error += f\"[{key}] => {value}\\n\"\n",
" raise RuntimeError(error)\n",
" pass\n",
"\n",
" print_info = \\\n",
" f\"==((====))== Unsloth: Conversion from QLoRA to GGUF information\\n\"\\\n",
" f\" \\\\\\ /| [0] Installing llama.cpp will take 3 minutes.\\n\"\\\n",
" f\"O^O/ \\_/ \\\\ [1] Converting HF to GUUF 16bits will take 3 minutes.\\n\"\\\n",
" f\"\\ / [2] Converting GGUF 16bits to q4_k_m will take 20 minutes.\\n\"\\\n",
" f' \"-____-\" In total, you will have to wait around 26 minutes.\\n'\n",
" print(print_info)\n",
"\n",
" if not os.path.exists(\"llama.cpp\"):\n",
" print(\"Unsloth: [0] Installing llama.cpp. This will take 3 minutes...\")\n",
" !git clone https://github.com/ggerganov/llama.cpp\n",
" !cd llama.cpp && make clean && LLAMA_CUBLAS=1 make -j\n",
" !pip install gguf protobuf\n",
" pass\n",
"\n",
" print(\"Unsloth: [1] Converting HF into GGUF 16bit. This will take 3 minutes...\")\n",
" !python llama.cpp/convert.py {save_directory} \\\n",
" --outfile {save_directory}-unsloth.gguf \\\n",
" --outtype f16\n",
"\n",
" print(\"Unsloth: [2] Converting GGUF 16bit into q4_k_m. This will take 20 minutes...\")\n",
" final_location = f\"./{save_directory}-{quantization_method}-unsloth.gguf\"\n",
" !./llama.cpp/quantize ./{save_directory}-unsloth.gguf \\\n",
" {final_location} {quantization_method}\n",
"\n",
" print(f\"Unsloth: Output location: {final_location}\")\n",
"pass\n"
],
"metadata": {
"cellView": "form",
"id": "nCVtR2ElF1GX"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"To save to `GGUF` / `llama.cpp`, we support it natively now! You can also go to our dedicated GGUF notebook [here](https://colab.research.google.com/drive/14DW0VwuqL2O3tqGlX7aUF6TOBA8S59M4?usp=sharing). Select either `save locally` for local saving or `save locally and quantize to 4bit` for 4bit quantization for llama.cpp / GGUF."
],
"metadata": {
"id": "TCv4vXHd61i7"
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"cell_type": "code",
"source": [
"from unsloth import unsloth_save_model\n",
"\n",
"# Change to `save locally` to save a float16 GGUF file or `\"save locally and quantize to 4bit\"`\n",
"# to quantize down to 4bit\n",
"SAVE_STRATEGY = \"none\"\n",
"\n",
"if SAVE_STRATEGY == \"save locally\":\n",
"\n",
" unsloth_save_model(model, tokenizer, \"output_model\")\n",
"\n",
"elif SAVE_STRATEGY == \"save locally and quantize to 4bit\":\n",
"\n",
" unsloth_save_model(model, tokenizer, \"output_model\")\n",
" colab_quantize_to_gguf(\"output_model\", quantization_method = \"q4_k_m\")"
],
"metadata": {
"id": "FqfebeAdT073"
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"source": [
"Now, use the `output_model.gguf` file or `output_model-q4_k_m-unsloth.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": {
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},
{
"cell_type": "markdown",
"source": [
"Otherwise, to merge the LoRA adapters into the 4bit model:"
],
"metadata": {
"id": "acUVCgzzU1Wv"
}
},
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"cell_type": "code",
"source": [
"model = model.merge_and_unload()"
],
"metadata": {
"id": "xcRjsZe0RK1b",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "896496df-5d9a-49e7-f60b-ce2304f70c39"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.10/dist-packages/peft/tuners/lora/bnb.py:229: UserWarning: Merge lora module to 4-bit linear may get different generations due to rounding errors.\n",
" warnings.warn(\n"
]
}
]
},
{
"cell_type": "markdown",
"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. Llama 7b [free Kaggle](https://www.kaggle.com/danielhanchen/unsloth-alpaca-t4-ddp)\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",
"\n",
""
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