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--- a +++ b/Code/Drug Discovery/Meta-Llama-3/Llama3-ChatQA-1.5-8B-Molecule.ipynb @@ -0,0 +1,11602 @@ +{ + "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", + "<div class=\"align-center\">\n", + " <a href=\"https://github.com/unslothai/unsloth\"><img src=\"https://github.com/unslothai/unsloth/raw/main/images/unsloth%20new%20logo.png\" width=\"115\"></a>\n", + " <a href=\"https://discord.gg/u54VK8m8tk\"><img src=\"https://github.com/unslothai/unsloth/raw/main/images/Discord button.png\" width=\"145\"></a>\n", + " <a href=\"https://ko-fi.com/unsloth\"><img src=\"https://github.com/unslothai/unsloth/raw/main/images/Kofi button.png\" width=\"145\"></a></a> Join Discord if you need help + support us if you can!\n", + "</div>\n", + "\n", + "To install Unsloth on your own computer, follow the installation instructions on our Github page [here](https://github.com/unslothai/unsloth#installation-instructions---conda).\n", + "\n", + "You will learn how to do [data prep](#Data), how to [train](#Train), how to [run the model](#Inference), & [how to save it](#Save) (eg for Llama.cpp).\n", + "\n", + "**[NEW] Llama-3 8b is trained on a crazy 15 trillion tokens! Llama-2 was 2 trillion.**" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "id": "2eSvM9zX_2d3" + }, + "outputs": [], + "source": [ + "%%capture\n", + "import torch\n", + "major_version, minor_version = torch.cuda.get_device_capability()\n", + "# Must install separately since Colab has torch 2.2.1, which breaks packages\n", + "!pip install \"unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git\"\n", + "if major_version >= 8:\n", + " # Use this for new GPUs like Ampere, Hopper GPUs (RTX 30xx, RTX 40xx, A100, H100, L40)\n", + " !pip install --no-deps packaging ninja einops flash-attn xformers trl peft accelerate bitsandbytes\n", + "else:\n", + " # Use this for older GPUs (V100, Tesla T4, RTX 20xx)\n", + " !pip install --no-deps xformers trl peft accelerate bitsandbytes\n", + "pass\n", + "# Llama 3 Video Tutorial https://www.youtube.com/watch?v=aQmoog_s8HE" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "r2v_X2fA0Df5" + }, + "source": [ + "* We support Llama, Mistral, CodeLlama, TinyLlama, Vicuna, Open Hermes etc\n", + "* And Yi, Qwen ([llamafied](https://huggingface.co/models?sort=trending&search=qwen+llama)), Deepseek, all Llama, Mistral derived archs.\n", + "* We support 16bit LoRA or 4bit QLoRA. Both 2x faster.\n", + "* `max_seq_length` can be set to anything, since we do automatic RoPE Scaling via [kaiokendev's](https://kaiokendev.github.io/til) method.\n", + "* [**NEW**] With [PR 26037](https://github.com/huggingface/transformers/pull/26037), we support downloading 4bit models **4x faster**! [Our repo](https://huggingface.co/unsloth) has Llama, Mistral 4bit models." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 722, + "referenced_widgets": [ + "22bca2c1eb4344c481c1484b5384e2e6", + "510efdc7fb7c4bafaf530c48fc9afacf", + "194f28c2940740549ea950c25355a3d8", + "8c175ebcc7a44cc9a4791fa4a603292f", + "adce60be91ac45ad9dba298ea7bf743a", + "875b3111543f42e2acd2d53c5fade3e7", + "10f3b08a36dd428fa20b92fd886df986", + "c4d5baf5b2a14bc587fb733f53825075", + "eb7d8b409339415d923e9e4f4b5e8ad0", + "ad0526a85a9341db8d953fe7c5ba0d48", + "bacbb9c419cb40f3a95761f08ca2929b", + "db654afc855642cca1f3c09c18aa7d32", + "937d41ebab0a451c9a492706f88c3ad7", + "289d19b3a5a5403b96071a7e108c2244", + "6f069a2f39014df599834d72e35c4f9c", + "b5c131a0fd1649d789a79cd141383cf7", + "63464fbe13954a1293fc97ba87722284", + "c36b52781fc44fc1a5ca2eca5dd1d2fa", + "6ac9cb0d5fdc4695a7fe3b5eb363e75b", + 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Memory-efficient attention, SwiGLU, sparse and more won't be available.\n", + " Set XFORMERS_MORE_DETAILS=1 for more details\n", + "/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n", + " warnings.warn(\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "config.json: 0%| | 0.00/653 [00:00<?, ?B/s]" + ], + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "22bca2c1eb4344c481c1484b5384e2e6" + } + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "==((====))== Unsloth: Fast Llama patching release 2024.4\n", + " \\\\ /| GPU: NVIDIA A100-SXM4-40GB. Max memory: 39.564 GB. Platform = Linux.\n", + "O^O/ \\_/ \\ Pytorch: 2.2.1+cu121. CUDA = 8.0. CUDA Toolkit = 12.1.\n", + "\\ / Bfloat16 = TRUE. Xformers = 0.0.26.post1. FA = True.\n", + " \"-____-\" Free Apache license: http://github.com/unslothai/unsloth\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "pytorch_model.bin.index.json: 0%| | 0.00/26.8k [00:00<?, ?B/s]" + ], + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "db654afc855642cca1f3c09c18aa7d32" + } + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Downloading shards: 0%| | 0/2 [00:00<?, ?it/s]" + ], + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "57b35f62df934db6956c94d6c7724c04" + } + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "pytorch_model-00001-of-00002.bin: 0%| | 0.00/9.98G [00:00<?, ?B/s]" + ], + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "22e27a82d89d4e1cbbba42d3f8a6afc5" + } + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "pytorch_model-00002-of-00002.bin: 0%| | 0.00/6.08G [00:00<?, ?B/s]" + ], + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "47f987571dd049019f71fbe5fde7859c" + } + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Loading checkpoint shards: 0%| | 0/2 [00:00<?, ?it/s]" + ], + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "c63cfc18b2aa4a079d65058188ff8804" + } + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "Some weights of the model checkpoint at nvidia/Llama3-ChatQA-1.5-8B were not used when initializing LlamaForCausalLM: ['model.layers.0.self_attn.rotary_emb.inv_freq', 'model.layers.1.self_attn.rotary_emb.inv_freq', 'model.layers.10.self_attn.rotary_emb.inv_freq', 'model.layers.11.self_attn.rotary_emb.inv_freq', 'model.layers.12.self_attn.rotary_emb.inv_freq', 'model.layers.13.self_attn.rotary_emb.inv_freq', 'model.layers.14.self_attn.rotary_emb.inv_freq', 'model.layers.15.self_attn.rotary_emb.inv_freq', 'model.layers.16.self_attn.rotary_emb.inv_freq', 'model.layers.17.self_attn.rotary_emb.inv_freq', 'model.layers.18.self_attn.rotary_emb.inv_freq', 'model.layers.19.self_attn.rotary_emb.inv_freq', 'model.layers.2.self_attn.rotary_emb.inv_freq', 'model.layers.20.self_attn.rotary_emb.inv_freq', 'model.layers.21.self_attn.rotary_emb.inv_freq', 'model.layers.22.self_attn.rotary_emb.inv_freq', 'model.layers.23.self_attn.rotary_emb.inv_freq', 'model.layers.24.self_attn.rotary_emb.inv_freq', 'model.layers.25.self_attn.rotary_emb.inv_freq', 'model.layers.26.self_attn.rotary_emb.inv_freq', 'model.layers.27.self_attn.rotary_emb.inv_freq', 'model.layers.28.self_attn.rotary_emb.inv_freq', 'model.layers.29.self_attn.rotary_emb.inv_freq', 'model.layers.3.self_attn.rotary_emb.inv_freq', 'model.layers.30.self_attn.rotary_emb.inv_freq', 'model.layers.31.self_attn.rotary_emb.inv_freq', 'model.layers.4.self_attn.rotary_emb.inv_freq', 'model.layers.5.self_attn.rotary_emb.inv_freq', 'model.layers.6.self_attn.rotary_emb.inv_freq', 'model.layers.7.self_attn.rotary_emb.inv_freq', 'model.layers.8.self_attn.rotary_emb.inv_freq', 'model.layers.9.self_attn.rotary_emb.inv_freq']\n", + "- This IS expected if you are initializing LlamaForCausalLM from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n", + "- This IS NOT expected if you are initializing LlamaForCausalLM from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "generation_config.json: 0%| | 0.00/136 [00:00<?, ?B/s]" + ], + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "da7fe9529f62409392a6cad8f466dcc5" + } + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "tokenizer_config.json: 0%| | 0.00/50.6k [00:00<?, ?B/s]" + ], + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "469dd26de0984c3e91c709086d18acbd" + } + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "tokenizer.json: 0%| | 0.00/9.08M [00:00<?, ?B/s]" + ], + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "3482cfc1285b4f44932ec673ff9af0e3" + } + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "special_tokens_map.json: 0%| | 0.00/73.0 [00:00<?, ?B/s]" + ], + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "f848b76491f74645a6340fe025a5d10b" + } + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n", + "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n", + "nvidia/Llama3-ChatQA-1.5-8B does not have a padding or unknown token!\n", + "Will use the EOS token of id 128001 as padding.\n" + ] + } + ], + "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 = False # Use 4bit quantization to reduce memory usage. Can be False.\n", + "\n", + "# 4bit pre quantized models we support for 4x faster downloading + no OOMs.\n", + "# fourbit_models = [\n", + "# \"unsloth/mistral-7b-bnb-4bit\",\n", + "# \"unsloth/mistral-7b-instruct-v0.2-bnb-4bit\",\n", + "# \"unsloth/llama-2-7b-bnb-4bit\",\n", + "# \"unsloth/gemma-7b-bnb-4bit\",\n", + "# \"unsloth/gemma-7b-it-bnb-4bit\", # Instruct version of Gemma 7b\n", + "# \"unsloth/gemma-2b-bnb-4bit\",\n", + "# \"unsloth/gemma-2b-it-bnb-4bit\", # Instruct version of Gemma 2b\n", + "# \"unsloth/llama-3-8b-bnb-4bit\", # [NEW] 15 Trillion token Llama-3\n", + "# ] # More models at https://huggingface.co/unsloth\n", + "\n", + "model, tokenizer = FastLanguageModel.from_pretrained(\n", + " model_name = \"nvidia/Llama3-ChatQA-1.5-8B\",\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", + "metadata": { + "id": "SXd9bTZd1aaL" + }, + "source": [ + "We now add LoRA adapters so we only need to update 1 to 10% of all parameters!" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 0 + }, + "id": "6bZsfBuZDeCL", + "outputId": "d2edbf2e-51e9-4a0f-ba12-bd4beb9a3cc7" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "Unsloth 2024.4 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 = 1, # 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 = 5,\n", + " lora_dropout = 0, # Supports any, but = 0 is optimized\n", + " bias = \"none\", # Supports any, but = \"none\" is optimized\n", + " # [NEW] \"unsloth\" uses 30% less VRAM, fits 2x larger batch sizes!\n", + " use_gradient_checkpointing = \"unsloth\", # True or \"unsloth\" for very long context\n", + " random_state = 3407,\n", + " use_rslora = False, # We support rank stabilized LoRA\n", + " loftq_config = None, # And LoftQ\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "vITh0KVJ10qX" + }, + "source": [ + "<a name=\"Data\"></a>\n", + "### Data Prep\n", + "We now use the Alpaca dataset from [yahma](https://huggingface.co/datasets/yahma/alpaca-cleaned), which is a filtered version of 52K of the original [Alpaca dataset](https://crfm.stanford.edu/2023/03/13/alpaca.html). You can replace this code section with your own data prep.\n", + "\n", + "**[NOTE]** To train only on completions (ignoring the user's input) read TRL's docs [here](https://huggingface.co/docs/trl/sft_trainer#train-on-completions-only).\n", + "\n", + "**[NOTE]** Remember to add the **EOS_TOKEN** to the tokenized output!! Otherwise you'll get infinite generations!\n", + "\n", + "If you want to use the `ChatML` template for ShareGPT datasets, try our conversational [notebook](https://colab.research.google.com/drive/1Aau3lgPzeZKQ-98h69CCu1UJcvIBLmy2?usp=sharing).\n", + "\n", + "For text completions like novel writing, try this [notebook](https://colab.research.google.com/drive/1ef-tab5bhkvWmBOObepl1WgJvfvSzn5Q?usp=sharing)." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 430, + "referenced_widgets": [ + "b105d9a47d8f4a3eac7fecf1c665d4b5", + "2c0d19594b86410b8b9029ccd0e41af5", + "3f9742b0839745e78afc6030c2701cfa", + "8203bd5087744a13b20f7d8e2e4bd578", + "3c1d71963d464c328a6d3eb01cc09bf6", + "736ae61ad72d4345aa55303e2747155a", + "91359be229b54499bb62ad409e53f5ee", + "d89edbdc23c249b48838d760ec07117f", + "386fb68b1666467e9a0a7129b1475fe0", + "e2acc34bca0d4005905991dd92bd0aa5", + "d4d9baf159f8448d9965bcbd75271c79", 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"d824897ff8f74aaa884596c8174a1756", + "7b92f347275341489c56341ef78c56cb", + "34425335e1b74511a641c015a2fad7c2", + "a3077d41ac19471ea9d0b6af09fa2c9e", + "043daebf95ad4640a24ed23514c838e3", + "dd96430fe9f748f687139280a907e442", + "651ef0d5e5674d85b4f0de807c8affc5", + "8ce5582e02a448c291f89af29d8c4a00", + "8d420cc80a1a4970929f2ccec7b2212b", + "e2ca881292d84c83b800ddaec8b4a233", + "20479addb05049cb9ec3cc09b9ffc5bd", + "81566ad19f44421c8784be2020db4a8d", + "150221a9fab0461898606a6d3fb97081", + "ec57d8d9c3cf4b91a0d8693dd423423c", + "c7f1a82b18164c8f8ce6e78301ecbb99", + "9465525901cc4ff3b42b792bf37d7c84", + "aa9ac1ccef8c41ea90fd6ca19427dcbb", + "a34cba0368654faaaaa6886a7518e6eb", + "8cf08dd07d2b4d568884dd1e08bac7a8", + "8b938897731b4f02803e1bca3ad16d50", + "6df896308363458e830a1b9434453d71", + "5df61481a3464b1caa587dcde4a55a80", + "f6ba6f7b9a574e25a840c403b210fbbb", + "c2928437c9c347ff87668b955b8e43b9", + "2deef94ebda54d218832a21a0ce3cd5d", + "3d75e56b5d50423abab814331ecbe2b4", + "442baac72f59487197fe6683863835f6", + "3f3869d0cbd4451f87334c1b70a95807", + "38a47921d71240e1a6addca03062a8e5", + "1999f8cf964e4f66be0d2fc75ca6e9b9", + "b6ca16c4cdf5474f9ed349475cc58401", + "a53171262b074466bad4aba2570e0cbe", + "82f0980daf854a3da515133c102f27a8", + "2f5113f0272443a7bcd92093dd4b6df4", + "1e120b561dbe4a24961f3c93ee4510bc", + "9b93636309b5493e96efe83bc199bdae", + "2fe6511972fd41a9bf25438f87768c35", + "952a23eaf1f14647abaef0cdcec43208", + "ecc0a9ba4e084bb39878dfd01ebffe04", + "c84ae8f22cb048c58e5759cbdadb056e", + "4c59c980819c43ad8ce5126c6bf89861", + "ce73f49829634ec0bb3ba62e2596f20e", + "38f4eee38875461d81ec32d69ed94591", + "fa25940ac1634981aff6c55ca058d2a6", + "6f7a322b9d7d43628e9d99024d0c05dc", + "8ed23e52a4eb4f479e4449e1085fb5d4", + "ea830f9da75d4cdb8b809ada11b7be2f", + "c77944dd4a5149be8256d7feb3168af9", + "c2c195f916bd48c88d80719c9e122a7d", + "d1168112b41e4970bbbdf385b83c0b09", + "b8818949ecd140c38f7044f53aebfe0f", + "deffdaa547ba467fa6d8d34b961c8b49", + "93292fdfae3f42b7b72e3832aabd82ad", + "8df12e80ee5b428aad1aab7a036ad2a2", + "dbd580f35f2c44c1a20b3382a8eac7db", + "b49703c1e3524f5586b04c8d92c1594c", + "14bc2c5c055a4482beeb237b77643d2e", + "80b23c3b072441fab2589c6b72cc1272", + "6cf87424db7f42e1ade5277148a2836b", + "953eadc3db484a7ea087c3807d809f3e", + "f9c7b1f1492b42a2988f6322ed36ad48", + "ec05ed0497c64986b6430c741c716d3a", + "e9a0884be95a4eecab0bbf32194c700a", + "8980c3be0a90414387580227f634eef0", + "524bb1af27774da9ace0b93c23c06f07", + "4d5ac61e269e472ab0da165e0f7feea6", + "399d8c354bf44526919f7a71b1cc7682", + "c7cca53162a64cf8a85667691cc0cc3a", + "7d0611eb65dd47abb9ab6f66eab1d348", + "4e841a10da3b4059b44dd5f2eb025ed8", + "aaca0caf8a604c5381c34771f5a640d2", + "29021fbf1e8d4e7d80ee2f9f6786d267", + "1dc64ba8770843d9b60efdc982b669d4" + ] + }, + "id": "LjY75GoYUCB8", + "outputId": "7d101216-7bbc-4ab5-8972-d0fb01952dfa" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.10/dist-packages/datasets/load.py:1486: FutureWarning: The repository for zjunlp/Mol-Instructions contains custom code which must be executed to correctly load the dataset. You can inspect the repository content at https://hf.co/datasets/zjunlp/Mol-Instructions\n", + "You can avoid this message in future by passing the argument `trust_remote_code=True`.\n", + "Passing `trust_remote_code=True` will be mandatory to load this dataset from the next major release of `datasets`.\n", + " warnings.warn(\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Downloading builder script: 0%| | 0.00/7.34k [00:00<?, ?B/s]" + ], + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "b105d9a47d8f4a3eac7fecf1c665d4b5" + } + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Downloading readme: 0%| | 0.00/19.6k [00:00<?, ?B/s]" + ], + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "c23c9d7d2bfe4d989fceffb916714958" + } + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Downloading data: 0%| | 0.00/73.2M [00:00<?, ?B/s]" + ], + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "7a52fbf65f644175a023804a785241fb" + } + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Generating description_guided_molecule_design split: 0 examples [00:00, ? examples/s]" + ], + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "08f9470fddff4832bdb56e2229838109" + } + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Generating forward_reaction_prediction split: 0 examples [00:00, ? examples/s]" + ], + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "8ce5582e02a448c291f89af29d8c4a00" + } + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Generating molecular_description_generation split: 0 examples [00:00, ? examples/s]" + ], + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "8cf08dd07d2b4d568884dd1e08bac7a8" + } + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Generating property_prediction split: 0 examples [00:00, ? examples/s]" + ], + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "1999f8cf964e4f66be0d2fc75ca6e9b9" + } + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Generating reagent_prediction split: 0 examples [00:00, ? examples/s]" + ], + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "4c59c980819c43ad8ce5126c6bf89861" + } + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Generating retrosynthesis split: 0 examples [00:00, ? examples/s]" + ], + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "deffdaa547ba467fa6d8d34b961c8b49" + } + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Map: 0%| | 0/298319 [00:00<?, ? examples/s]" + ], + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "e9a0884be95a4eecab0bbf32194c700a" + } + }, + "metadata": {} + } + ], + "source": [ + "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", + "EOS_TOKEN = tokenizer.eos_token # Must add EOS_TOKEN\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", + " # Must add EOS_TOKEN, otherwise your generation will go on forever!\n", + " text = alpaca_prompt.format(instruction, input, output) + EOS_TOKEN\n", + " texts.append(text)\n", + " return { \"text\" : texts, }\n", + "pass\n", + "\n", + "from datasets import load_dataset\n", + "dataset = load_dataset(\"zjunlp/Mol-Instructions\", \"Molecule-oriented Instructions\", split=\"description_guided_molecule_design\")\n", + "dataset = dataset.map(formatting_prompts_func, batched = True,)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "idAEIeSQ3xdS" + }, + "source": [ + "<a name=\"Train\"></a>\n", + "### Train the model\n", + "Now let's use Huggingface TRL's `SFTTrainer`! More docs here: [TRL SFT docs](https://huggingface.co/docs/trl/sft_trainer). We do 60 steps to speed things up, but you can set `num_train_epochs=1` for a full run, and turn off `max_steps=None`. We also support TRL's `DPOTrainer`!" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 124, + "referenced_widgets": [ + "029f0dd1adb34818b63e424447df1b38", + "d08aa7985a0f4d46860e62681a214ec0", + "2fdcccbe8b8c407ba184a2f0aa07aea6", + "5fab9560c1c84bd2859b1b522ecaefdf", + "f9022d9358e74f8d9a2605f62af77a3b", + "67f380cf428f490fb1e16cf67c9740de", + "9e58e2fc16d045b8b549c0b722e5e50c", + "15ce21547fbc4242b5258c545b48bf66", + "145b97aa126c44c0af5516467356d189", + "542d72befb354e83b9a1b43ac0170bb7", + "a58d73c5335d443281bf2b7d471d5962" + ] + }, + "id": "95_Nn-89DhsL", + "outputId": "0f0b7c0a-1186-4c8a-a072-b41a2e3195ad" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.10/dist-packages/multiprocess/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n", + " self.pid = os.fork()\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Map (num_proc=2): 0%| | 0/298319 [00:00<?, ? examples/s]" + ], + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "029f0dd1adb34818b63e424447df1b38" + } + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "max_steps is given, it will override any value given in num_train_epochs\n" + ] + } + ], + "source": [ + "from trl import SFTTrainer\n", + "from transformers import TrainingArguments\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 = False, # Can make training 5x faster for short sequences.\n", + " args = TrainingArguments(\n", + " per_device_train_batch_size = 2,\n", + " gradient_accumulation_steps = 4,\n", + " warmup_steps = 5,\n", + " max_steps = 400,\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": 6, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 0 + }, + "id": "2ejIt2xSNKKp", + "outputId": "761772b4-b4d4-4ff9-eeae-6636c265faf2" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "GPU = NVIDIA A100-SXM4-40GB. Max memory = 39.564 GB.\n", + "15.094 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": 7, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 12705 + }, + "id": "yqxqAZ7KJ4oL", + "outputId": "409adcd1-41ac-4bf8-ba74-4b467393288f" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "==((====))== Unsloth - 2x faster free finetuning | Num GPUs = 1\n", + " \\\\ /| Num examples = 298,319 | Num Epochs = 1\n", + "O^O/ \\_/ \\ Batch size per device = 2 | Gradient Accumulation steps = 4\n", + "\\ / Total batch size = 8 | Total steps = 400\n", + " \"-____-\" Number of trainable parameters = 2,621,440\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "<IPython.core.display.HTML object>" + ], + "text/html": [ + "\n", + " <div>\n", + " \n", + " <progress value='400' max='400' style='width:300px; height:20px; vertical-align: middle;'></progress>\n", + " [400/400 09:51, Epoch 0/1]\n", + " </div>\n", + " <table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: left;\">\n", + " <th>Step</th>\n", + " <th>Training Loss</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <td>1</td>\n", + " <td>1.918900</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2</td>\n", + " <td>2.120800</td>\n", + " </tr>\n", + " <tr>\n", + " <td>3</td>\n", + " <td>2.188200</td>\n", + " </tr>\n", + " <tr>\n", + " <td>4</td>\n", + " <td>1.793900</td>\n", + " </tr>\n", + " <tr>\n", + " <td>5</td>\n", + " <td>1.848100</td>\n", + " </tr>\n", + " <tr>\n", + " <td>6</td>\n", + " <td>2.075200</td>\n", + " </tr>\n", + " <tr>\n", + " <td>7</td>\n", + " <td>1.634900</td>\n", + " </tr>\n", + " <tr>\n", + " <td>8</td>\n", + " <td>1.435100</td>\n", + " </tr>\n", + " <tr>\n", + " <td>9</td>\n", + " <td>1.367400</td>\n", + " </tr>\n", + " <tr>\n", + " <td>10</td>\n", + " <td>1.102000</td>\n", + " </tr>\n", + " <tr>\n", + " <td>11</td>\n", + " <td>1.105100</td>\n", + " </tr>\n", + " <tr>\n", + " <td>12</td>\n", + " <td>1.231900</td>\n", + " </tr>\n", + " <tr>\n", + " <td>13</td>\n", + " <td>1.020600</td>\n", + " </tr>\n", + " <tr>\n", + " <td>14</td>\n", + " <td>1.040000</td>\n", + " </tr>\n", + " <tr>\n", + " <td>15</td>\n", + " <td>0.928400</td>\n", + " </tr>\n", + " <tr>\n", + " <td>16</td>\n", + " <td>0.844400</td>\n", + " </tr>\n", + " <tr>\n", + " <td>17</td>\n", + " <td>0.914900</td>\n", + " </tr>\n", + " <tr>\n", + " <td>18</td>\n", + " <td>0.779800</td>\n", + " </tr>\n", + " <tr>\n", + " <td>19</td>\n", + " <td>0.826100</td>\n", + " </tr>\n", + " <tr>\n", + " <td>20</td>\n", + " <td>0.772200</td>\n", + " </tr>\n", + " <tr>\n", + " <td>21</td>\n", + " <td>0.736600</td>\n", + " </tr>\n", + " <tr>\n", + " <td>22</td>\n", + " <td>0.751400</td>\n", + " </tr>\n", + " <tr>\n", + " <td>23</td>\n", + " <td>0.764700</td>\n", + " </tr>\n", + " <tr>\n", + " <td>24</td>\n", + " <td>0.787400</td>\n", + " </tr>\n", + " <tr>\n", + " <td>25</td>\n", + " <td>0.602000</td>\n", + " </tr>\n", + " <tr>\n", + " <td>26</td>\n", + " <td>0.762100</td>\n", + " </tr>\n", + " <tr>\n", + " <td>27</td>\n", + " <td>0.621400</td>\n", + " </tr>\n", + " <tr>\n", + " <td>28</td>\n", + " <td>0.657300</td>\n", + " </tr>\n", + " <tr>\n", + " <td>29</td>\n", + " <td>0.766200</td>\n", + " </tr>\n", + " <tr>\n", + " <td>30</td>\n", + " <td>0.671700</td>\n", + " </tr>\n", + " <tr>\n", + " <td>31</td>\n", + " <td>0.645300</td>\n", + " </tr>\n", + " <tr>\n", + " <td>32</td>\n", + " <td>0.688300</td>\n", + " </tr>\n", + " <tr>\n", + " <td>33</td>\n", + " <td>0.668700</td>\n", + " </tr>\n", + " <tr>\n", + " <td>34</td>\n", + " <td>0.648200</td>\n", + " </tr>\n", + " <tr>\n", + " <td>35</td>\n", + " <td>0.612400</td>\n", + " </tr>\n", + " <tr>\n", + " <td>36</td>\n", + " <td>0.667900</td>\n", + " </tr>\n", + " <tr>\n", + " <td>37</td>\n", + " <td>0.618100</td>\n", + " </tr>\n", + " <tr>\n", + " <td>38</td>\n", + " <td>0.545500</td>\n", + " </tr>\n", + " <tr>\n", + " <td>39</td>\n", + " <td>0.704400</td>\n", + " </tr>\n", + " <tr>\n", + " <td>40</td>\n", + " <td>0.586800</td>\n", + " </tr>\n", + " <tr>\n", + " <td>41</td>\n", + " <td>0.566000</td>\n", + " </tr>\n", + " <tr>\n", + " <td>42</td>\n", + " <td>0.657400</td>\n", + " </tr>\n", + " <tr>\n", + " <td>43</td>\n", + " <td>0.549900</td>\n", + " </tr>\n", + " <tr>\n", + " <td>44</td>\n", + " <td>0.651200</td>\n", + " </tr>\n", + " <tr>\n", + " <td>45</td>\n", + " <td>0.727400</td>\n", + " </tr>\n", + " <tr>\n", + " <td>46</td>\n", + " <td>0.597600</td>\n", + " </tr>\n", + " <tr>\n", + " <td>47</td>\n", + " <td>0.584600</td>\n", + " </tr>\n", + " <tr>\n", + " <td>48</td>\n", + " <td>0.633200</td>\n", + " </tr>\n", + " <tr>\n", + " <td>49</td>\n", + " <td>0.563600</td>\n", + " </tr>\n", + " <tr>\n", + " <td>50</td>\n", + " <td>0.590400</td>\n", + " </tr>\n", + " <tr>\n", + " <td>51</td>\n", + " <td>0.578100</td>\n", + " </tr>\n", + " <tr>\n", + " <td>52</td>\n", + " <td>0.561200</td>\n", + " </tr>\n", + " <tr>\n", + " <td>53</td>\n", + " <td>0.615500</td>\n", + " </tr>\n", + " <tr>\n", + " <td>54</td>\n", + " <td>0.585000</td>\n", + " </tr>\n", + " <tr>\n", + " <td>55</td>\n", + " <td>0.561400</td>\n", + " </tr>\n", + " <tr>\n", + " <td>56</td>\n", + " <td>0.579200</td>\n", + " </tr>\n", + " <tr>\n", + " <td>57</td>\n", + " <td>0.482300</td>\n", + " </tr>\n", + " <tr>\n", + " <td>58</td>\n", + " <td>0.509400</td>\n", + " </tr>\n", + " <tr>\n", + " <td>59</td>\n", + " <td>0.573300</td>\n", + " </tr>\n", + " <tr>\n", + " <td>60</td>\n", + " <td>0.681300</td>\n", + " </tr>\n", + " <tr>\n", + " <td>61</td>\n", + " <td>0.562300</td>\n", + " </tr>\n", + " <tr>\n", + " <td>62</td>\n", + " <td>0.597300</td>\n", + " </tr>\n", + " <tr>\n", + " <td>63</td>\n", + " <td>0.569700</td>\n", + " </tr>\n", + " <tr>\n", + " <td>64</td>\n", + " <td>0.592500</td>\n", + " </tr>\n", + " <tr>\n", + " <td>65</td>\n", + " <td>0.529600</td>\n", + " </tr>\n", + " <tr>\n", + " <td>66</td>\n", + " <td>0.504800</td>\n", + " </tr>\n", + " <tr>\n", + " <td>67</td>\n", + " <td>0.728700</td>\n", + " </tr>\n", + " <tr>\n", + " <td>68</td>\n", + " <td>0.548500</td>\n", + " </tr>\n", + " <tr>\n", + " <td>69</td>\n", + " <td>0.534400</td>\n", + " </tr>\n", + " <tr>\n", + " <td>70</td>\n", + " <td>0.520600</td>\n", + " </tr>\n", + " <tr>\n", + " <td>71</td>\n", + " <td>0.669100</td>\n", + " </tr>\n", + " <tr>\n", + " <td>72</td>\n", + " <td>0.754600</td>\n", + " </tr>\n", + " <tr>\n", + " <td>73</td>\n", + " <td>0.508100</td>\n", + " </tr>\n", + " <tr>\n", + " <td>74</td>\n", + " <td>0.501500</td>\n", + " </tr>\n", + " <tr>\n", + " <td>75</td>\n", + " <td>0.457200</td>\n", + " </tr>\n", + " <tr>\n", + " <td>76</td>\n", + " <td>0.651800</td>\n", + " </tr>\n", + " <tr>\n", + " <td>77</td>\n", + " <td>0.517400</td>\n", + " </tr>\n", + " <tr>\n", + " <td>78</td>\n", + " <td>0.535800</td>\n", + " </tr>\n", + " <tr>\n", + " <td>79</td>\n", + " <td>0.478900</td>\n", + " </tr>\n", + " <tr>\n", + " <td>80</td>\n", + " <td>0.456900</td>\n", + " </tr>\n", + " <tr>\n", + " <td>81</td>\n", + " <td>0.502300</td>\n", + " </tr>\n", + " <tr>\n", + " <td>82</td>\n", + " <td>0.530400</td>\n", + " </tr>\n", + " <tr>\n", + " <td>83</td>\n", + " <td>0.499100</td>\n", + " </tr>\n", + " <tr>\n", + " <td>84</td>\n", + " <td>0.519200</td>\n", + " </tr>\n", + " <tr>\n", + " <td>85</td>\n", + " <td>0.538000</td>\n", + " </tr>\n", + " <tr>\n", + " <td>86</td>\n", + " <td>0.523400</td>\n", + " </tr>\n", + " <tr>\n", + " <td>87</td>\n", + " <td>0.533300</td>\n", + " </tr>\n", + " <tr>\n", + " <td>88</td>\n", + " <td>0.462400</td>\n", + " </tr>\n", + " <tr>\n", + " <td>89</td>\n", + " <td>0.562200</td>\n", + " </tr>\n", + " <tr>\n", + " <td>90</td>\n", + " <td>0.489400</td>\n", + " </tr>\n", + " <tr>\n", + " <td>91</td>\n", + " <td>0.449000</td>\n", + " </tr>\n", + " <tr>\n", + " <td>92</td>\n", + " <td>0.476800</td>\n", + " </tr>\n", + " <tr>\n", + " <td>93</td>\n", + " <td>0.518600</td>\n", + " </tr>\n", + " <tr>\n", + " <td>94</td>\n", + " <td>0.579000</td>\n", + " </tr>\n", + " <tr>\n", + " <td>95</td>\n", + " <td>0.531300</td>\n", + " </tr>\n", + " <tr>\n", + " <td>96</td>\n", + " <td>0.496000</td>\n", + " </tr>\n", + " <tr>\n", + " <td>97</td>\n", + " <td>0.454200</td>\n", + " </tr>\n", + " <tr>\n", + " <td>98</td>\n", + " <td>0.518200</td>\n", + " </tr>\n", + " <tr>\n", + " <td>99</td>\n", + " <td>0.514500</td>\n", + " </tr>\n", + " <tr>\n", + " <td>100</td>\n", + " <td>0.518000</td>\n", + " </tr>\n", + " <tr>\n", + " <td>101</td>\n", + " <td>0.557100</td>\n", + " </tr>\n", + " <tr>\n", + " <td>102</td>\n", + " <td>0.522600</td>\n", + " </tr>\n", + " <tr>\n", + " <td>103</td>\n", + " <td>0.472400</td>\n", + " </tr>\n", + " <tr>\n", + " <td>104</td>\n", + " <td>0.528700</td>\n", + " </tr>\n", + " <tr>\n", + " <td>105</td>\n", + " <td>0.612100</td>\n", + " </tr>\n", + " <tr>\n", + " <td>106</td>\n", + " <td>0.462500</td>\n", + " </tr>\n", + " <tr>\n", + " <td>107</td>\n", + " <td>0.541200</td>\n", + " </tr>\n", + " <tr>\n", + " <td>108</td>\n", + " <td>0.455400</td>\n", + " </tr>\n", + " <tr>\n", + " <td>109</td>\n", + " <td>0.432200</td>\n", + " </tr>\n", + " <tr>\n", + " <td>110</td>\n", + " <td>0.526200</td>\n", + " </tr>\n", + " <tr>\n", + " <td>111</td>\n", + " <td>0.608200</td>\n", + " </tr>\n", + " <tr>\n", + " <td>112</td>\n", + " <td>0.442100</td>\n", + " </tr>\n", + " <tr>\n", + " <td>113</td>\n", + " <td>0.610400</td>\n", + " </tr>\n", + " <tr>\n", + " <td>114</td>\n", + " <td>0.512500</td>\n", + " </tr>\n", + " <tr>\n", + " <td>115</td>\n", + " <td>0.500300</td>\n", + " </tr>\n", + " <tr>\n", + " <td>116</td>\n", + " <td>0.505600</td>\n", + " </tr>\n", + " <tr>\n", + " <td>117</td>\n", + " <td>0.487000</td>\n", + " </tr>\n", + " <tr>\n", + " <td>118</td>\n", + " <td>0.482300</td>\n", + " </tr>\n", + " <tr>\n", + " <td>119</td>\n", + " <td>0.459900</td>\n", + " </tr>\n", + " <tr>\n", + " <td>120</td>\n", + " <td>0.525800</td>\n", + " </tr>\n", + " <tr>\n", + " <td>121</td>\n", + " <td>0.593400</td>\n", + " </tr>\n", + " <tr>\n", + " <td>122</td>\n", + " <td>0.469700</td>\n", + " </tr>\n", + " <tr>\n", + " <td>123</td>\n", + " <td>0.479000</td>\n", + " </tr>\n", + " <tr>\n", + " <td>124</td>\n", + " <td>0.452100</td>\n", + " </tr>\n", + " <tr>\n", + " <td>125</td>\n", + " <td>0.575000</td>\n", + " </tr>\n", + " <tr>\n", + " <td>126</td>\n", + " <td>0.528000</td>\n", + " </tr>\n", + " <tr>\n", + " <td>127</td>\n", + " <td>0.557400</td>\n", + " </tr>\n", + " <tr>\n", + " <td>128</td>\n", + " <td>0.470000</td>\n", + " </tr>\n", + " <tr>\n", + " <td>129</td>\n", + " <td>0.470200</td>\n", + " </tr>\n", + " <tr>\n", + " <td>130</td>\n", + " <td>0.486000</td>\n", + " </tr>\n", + " <tr>\n", + " <td>131</td>\n", + " <td>0.548000</td>\n", + " </tr>\n", + " <tr>\n", + " <td>132</td>\n", + " <td>0.549300</td>\n", + " </tr>\n", + " <tr>\n", + " <td>133</td>\n", + " <td>0.690200</td>\n", + " </tr>\n", + " <tr>\n", + " <td>134</td>\n", + " <td>0.696200</td>\n", + " </tr>\n", + " <tr>\n", + " <td>135</td>\n", + " <td>0.458100</td>\n", + " </tr>\n", + " <tr>\n", + " <td>136</td>\n", + " <td>0.518900</td>\n", + " </tr>\n", + " <tr>\n", + " <td>137</td>\n", + " <td>0.504000</td>\n", + " </tr>\n", + " <tr>\n", + " <td>138</td>\n", + " <td>0.457900</td>\n", + " </tr>\n", + " <tr>\n", + " <td>139</td>\n", + " <td>0.505700</td>\n", + " </tr>\n", + " <tr>\n", + " <td>140</td>\n", + " <td>0.433700</td>\n", + " </tr>\n", + " <tr>\n", + " <td>141</td>\n", + " <td>0.420200</td>\n", + " </tr>\n", + " <tr>\n", + " <td>142</td>\n", + " <td>0.432100</td>\n", + " </tr>\n", + " <tr>\n", + " <td>143</td>\n", + " <td>0.561700</td>\n", + " </tr>\n", + " <tr>\n", + " <td>144</td>\n", + " <td>0.445600</td>\n", + " </tr>\n", + " <tr>\n", + " <td>145</td>\n", + " <td>0.573900</td>\n", + " </tr>\n", + " <tr>\n", + " <td>146</td>\n", + " <td>0.403500</td>\n", + " </tr>\n", + " <tr>\n", + " <td>147</td>\n", + " <td>0.449000</td>\n", + " </tr>\n", + " <tr>\n", + " <td>148</td>\n", + " <td>0.445500</td>\n", + " </tr>\n", + " <tr>\n", + " <td>149</td>\n", + " <td>0.449600</td>\n", + " </tr>\n", + " <tr>\n", + " <td>150</td>\n", + " <td>0.450900</td>\n", + " </tr>\n", + " <tr>\n", + " <td>151</td>\n", + " <td>0.455100</td>\n", + " </tr>\n", + " <tr>\n", + " <td>152</td>\n", + " <td>0.467200</td>\n", + " </tr>\n", + " <tr>\n", + " <td>153</td>\n", + " <td>0.395900</td>\n", + " </tr>\n", + " <tr>\n", + " <td>154</td>\n", + " <td>0.430000</td>\n", + " </tr>\n", + " <tr>\n", + " <td>155</td>\n", + " <td>0.512500</td>\n", + " </tr>\n", + " <tr>\n", + " <td>156</td>\n", + " <td>0.471900</td>\n", + " </tr>\n", + " <tr>\n", + " <td>157</td>\n", + " <td>0.497100</td>\n", + " </tr>\n", + " <tr>\n", + " <td>158</td>\n", + " <td>0.457900</td>\n", + " </tr>\n", + " <tr>\n", + " <td>159</td>\n", + " <td>0.527100</td>\n", + " </tr>\n", + " <tr>\n", + " <td>160</td>\n", + " <td>0.498100</td>\n", + " </tr>\n", + " <tr>\n", + " <td>161</td>\n", + " <td>0.421300</td>\n", + " </tr>\n", + " <tr>\n", + " <td>162</td>\n", + " <td>0.515900</td>\n", + " </tr>\n", + " <tr>\n", + " <td>163</td>\n", + " <td>0.358900</td>\n", + " </tr>\n", + " <tr>\n", + " <td>164</td>\n", + " <td>0.476900</td>\n", + " </tr>\n", + " <tr>\n", + " <td>165</td>\n", + " <td>0.417100</td>\n", + " </tr>\n", + " <tr>\n", + " <td>166</td>\n", + " <td>0.367700</td>\n", + " </tr>\n", + " <tr>\n", + " <td>167</td>\n", + " <td>0.478800</td>\n", + " </tr>\n", + " <tr>\n", + " <td>168</td>\n", + " <td>0.533100</td>\n", + " </tr>\n", + " <tr>\n", + " <td>169</td>\n", + " <td>0.425000</td>\n", + " </tr>\n", + " <tr>\n", + " <td>170</td>\n", + " <td>0.514400</td>\n", + " </tr>\n", + " <tr>\n", + " <td>171</td>\n", + " <td>0.484500</td>\n", + " </tr>\n", + " <tr>\n", + " <td>172</td>\n", + " <td>0.411500</td>\n", + " </tr>\n", + " <tr>\n", + " <td>173</td>\n", + " <td>0.555200</td>\n", + " </tr>\n", + 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<td>0.396200</td>\n", + " </tr>\n", + " <tr>\n", + " <td>350</td>\n", + " <td>0.404900</td>\n", + " </tr>\n", + " <tr>\n", + " <td>351</td>\n", + " <td>0.426700</td>\n", + " </tr>\n", + " <tr>\n", + " <td>352</td>\n", + " <td>0.388400</td>\n", + " </tr>\n", + " <tr>\n", + " <td>353</td>\n", + " <td>0.375600</td>\n", + " </tr>\n", + " <tr>\n", + " <td>354</td>\n", + " <td>0.614800</td>\n", + " </tr>\n", + " <tr>\n", + " <td>355</td>\n", + " <td>0.364300</td>\n", + " </tr>\n", + " <tr>\n", + " <td>356</td>\n", + " <td>0.448900</td>\n", + " </tr>\n", + " <tr>\n", + " <td>357</td>\n", + " <td>0.331600</td>\n", + " </tr>\n", + " <tr>\n", + " <td>358</td>\n", + " <td>0.486800</td>\n", + " </tr>\n", + " <tr>\n", + " <td>359</td>\n", + " <td>0.392400</td>\n", + " </tr>\n", + " <tr>\n", + " <td>360</td>\n", + " <td>0.437800</td>\n", + " </tr>\n", + " <tr>\n", + " <td>361</td>\n", + " <td>0.417800</td>\n", + " </tr>\n", + " <tr>\n", + " <td>362</td>\n", + " <td>0.432200</td>\n", + " </tr>\n", + " <tr>\n", + " <td>363</td>\n", + " <td>0.389800</td>\n", + " </tr>\n", + " <tr>\n", + " <td>364</td>\n", + " <td>0.472800</td>\n", + " </tr>\n", + " <tr>\n", + " <td>365</td>\n", + " <td>0.492300</td>\n", + " </tr>\n", + " <tr>\n", + " <td>366</td>\n", + " <td>0.527900</td>\n", + " </tr>\n", + " <tr>\n", + " <td>367</td>\n", + " <td>0.358100</td>\n", + " </tr>\n", + " <tr>\n", + " <td>368</td>\n", + " <td>0.364300</td>\n", + " </tr>\n", + " <tr>\n", + " <td>369</td>\n", + " <td>0.336100</td>\n", + " </tr>\n", + " <tr>\n", + " <td>370</td>\n", + " <td>0.464100</td>\n", + " </tr>\n", + " <tr>\n", + " <td>371</td>\n", + " <td>0.453000</td>\n", + " </tr>\n", + " <tr>\n", + " <td>372</td>\n", + " <td>0.364300</td>\n", + " </tr>\n", + " <tr>\n", + " <td>373</td>\n", + " <td>0.408700</td>\n", + " </tr>\n", + " <tr>\n", + " <td>374</td>\n", + " <td>0.445600</td>\n", + " </tr>\n", + " <tr>\n", + " <td>375</td>\n", + " <td>0.412200</td>\n", + " </tr>\n", + " <tr>\n", + " <td>376</td>\n", + " <td>0.384500</td>\n", + " </tr>\n", + " <tr>\n", + " <td>377</td>\n", + " <td>0.441600</td>\n", + " </tr>\n", + " <tr>\n", + " <td>378</td>\n", + " <td>0.423900</td>\n", + " </tr>\n", + " <tr>\n", + " <td>379</td>\n", + " <td>0.414100</td>\n", + " </tr>\n", + " <tr>\n", + " <td>380</td>\n", + " <td>0.350200</td>\n", + " </tr>\n", + " <tr>\n", + " <td>381</td>\n", + " <td>0.502700</td>\n", + " </tr>\n", + " <tr>\n", + " <td>382</td>\n", + " <td>0.450800</td>\n", + " </tr>\n", + " <tr>\n", + " <td>383</td>\n", + " <td>0.416900</td>\n", + " </tr>\n", + " <tr>\n", + " <td>384</td>\n", + " <td>0.363200</td>\n", + " </tr>\n", + " <tr>\n", + " <td>385</td>\n", + " <td>0.374200</td>\n", + " </tr>\n", + " <tr>\n", + " <td>386</td>\n", + " <td>0.391100</td>\n", + " </tr>\n", + " <tr>\n", + " <td>387</td>\n", + " <td>0.407100</td>\n", + " </tr>\n", + " <tr>\n", + " <td>388</td>\n", + " <td>0.346900</td>\n", + " </tr>\n", + " <tr>\n", + " <td>389</td>\n", + " <td>0.463500</td>\n", + " </tr>\n", + " <tr>\n", + " <td>390</td>\n", + " <td>0.385500</td>\n", + " </tr>\n", + " <tr>\n", + " <td>391</td>\n", + " <td>0.390500</td>\n", + " </tr>\n", + " <tr>\n", + " <td>392</td>\n", + " <td>0.370400</td>\n", + " </tr>\n", + " <tr>\n", + " <td>393</td>\n", + " <td>0.399200</td>\n", + " </tr>\n", + " <tr>\n", + " <td>394</td>\n", + " <td>0.370000</td>\n", + " </tr>\n", + " <tr>\n", + " <td>395</td>\n", + " <td>0.373900</td>\n", + " </tr>\n", + " <tr>\n", + " <td>396</td>\n", + " <td>0.346100</td>\n", + " </tr>\n", + " <tr>\n", + " <td>397</td>\n", + " <td>0.498200</td>\n", + " </tr>\n", + " <tr>\n", + " <td>398</td>\n", + " <td>0.448800</td>\n", + " </tr>\n", + " <tr>\n", + " <td>399</td>\n", + " <td>0.489500</td>\n", + " </tr>\n", + " <tr>\n", + " <td>400</td>\n", + " <td>0.418300</td>\n", + " </tr>\n", + " </tbody>\n", + "</table><p>" + ] + }, + "metadata": {} + } + ], + "source": [ + "trainer_stats = trainer.train()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 0 + }, + "id": "pCqnaKmlO1U9", + "outputId": "8658ee6b-c390-4ac7-b384-ad0a710e1e62" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "599.4502 seconds used for training.\n", + "9.99 minutes used for training.\n", + "Peak reserved memory = 20.223 GB.\n", + "Peak reserved memory for training = 5.129 GB.\n", + "Peak reserved memory % of max memory = 51.115 %.\n", + "Peak reserved memory for training % of max memory = 12.964 %.\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": [ + "<a name=\"Inference\"></a>\n", + "### Inference\n", + "Let's run the model! You can change the instruction and input - leave the output blank!" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 0 + }, + "id": "kR3gIAX-SM2q", + "outputId": "e2d4ceb3-6026-4061-e8a5-1cc14bb72bc3" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "['Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\\n\\n### Instruction:\\nContinue the fibonnaci sequence.\\n\\n### Input:\\n1, 1, 2, 3, 5, 8\\n\\n### Response:\\n13<|end_of_text|>']" + ] + }, + "metadata": {}, + "execution_count": 9 + } + ], + "source": [ + "# alpaca_prompt = Copied from above\n", + "FastLanguageModel.for_inference(model) # Enable native 2x faster inference\n", + "inputs = tokenizer(\n", + "[\n", + " alpaca_prompt.format(\n", + " \"Continue the fibonnaci sequence.\", # instruction\n", + " \"1, 1, 2, 3, 5, 8\", # input\n", + " \"\", # output - leave this blank for generation!\n", + " )\n", + "], return_tensors = \"pt\").to(\"cuda\")\n", + "\n", + "outputs = model.generate(**inputs, max_new_tokens = 64, use_cache = True)\n", + "tokenizer.batch_decode(outputs)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "CrSvZObor0lY" + }, + "source": [ + " You can also use a `TextStreamer` for continuous inference - so you can see the generation token by token, instead of waiting the whole time!" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 0 + }, + "id": "e2pEuRb1r2Vg", + "outputId": "18080456-6d74-44f3-9e45-2cb20fa4bab0" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n", + "\n", + "### Instruction:\n", + "Continue the fibonnaci sequence.\n", + "\n", + "### Input:\n", + "1, 1, 2, 3, 5, 8\n", + "\n", + "### Response:\n", + "13<|end_of_text|>\n" + ] + } + ], + "source": [ + "# alpaca_prompt = Copied from above\n", + "FastLanguageModel.for_inference(model) # Enable native 2x faster inference\n", + "inputs = tokenizer(\n", + "[\n", + " alpaca_prompt.format(\n", + " \"Continue the fibonnaci sequence.\", # instruction\n", + " \"1, 1, 2, 3, 5, 8\", # input\n", + " \"\", # output - leave this blank for generation!\n", + " )\n", + "], return_tensors = \"pt\").to(\"cuda\")\n", + "\n", + "from transformers import TextStreamer\n", + "text_streamer = TextStreamer(tokenizer)\n", + "_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 128)" + ] + }, + { + "cell_type": "code", + "source": [ + "# alpaca_prompt = Copied from above\n", + "FastLanguageModel.for_inference(model) # Enable native 2x faster inference\n", + "inputs = tokenizer(\n", + "[\n", + " alpaca_prompt.format(\n", + " \"What is a famous bridge in San Francisco bay area?\", # instruction\n", + " \"\", # input\n", + " \"\", # output - leave this blank for generation!\n", + " )\n", + "], return_tensors = \"pt\").to(\"cuda\")\n", + "\n", + "from transformers import TextStreamer\n", + "text_streamer = TextStreamer(tokenizer)\n", + "_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 128)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 0 + }, + "id": "Z5tWI7ivVjMg", + "outputId": "6f6baa35-1b13-4671-d910-d824e66f1640" + }, + "execution_count": 11, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n", + "\n", + "### Instruction:\n", + "What is a famous bridge in San Francisco bay area?\n", + "\n", + "### Input:\n", + "\n", + "\n", + "### Response:\n", + "The Golden Gate Bridge is a suspension bridge that spans the Golden Gate Strait, the mile-wide, three-mile-long channel between San Francisco Bay and the Pacific Ocean.<|end_of_text|>\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# alpaca_prompt = Copied from above\n", + "FastLanguageModel.for_inference(model) # Enable native 2x faster inference\n", + "inputs = tokenizer(\n", + "[\n", + " alpaca_prompt.format(\n", + " \"What is a famous university in San Francisco bay area?\", # instruction\n", + " \"\", # input\n", + " \"\", # output - leave this blank for generation!\n", + " )\n", + "], return_tensors = \"pt\").to(\"cuda\")\n", + "\n", + "from transformers import TextStreamer\n", + "text_streamer = TextStreamer(tokenizer)\n", + "_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 128)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 0 + }, + "id": "9fJZQcwlVlBq", + "outputId": "079a5704-678a-48a6-e41b-eb9526af113e" + }, + "execution_count": 12, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n", + "\n", + "### Instruction:\n", + "What is a famous university in San Francisco bay area?\n", + "\n", + "### Input:\n", + "\n", + "\n", + "### Response:\n", + "Stanford University is a famous university in San Francisco bay area.<|end_of_text|>\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# alpaca_prompt = Copied from above\n", + "FastLanguageModel.for_inference(model) # Enable native 2x faster inference\n", + "inputs = tokenizer(\n", + "[\n", + " alpaca_prompt.format(\n", + " \"What are the DNA bases?\", # instruction\n", + " \"\", # input\n", + " \"\", # output - leave this blank for generation!\n", + " )\n", + "], return_tensors = \"pt\").to(\"cuda\")\n", + "\n", + "from transformers import TextStreamer\n", + "text_streamer = TextStreamer(tokenizer)\n", + "_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 128)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 0 + }, + "id": "1TPeJxviVls8", + "outputId": "2db6af74-dc11-45eb-84b3-b0a716e3d519" + }, + "execution_count": 13, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n", + "\n", + "### Instruction:\n", + "What are the DNA bases?\n", + "\n", + "### Input:\n", + "\n", + "\n", + "### Response:\n", + "The DNA bases are the four nucleobases that are found in DNA. They are adenine (A), cytosine (C), guanine (G), and thymine (T).<|end_of_text|>\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# alpaca_prompt = Copied from above\n", + "FastLanguageModel.for_inference(model) # Enable native 2x faster inference\n", + "inputs = tokenizer(\n", + "[\n", + " alpaca_prompt.format(\n", + " \"What are all of the types of bonds found in DNA?\", # instruction\n", + " \"\", # input\n", + " \"\", # output - leave this blank for generation!\n", + " )\n", + "], return_tensors = \"pt\").to(\"cuda\")\n", + "\n", + "from transformers import TextStreamer\n", + "text_streamer = TextStreamer(tokenizer)\n", + "_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 128)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 0 + }, + "id": "GlPUCSprVnRK", + "outputId": "a2a2bd5e-e175-4163-b8c9-59dfde786e6c" + }, + "execution_count": 14, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n", + "\n", + "### Instruction:\n", + "What are all of the types of bonds found in DNA?\n", + "\n", + "### Input:\n", + "\n", + "\n", + "### Response:\n", + "The four types of bonds found in DNA are hydrogen bonds, phosphodiester bonds, covalent bonds, and ionic bonds.<|end_of_text|>\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# alpaca_prompt = Copied from above\n", + "FastLanguageModel.for_inference(model) # Enable native 2x faster inference\n", + "inputs = tokenizer(\n", + "[\n", + " alpaca_prompt.format(\n", + " \"What are the reaction names to create DNA?\", # instruction\n", + " \"\", # input\n", + " \"\", # output - leave this blank for generation!\n", + " )\n", + "], return_tensors = \"pt\").to(\"cuda\")\n", + "\n", + "from transformers import TextStreamer\n", + "text_streamer = TextStreamer(tokenizer)\n", + "_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 128)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 0 + }, + "id": "NelWwdJjVpqR", + "outputId": "4d21c371-0fb5-41c2-971a-a2a3802e0b3a" + }, + "execution_count": 15, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n", + "\n", + "### Instruction:\n", + "What are the reaction names to create DNA?\n", + "\n", + "### Input:\n", + "\n", + "\n", + "### Response:\n", + "The reaction names to create DNA are DNA polymerization and DNA replication.<|end_of_text|>\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# alpaca_prompt = Copied from above\n", + "FastLanguageModel.for_inference(model) # Enable native 2x faster inference\n", + "inputs = tokenizer(\n", + "[\n", + " alpaca_prompt.format(\n", + " \"What is the structure for adenine?\", # instruction\n", + " \"\", # input\n", + " \"\", # output - leave this blank for generation!\n", + " )\n", + "], return_tensors = \"pt\").to(\"cuda\")\n", + "\n", + "from transformers import TextStreamer\n", + "text_streamer = TextStreamer(tokenizer)\n", + "_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 128)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 0 + }, + "id": "FQw11BVmrV-f", + "outputId": "13faeee6-0226-4d1b-c3a6-29c2662d4f0d" + }, + "execution_count": 16, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n", + "\n", + "### Instruction:\n", + "What is the structure for adenine?\n", + "\n", + "### Input:\n", + "\n", + "\n", + "### Response:\n", + "[C][C][Branch1][C][C][C][C][C][C][C][C][C][C][C][C][C][C][C][C][C][C][C][C][C][C][C][C][C][C][C][C][C][C][C][C][C][C][C][C][C][C][C][C][C][C][C][C][C][C][C][C][C][C][C][C][C][C][C][C][C][C][C][C][C\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "uMuVrWbjAzhc" + }, + "source": [ + "<a name=\"Save\"></a>\n", + "### Saving, loading finetuned models\n", + "To save the final model as LoRA adapters, either use Huggingface's `push_to_hub` for an online save or `save_pretrained` for a local save.\n", + "\n", + "**[NOTE]** This ONLY saves the LoRA adapters, and not the full model. To save to 16bit or GGUF, scroll down!" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "id": "upcOlWe7A1vc", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 0 + }, + "outputId": "8b077467-8558-405d-c397-d498ed565c81" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n", + " warnings.warn(\n" + ] + } + ], + "source": [ + "model.save_pretrained(\"lora_model\") # Local saving\n", + "# model.push_to_hub(\"Your-Model-Name\", organization=\"kevinkawchak\", token = \"Your HF writable token\", private=True) # ONLY saves the LoRA adapters" + ] + }, + { + "cell_type": "code", + "source": [ + "if False:\n", + " from unsloth import FastLanguageModel\n", + " model, tokenizer = FastLanguageModel.from_pretrained(\n", + " model_name = \"lora_model\", # YOUR MODEL YOU USED FOR TRAINING\n", + " max_seq_length = max_seq_length,\n", + " dtype = dtype,\n", + " load_in_4bit = load_in_4bit,\n", + " )\n", + " FastLanguageModel.for_inference(model) # Enable native 2x faster inference\n", + "# alpaca_prompt = You MUST copy from above!\n", + "inputs = tokenizer(\n", + "[\n", + " alpaca_prompt.format(\n", + " \"What is a famous tall tower in Paris?\", # instruction\n", + " \"\", # input\n", + " \"\", # output - leave this blank for generation!\n", + " )\n", + "], return_tensors = \"pt\").to(\"cuda\")\n", + "outputs = model.generate(**inputs, max_new_tokens = 128, use_cache = True)\n", + "tokenizer.batch_decode(outputs)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 0 + }, + "id": "RYh575LYOG5J", + "outputId": "4686a43c-839c-47cf-ef8d-c2723184b5a1" + }, + "execution_count": 18, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "['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.<|end_of_text|>']" + ] + }, + "metadata": {}, + "execution_count": 18 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "AEEcJ4qfC7Lp" + }, + "source": [ + "Now if you want to load the LoRA adapters we just saved for inference, set `False` to `True`:" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "QQMjaNrjsU5_" + }, + "source": [ + "You can also use Hugging Face's `AutoModelForPeftCausalLM`. Only use this if you do not have `unsloth` installed. It can be hopelessly slow, since `4bit` model downloading is not supported, and Unsloth's **inference is 2x faster**." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "id": "yFfaXG0WsQuE" + }, + "outputs": [], + "source": [ + "## If using model.save_pretrained(\"lora_model\") # Local saving\n", + "if False:\n", + " # I highly do NOT suggest - use Unsloth if possible\n", + " from peft import AutoPeftModelForCausalLM\n", + " from transformers import AutoTokenizer\n", + " model = AutoPeftModelForCausalLM.from_pretrained(\n", + " \"lora_model\", # YOUR MODEL YOU USED FOR TRAINING\n", + " load_in_4bit = load_in_4bit,\n", + " )\n", + " tokenizer = AutoTokenizer.from_pretrained(\"lora_model\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "f422JgM9sdVT" + }, + "source": [ + "### Saving to float16 for VLLM\n", + "\n", + "We also support saving to `float16` directly. Select `merged_16bit` for float16 or `merged_4bit` for int4. We also allow `lora` adapters as a fallback. Use `push_to_hub_merged` to upload to your Hugging Face account! You can go to https://huggingface.co/settings/tokens for your personal tokens." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "id": "iHjt_SMYsd3P", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 206, + "referenced_widgets": [ + "21efc5c935a74e399d4bb7402ec21bd5", + "d6c2ad7d6f634cebacf96b2ab411fe69", + "ef0016a3d73c4925ab602ecbcc2a9ecd", + "b63d3cfd22a443509b05faf2f0139564", + "03a37e97ad844a5e835a759fba6f85aa", + "a07aebf884d242c89c3fe38d4dc1d987", + "36dd174ab9ce4d0da88a05906a8c2bbb", + "c20c18e05ae24a9a8a7820f74077c6a0", + "d5e72923fc87490192f3c9bdc9376cf1", + "7f75d497905f42c8940a94e9d446c936", + "993c768a6ade405e80b54704caa4b010", + "bf926de571624957b17b677173c95497", + "5e4521a9b479429d9e30b1508d5e08d0", + "38aef7e27aea46838aa15de407b4fa53", + "5c7197a9495e4e9194cc900846ea18ee", + "de70c5e7ee7a46fdb7bb1d120f37d0f5", + "de388ca2a20849cd88209af458746b3f", + "c09ffc8bcfc64229b3857832b821a747", + "8191ee4bda644f9ca57dbdcc1ed1eda1", + "cceea9cc9f0d49e7bfc7c1a3d19bf1df", + "f3f51801dd0b42e7883f2c068380d2ec", + "f8c2cd649e18456086e061eedcbc4970", + "883a4bcfacea4df6a23b27aba8a5cac7", + "f227c3c7c08d4ee6b5005a9635b9b99f", + "23c7d3f944c34d52ba4501b6d1d6beea", + "0d85ead432c44e06b212532801752d26", + "4425f01c7aff4416b92e08c2a49c0283", + "ae48783d6e154b358788c9949aec12ab", + "04cd6402aef24bd6b98150e3066889ee", + "0b0f71eda22149e7aeb761cecac09d90", + "a70fb36a75d1476295795417f1d0a513", + "6c6f346f55a5489687c1c579b043c070", + "bbd88bff2d8d44dbbce4c891cbfb226e" + ] + }, + "outputId": "3867448e-82d8-4f16-acac-07c4580c143c" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Unsloth: Saving LoRA adapters. Please wait...\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "README.md: 0%| | 0.00/579 [00:00<?, ?B/s]" + ], + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "21efc5c935a74e399d4bb7402ec21bd5" + } + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n", + " warnings.warn(\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "config.json: 0%| | 0.00/653 [00:00<?, ?B/s]" + ], + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "bf926de571624957b17b677173c95497" + } + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "adapter_model.safetensors: 0%| | 0.00/48.0 [00:00<?, ?B/s]" + ], + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "883a4bcfacea4df6a23b27aba8a5cac7" + } + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Saved lora model to https://huggingface.co/nvidia-Llama3-ChatQA-1.5-8B-MoleculeLo\n" + ] + } + ], + "source": [ + "# Merge to 16bit\n", + "if False: model.save_pretrained_merged(\"model\", tokenizer, save_method = \"merged_16bit\",)\n", + "if True: model.push_to_hub_merged(\"nvidia-Llama3-ChatQA-1.5-8B-Molecule16\", tokenizer, save_method = \"merged_16bit\", token = \"hf_\", private=True)\n", + "\n", + "# Merge to 4bit\n", + "if False: model.save_pretrained_merged(\"model\", tokenizer, save_method = \"merged_4bit\",)\n", + "if True: model.push_to_hub_merged(\"nvidia-Llama3-ChatQA-1.5-8B-Molecule04\", tokenizer, save_method = \"merged_4bit_forced\", token = \"hf_\", private=True)\n", + "\n", + "# Just LoRA adapters\n", + "if False: model.save_pretrained_merged(\"model\", tokenizer, save_method = \"lora\",)\n", + "if True: model.push_to_hub_merged(\"nvidia-Llama3-ChatQA-1.5-8B-MoleculeLo\", tokenizer, save_method = \"lora\", token = \"hf_\", private=True)" + ] + }, + { + "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." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "id": "FqfebeAdT073" + }, + "outputs": [], + "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 = \"\")" + ] + }, + { + "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)." + ] + }, + { + "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", + "\n", + "<div class=\"align-center\">\n", + " <a href=\"https://github.com/unslothai/unsloth\"><img src=\"https://github.com/unslothai/unsloth/raw/main/images/unsloth%20new%20logo.png\" width=\"115\"></a>\n", + " <a href=\"https://discord.gg/u54VK8m8tk\"><img src=\"https://github.com/unslothai/unsloth/raw/main/images/Discord.png\" width=\"145\"></a>\n", + " <a href=\"https://ko-fi.com/unsloth\"><img src=\"https://github.com/unslothai/unsloth/raw/main/images/Kofi button.png\" width=\"145\"></a></a> Support our work if you can! Thanks!\n", + "</div>" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "id": "zmdJjv6-GarM" + }, + "outputs": [], + "source": [ + "# from google.colab import runtime\n", + "# runtime.unassign()" + ] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "gpuType": "A100", + "machine_shape": "hm", + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "name": "python" + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "22bca2c1eb4344c481c1484b5384e2e6": { + "model_module": "@jupyter-widgets/controls", + "model_name": "HBoxModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + "box_style": "", + 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