{ "cells": [ { "cell_type": "markdown", "id": "14ac6109", "metadata": { "id": "IqM-T1RTzY6C", "papermill": { "duration": 0.021668, "end_time": "2025-01-01T01:36:49.042245", "exception": false, "start_time": "2025-01-01T01:36:49.020577", "status": "completed" }, "tags": [] }, "source": [ "To run this, press \"*Runtime*\" and press \"*Run all*\" on a **free** Tesla T4 Google Colab instance!\n", "
\n", " \n", " \n", " Join Discord if you need help + support us if you can!\n", "
\n", "\n", "To install Unsloth on your own computer, follow the installation instructions on our Github page [here](https://github.com/unslothai/unsloth#installation-instructions---conda).\n", "\n", "You will learn how to do [data prep](#Data), how to [train](#Train), how to [run the model](#Inference), & [how to save it](#Save) (eg for Llama.cpp)." ] }, { "cell_type": "markdown", "id": "992529ab", "metadata": { "papermill": { "duration": 0.017829, "end_time": "2025-01-01T01:36:49.078747", "exception": false, "start_time": "2025-01-01T01:36:49.060918", "status": "completed" }, "tags": [] }, "source": [ "## Kaggle is slow - you'll have to wait **5 minutes** for it to install.\n", "\n", "I suggest you to use our free Colab notebooks instead. I linked our Mistral Colab notebook here: [notebook](https://colab.research.google.com/drive/1Dyauq4kTZoLewQ1cApceUQVNcnnNTzg_?usp=sharing)" ] }, { "cell_type": "code", "execution_count": 1, "id": "5891519d", "metadata": { "execution": { "iopub.execute_input": "2025-01-01T01:36:49.115782Z", "iopub.status.busy": "2025-01-01T01:36:49.115557Z", "iopub.status.idle": "2025-01-01T01:39:57.034466Z", "shell.execute_reply": "2025-01-01T01:39:57.033580Z" }, "papermill": { "duration": 187.938919, "end_time": "2025-01-01T01:39:57.036077", "exception": false, "start_time": "2025-01-01T01:36:49.097158", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Collecting pip3-autoremove\r\n", " Downloading pip3_autoremove-1.2.2-py2.py3-none-any.whl.metadata (2.2 kB)\r\n", "Requirement already satisfied: pip in /usr/local/lib/python3.10/dist-packages (from pip3-autoremove) (24.1.2)\r\n", "Requirement already satisfied: setuptools in 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name...\r\n", "google-api-core 1.34.1 is installed but google-api-core<3.0.0dev,>=2.10.2 is required\r\n", "Redoing requirement with just package name...\r\n", "The 'pycairo>=1.16.0' distribution was not found and is required by the application\r\n", "Skipping pycairo\r\n", "torchvision 0.19.1+cu121 (/usr/local/lib/python3.10/dist-packages)\r\n", " torch 2.4.1+cu121 (/usr/local/lib/python3.10/dist-packages)\r\n", " sympy 1.13.3 (/usr/local/lib/python3.10/dist-packages)\r\n", " mpmath 1.3.0 (/usr/local/lib/python3.10/dist-packages)\r\n", "torch 2.4.1+cu121 (/usr/local/lib/python3.10/dist-packages)\r\n", " sympy 1.13.3 (/usr/local/lib/python3.10/dist-packages)\r\n", " mpmath 1.3.0 (/usr/local/lib/python3.10/dist-packages)\r\n", "torchaudio 2.4.1+cu121 (/usr/local/lib/python3.10/dist-packages)\r\n", " torch 2.4.1+cu121 (/usr/local/lib/python3.10/dist-packages)\r\n", " sympy 1.13.3 (/usr/local/lib/python3.10/dist-packages)\r\n", " mpmath 1.3.0 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nvidia-cublas-cu12, nvidia-cusparse-cu12, nvidia-cudnn-cu12, nvidia-cusolver-cu12, torch, xformers, torchvision, torchaudio\r\n", " Attempting uninstall: nvidia-nccl-cu12\r\n", " Found existing installation: nvidia-nccl-cu12 2.23.4\r\n", " Uninstalling nvidia-nccl-cu12-2.23.4:\r\n", " Successfully uninstalled nvidia-nccl-cu12-2.23.4\r\n", "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\r\n", "fastai 2.7.17 requires torch<2.5,>=1.10, but you have torch 2.5.1+cu121 which is incompatible.\u001b[0m\u001b[31m\r\n", "\u001b[0mSuccessfully installed mpmath-1.3.0 nvidia-cublas-cu12-12.1.3.1 nvidia-cuda-cupti-cu12-12.1.105 nvidia-cuda-nvrtc-cu12-12.1.105 nvidia-cuda-runtime-cu12-12.1.105 nvidia-cudnn-cu12-9.1.0.70 nvidia-cufft-cu12-11.0.2.54 nvidia-curand-cu12-10.3.2.106 nvidia-cusolver-cu12-11.4.5.107 nvidia-cusparse-cu12-12.1.0.106 nvidia-nccl-cu12-2.21.5 nvidia-nvjitlink-cu12-12.1.105 nvidia-nvtx-cu12-12.1.105 sympy-1.13.1 torch-2.5.1+cu121 torchaudio-2.5.1+cu121 torchvision-0.20.1+cu121 triton-3.1.0 xformers-0.0.29.post1\r\n", "Collecting unsloth\r\n", " Downloading unsloth-2024.12.12-py3-none-any.whl.metadata (60 kB)\r\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m60.0/60.0 kB\u001b[0m \u001b[31m2.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n", "\u001b[?25hCollecting unsloth_zoo>=2024.12.7 (from unsloth)\r\n", " Downloading unsloth_zoo-2024.12.7-py3-none-any.whl.metadata (16 kB)\r\n", "Requirement already satisfied: torch>=2.4.0 in /usr/local/lib/python3.10/dist-packages (from unsloth) (2.5.1+cu121)\r\n", "Requirement already satisfied: xformers>=0.0.27.post2 in /usr/local/lib/python3.10/dist-packages (from unsloth) (0.0.29.post1)\r\n", "Collecting bitsandbytes (from unsloth)\r\n", " Downloading bitsandbytes-0.45.0-py3-none-manylinux_2_24_x86_64.whl.metadata (2.9 kB)\r\n", "Requirement already satisfied: triton>=3.0.0 in /usr/local/lib/python3.10/dist-packages (from unsloth) (3.1.0)\r\n", "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from unsloth) (24.1)\r\n", "Collecting tyro (from unsloth)\r\n", " Downloading tyro-0.9.5-py3-none-any.whl.metadata (9.4 kB)\r\n", "Collecting transformers!=4.47.0,>=4.46.1 (from unsloth)\r\n", " Downloading transformers-4.47.1-py3-none-any.whl.metadata (44 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(0.34.2)\r\n", "Collecting trl!=0.9.0,!=0.9.1,!=0.9.2,!=0.9.3,>=0.7.9 (from unsloth)\r\n", " Downloading trl-0.13.0-py3-none-any.whl.metadata (11 kB)\r\n", "Collecting peft!=0.11.0,>=0.7.1 (from unsloth)\r\n", " Downloading peft-0.14.0-py3-none-any.whl.metadata (13 kB)\r\n", "Requirement already satisfied: protobuf<4.0.0 in /usr/local/lib/python3.10/dist-packages (from unsloth) (3.20.3)\r\n", "Requirement already satisfied: huggingface_hub in /usr/local/lib/python3.10/dist-packages (from unsloth) (0.24.7)\r\n", "Collecting hf_transfer (from unsloth)\r\n", " Downloading hf_transfer-0.1.8-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (1.7 kB)\r\n", "Requirement already satisfied: pyyaml in /usr/local/lib/python3.10/dist-packages (from accelerate>=0.34.1->unsloth) (6.0.2)\r\n", "Requirement already satisfied: safetensors>=0.4.3 in /usr/local/lib/python3.10/dist-packages (from accelerate>=0.34.1->unsloth) (0.4.5)\r\n", "Requirement already satisfied: filelock in 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nvidia-cuda-runtime-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch>=2.4.0->unsloth) (12.1.105)\r\n", "Requirement already satisfied: nvidia-cuda-cupti-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch>=2.4.0->unsloth) (12.1.105)\r\n", "Requirement already satisfied: nvidia-cudnn-cu12==9.1.0.70 in /usr/local/lib/python3.10/dist-packages (from torch>=2.4.0->unsloth) (9.1.0.70)\r\n", "Requirement already satisfied: nvidia-cublas-cu12==12.1.3.1 in /usr/local/lib/python3.10/dist-packages (from torch>=2.4.0->unsloth) (12.1.3.1)\r\n", "Requirement already satisfied: nvidia-cufft-cu12==11.0.2.54 in /usr/local/lib/python3.10/dist-packages (from torch>=2.4.0->unsloth) (11.0.2.54)\r\n", "Requirement already satisfied: nvidia-curand-cu12==10.3.2.106 in /usr/local/lib/python3.10/dist-packages (from torch>=2.4.0->unsloth) (10.3.2.106)\r\n", "Requirement already satisfied: nvidia-cusolver-cu12==11.4.5.107 in /usr/local/lib/python3.10/dist-packages (from torch>=2.4.0->unsloth) (11.4.5.107)\r\n", "Requirement already satisfied: nvidia-cusparse-cu12==12.1.0.106 in /usr/local/lib/python3.10/dist-packages (from torch>=2.4.0->unsloth) (12.1.0.106)\r\n", "Requirement already satisfied: nvidia-nccl-cu12==2.21.5 in /usr/local/lib/python3.10/dist-packages (from torch>=2.4.0->unsloth) (2.21.5)\r\n", "Requirement already satisfied: nvidia-nvtx-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch>=2.4.0->unsloth) (12.1.105)\r\n", "Requirement already satisfied: sympy==1.13.1 in /usr/local/lib/python3.10/dist-packages (from torch>=2.4.0->unsloth) (1.13.1)\r\n", "Requirement already satisfied: nvidia-nvjitlink-cu12 in /usr/local/lib/python3.10/dist-packages (from nvidia-cusolver-cu12==11.4.5.107->torch>=2.4.0->unsloth) (12.1.105)\r\n", "Requirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from sympy==1.13.1->torch>=2.4.0->unsloth) (1.3.0)\r\n", "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers!=4.47.0,>=4.46.1->unsloth) (2024.9.11)\r\n", "Collecting tokenizers<0.22,>=0.21 (from transformers!=4.47.0,>=4.46.1->unsloth)\r\n", " Downloading tokenizers-0.21.0-cp39-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (6.7 kB)\r\n", "Requirement already satisfied: rich in /usr/local/lib/python3.10/dist-packages (from trl!=0.9.0,!=0.9.1,!=0.9.2,!=0.9.3,>=0.7.9->unsloth) (13.8.1)\r\n", "Collecting cut_cross_entropy (from unsloth_zoo>=2024.12.7->unsloth)\r\n", " Downloading cut_cross_entropy-24.12.3-py3-none-any.whl.metadata (9.3 kB)\r\n", "Requirement already satisfied: pillow in /usr/local/lib/python3.10/dist-packages (from unsloth_zoo>=2024.12.7->unsloth) (10.4.0)\r\n", "Requirement already satisfied: docstring-parser>=0.15 in /usr/local/lib/python3.10/dist-packages (from tyro->unsloth) (0.16)\r\n", "Collecting shtab>=1.5.6 (from tyro->unsloth)\r\n", " Downloading 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huggingface_hub, tyro, tokenizers, transformers, cut_cross_entropy, bitsandbytes, peft, trl, unsloth_zoo, unsloth\r\n", " Attempting uninstall: huggingface_hub\r\n", " Found existing installation: huggingface-hub 0.24.7\r\n", " Uninstalling huggingface-hub-0.24.7:\r\n", " Successfully uninstalled huggingface-hub-0.24.7\r\n", " Attempting uninstall: tokenizers\r\n", " Found existing installation: tokenizers 0.19.1\r\n", " Uninstalling tokenizers-0.19.1:\r\n", " Successfully uninstalled tokenizers-0.19.1\r\n", " Attempting uninstall: transformers\r\n", " Found existing installation: transformers 4.44.2\r\n", " Uninstalling transformers-4.44.2:\r\n", " Successfully uninstalled transformers-4.44.2\r\n", "Successfully installed bitsandbytes-0.45.0 cut_cross_entropy-24.12.3 hf_transfer-0.1.8 huggingface_hub-0.27.0 peft-0.14.0 shtab-1.7.1 tokenizers-0.21.0 transformers-4.47.1 trl-0.13.0 tyro-0.9.5 unsloth-2024.12.12 unsloth_zoo-2024.12.7\r\n" ] } ], "source": [ "!pip install pip3-autoremove\n", "!pip-autoremove torch torchvision torchaudio -y\n", "!pip install torch torchvision torchaudio xformers --index-url https://download.pytorch.org/whl/cu121\n", "!pip install unsloth" ] }, { "cell_type": "markdown", "id": "82af8803", "metadata": { "id": "r2v_X2fA0Df5", "papermill": { "duration": 0.055944, "end_time": "2025-01-01T01:39:57.151102", "exception": false, "start_time": "2025-01-01T01:39:57.095158", "status": "completed" }, "tags": [] }, "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, "id": "81081d4b", "metadata": { "execution": { "iopub.execute_input": "2025-01-01T01:39:57.263299Z", "iopub.status.busy": "2025-01-01T01:39:57.263043Z", "iopub.status.idle": "2025-01-01T01:40:53.320412Z", "shell.execute_reply": "2025-01-01T01:40:53.319479Z" }, "id": "QmUBVEnvCDJv", "outputId": "5eff0d61-05b4-471c-eea2-c2e84a915109", "papermill": { "duration": 56.11539, "end_time": "2025-01-01T01:40:53.322099", "exception": false, "start_time": "2025-01-01T01:39:57.206709", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "🦥 Unsloth: Will patch your computer to enable 2x faster free finetuning.\n", "🦥 Unsloth Zoo will now patch everything to make training faster!\n", "==((====))== Unsloth 2024.12.12: Fast Mllama vision patching. Transformers: 4.47.1.\n", " \\\\ /| GPU: Tesla T4. Max memory: 14.741 GB. Platform: Linux.\n", "O^O/ \\_/ \\ Torch: 2.5.1+cu121. CUDA: 7.5. CUDA Toolkit: 12.1. Triton: 3.1.0\n", "\\ / Bfloat16 = FALSE. FA [Xformers = 0.0.29.post1. FA2 = False]\n", " \"-____-\" Free Apache license: http://github.com/unslothai/unsloth\n", "Unsloth: Fast downloading is enabled - ignore downloading bars which are red colored!\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "e50032cd57a9417892ea70e3fd0a9f5b", "version_major": 2, "version_minor": 0 }, "text/plain": [ "model.safetensors.index.json: 0%| | 0.00/375k [00:00 0 ! Suggested 8, 16, 32, 64, 128\n", " finetune_vision_layers = True, # False if not finetuning vision part\n", " finetune_language_layers = True, # False if not finetuning language part\n", " finetune_attention_modules = True, # False if not finetuning attention layers\n", " finetune_mlp_modules = True, # False if not finetuning MLP layers\n", " lora_alpha = 32, # Recommended alpha == r at least\n", " lora_dropout = 0, # Supports any, but = 0 is optimized\n", " bias = \"none\", # Supports any, but = \"none\" is optimized\n", " random_state = 3407,\n", " use_rslora = False, # We support rank stabilized LoRA\n", " loftq_config = None, # And LoftQ\n", ")" ] }, { "cell_type": "markdown", "id": "bbe9e44b", "metadata": { "id": "vITh0KVJ10qX", "papermill": { "duration": 0.05753, "end_time": "2025-01-01T01:40:59.328848", "exception": false, "start_time": "2025-01-01T01:40:59.271318", "status": "completed" }, "tags": [] }, "source": [ "\n", "### Data Prep\n", "We now use the Alpaca dataset from [yahma](https://huggingface.co/datasets/yahma/alpaca-cleaned), which is a filtered version of 52K of the original [Alpaca dataset](https://crfm.stanford.edu/2023/03/13/alpaca.html). You can replace this code section with your own data prep.\n", "\n", "**[NOTE]** To train only on completions (ignoring the user's input) read TRL's docs [here](https://huggingface.co/docs/trl/sft_trainer#train-on-completions-only).\n", "\n", "**[NOTE]** Remember to add the **EOS_TOKEN** to the tokenized output!! Otherwise you'll get infinite generations!\n", "\n", "If you want to use the `ChatML` template for ShareGPT datasets, try our conversational [notebook](https://colab.research.google.com/drive/1Aau3lgPzeZKQ-98h69CCu1UJcvIBLmy2?usp=sharing).\n", "\n", "For text completions like novel writing, try this [notebook](https://colab.research.google.com/drive/1ef-tab5bhkvWmBOObepl1WgJvfvSzn5Q?usp=sharing)." ] }, { "cell_type": "code", "execution_count": 4, "id": "3a74fde3", "metadata": { "execution": { "iopub.execute_input": "2025-01-01T01:40:59.444214Z", "iopub.status.busy": "2025-01-01T01:40:59.443934Z", "iopub.status.idle": "2025-01-01T01:41:04.410001Z", "shell.execute_reply": "2025-01-01T01:41:04.409044Z" }, "id": "LjY75GoYUCB8", "outputId": "9f40f734-788c-4793-c1af-e9d003337612", "papermill": { "duration": 5.025641, "end_time": "2025-01-01T01:41:04.411709", "exception": false, "start_time": "2025-01-01T01:40:59.386068", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "0aca87e0f7d4459ea312cf1a7a3160a4", "version_major": 2, "version_minor": 0 }, "text/plain": [ "image_summaries.parquet: 0%| | 0.00/264M [00:00\n", "### Train the model\n", "Now let's use Huggingface TRL's `SFTTrainer`! More docs here: [TRL SFT docs](https://huggingface.co/docs/trl/sft_trainer). We do 60 steps to speed things up, but you can set `num_train_epochs=1` for a full run, and turn off `max_steps=None`. We also support TRL's `DPOTrainer`!" ] }, { "cell_type": "code", "execution_count": 5, "id": "116e1ce1", "metadata": { "execution": { "iopub.execute_input": "2025-01-01T01:41:04.644664Z", "iopub.status.busy": "2025-01-01T01:41:04.644382Z", "iopub.status.idle": "2025-01-01T01:41:06.493679Z", "shell.execute_reply": "2025-01-01T01:41:06.493023Z" }, "id": "95_Nn-89DhsL", "outputId": "4b809e6d-271f-446f-dec8-abe0d13259f8", "papermill": { "duration": 1.908619, "end_time": "2025-01-01T01:41:06.495152", "exception": false, "start_time": "2025-01-01T01:41:04.586533", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "from trl import SFTTrainer, SFTConfig\n", "from unsloth.trainer import UnslothVisionDataCollator\n", "from transformers import DataCollatorForSeq2Seq\n", "from unsloth import is_bf16_supported\n", "from transformers import AutoProcessor\n", "\n", "FastVisionModel.for_training(model) # Enable for training!\n", "\n", "processor = AutoProcessor.from_pretrained(\"unsloth/Llama-3.2-11B-Vision-Instruct-unsloth-bnb-4bit\")\n", "\n", "# unsloth-zoo collator doesn't support messages without images\n", "def collate_fn(examples):\n", " # Get the texts and images, and apply the chat template\n", " texts = [processor.apply_chat_template(example[\"messages\"], tokenize=False) for example in examples]\n", " # MUST be None or be populated with images; this is what unsloth is missing\n", " images = None\n", " for example in examples:\n", " if \"messages\" in examples:\n", " for msg in examples[\"messages\"]:\n", " if isinstance(msg[\"content\"], list):\n", " has_image = False\n", " for mode in msg[\"content\"]:\n", " if mode[\"type\"] == \"image\":\n", " if images is None:\n", " images = []\n", " images.append(mode[\"image\"])\n", " has_image = True\n", " if not has_image and isinstance(images, list):\n", " images.append(None)\n", "\n", " # Tokenize the texts and process the images\n", " batch = processor(texts, images, return_tensors=\"pt\", padding=True)\n", "\n", " # The labels are the input_ids, and we mask the padding tokens in the loss computation\n", " labels = batch[\"input_ids\"].clone()\n", " labels[labels == processor.tokenizer.pad_token_id] = -100\n", " batch[\"labels\"] = labels\n", "\n", " return batch\n", "\n", "trainer = SFTTrainer(\n", " model = model,\n", " tokenizer = tokenizer,\n", " train_dataset = dataset,\n", " #data_collator = collate_fn,\n", " data_collator = UnslothVisionDataCollator(model, tokenizer),\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 = SFTConfig(\n", " per_device_train_batch_size = 2,\n", " gradient_accumulation_steps = 4,\n", " warmup_steps = 5,\n", " #max_steps = 30,\n", " num_train_epochs = 1, # Set this instead of max_steps for full training runs\n", " learning_rate = 2e-4,\n", " fp16 = not is_bf16_supported(),\n", " bf16 = 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", " report_to = \"none\", # For Weights and Biases\n", "\n", " # You MUST put the below items for vision finetuning:\n", " remove_unused_columns = False,\n", " dataset_text_field = \"\",\n", " dataset_kwargs = {\"skip_prepare_dataset\": True},\n", " dataset_num_proc = 4,\n", " max_seq_length = 2048,\n", " ),\n", ")" ] }, { "cell_type": "code", "execution_count": 6, "id": "962391b6", "metadata": { "cellView": "form", "execution": { "iopub.execute_input": "2025-01-01T01:41:06.614544Z", "iopub.status.busy": "2025-01-01T01:41:06.614275Z", "iopub.status.idle": "2025-01-01T01:41:06.619459Z", "shell.execute_reply": "2025-01-01T01:41:06.618801Z" }, "id": "2ejIt2xSNKKp", "outputId": "4815a050-0c0f-4a6a-9d93-b01c44eaea35", "papermill": { "duration": 0.06503, "end_time": "2025-01-01T01:41:06.620624", "exception": false, "start_time": "2025-01-01T01:41:06.555594", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "GPU = Tesla T4. Max memory = 14.741 GB.\n", "7.881 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, "id": "52bb19f4", "metadata": { "execution": { "iopub.execute_input": "2025-01-01T01:41:06.736648Z", "iopub.status.busy": "2025-01-01T01:41:06.736441Z", "iopub.status.idle": "2025-01-01T01:55:19.558624Z", "shell.execute_reply": "2025-01-01T01:55:19.557921Z" }, "id": "yqxqAZ7KJ4oL", "outputId": "3cf26aac-6042-4458-c4a6-d8849efb6a95", "papermill": { "duration": 852.881724, "end_time": "2025-01-01T01:55:19.560033", "exception": false, "start_time": "2025-01-01T01:41:06.678309", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "==((====))== Unsloth - 2x faster free finetuning | Num GPUs = 1\n", " \\\\ /| Num examples = 183 | Num Epochs = 1\n", "O^O/ \\_/ \\ Batch size per device = 2 | Gradient Accumulation steps = 4\n", "\\ / Total batch size = 8 | Total steps = 23\n", " \"-____-\" Number of trainable parameters = 134,348,800\n", "🦥 Unsloth needs about 1-3 minutes to load everything - please wait!\n" ] }, { "data": { "text/html": [ "\n", "
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" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "trainer_stats = trainer.train()" ] }, { "cell_type": "code", "execution_count": 8, "id": "55b8762b", "metadata": { "cellView": "form", "execution": { "iopub.execute_input": "2025-01-01T01:55:19.678426Z", "iopub.status.busy": "2025-01-01T01:55:19.677755Z", "iopub.status.idle": "2025-01-01T01:55:19.684152Z", "shell.execute_reply": "2025-01-01T01:55:19.683381Z" }, "id": "pCqnaKmlO1U9", "outputId": "cf63d152-e152-468c-ba0d-938e0d2f71a0", "papermill": { "duration": 0.065945, "end_time": "2025-01-01T01:55:19.685303", "exception": false, "start_time": "2025-01-01T01:55:19.619358", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "848.9055 seconds used for training.\n", "14.15 minutes used for training.\n", "Peak reserved memory = 11.381 GB.\n", "Peak reserved memory for training = 3.5 GB.\n", "Peak reserved memory % of max memory = 77.206 %.\n", "Peak reserved memory for training % of max memory = 23.743 %.\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", "id": "986aa13e", "metadata": { "id": "ekOmTR1hSNcr", "papermill": { "duration": 0.057392, "end_time": "2025-01-01T01:55:19.838995", "exception": false, "start_time": "2025-01-01T01:55:19.781603", "status": "completed" }, "tags": [] }, "source": [ "\n", "### Inference\n", "Let's run the model! You can change the instruction and input - leave the output blank!" ] }, { "cell_type": "code", "execution_count": 9, "id": "111e0b71", "metadata": { "execution": { "iopub.execute_input": "2025-01-01T01:55:19.955392Z", "iopub.status.busy": "2025-01-01T01:55:19.955102Z", "iopub.status.idle": "2025-01-01T01:55:19.959163Z", "shell.execute_reply": "2025-01-01T01:55:19.958475Z" }, "id": "kR3gIAX-SM2q", "outputId": "5b71f982-38c0-44c8-a4e5-58cd20b5a585", "papermill": { "duration": 0.063692, "end_time": "2025-01-01T01:55:19.960308", "exception": false, "start_time": "2025-01-01T01:55:19.896616", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "if False:\n", " # alpaca_prompt = Copied from above\n", " FastVisionModel.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", "id": "04376d0a", "metadata": { "id": "CrSvZObor0lY", "papermill": { "duration": 0.057197, "end_time": "2025-01-01T01:55:20.074913", "exception": false, "start_time": "2025-01-01T01:55:20.017716", "status": "completed" }, "tags": [] }, "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, "id": "ddb50ced", "metadata": { "execution": { "iopub.execute_input": "2025-01-01T01:55:20.191902Z", "iopub.status.busy": "2025-01-01T01:55:20.191675Z", "iopub.status.idle": "2025-01-01T01:55:20.195589Z", "shell.execute_reply": "2025-01-01T01:55:20.194954Z" }, "id": "e2pEuRb1r2Vg", "outputId": "084aab62-2122-436a-c0cb-8871986640eb", "papermill": { "duration": 0.062828, "end_time": "2025-01-01T01:55:20.196639", "exception": false, "start_time": "2025-01-01T01:55:20.133811", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "if False:\n", " # alpaca_prompt = Copied from above\n", " FastVisionModel.for_inference(model) # Enable native 2x faster inference\n", " inputs = tokenizer(\n", " [\n", " alpaca_prompt.format(\n", " \"Continue the fibonnaci sequence.\", # instruction\n", " \"1, 1, 2, 3, 5, 8\", # input\n", " \"\", # output - leave this blank for generation!\n", " )\n", " ], return_tensors = \"pt\").to(\"cuda\")\n", "\n", " from transformers import TextStreamer\n", " text_streamer = TextStreamer(tokenizer)\n", " _ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 128)" ] }, { "cell_type": "markdown", "id": "55dafc1e", "metadata": { "id": "uMuVrWbjAzhc", "papermill": { "duration": 0.057403, "end_time": "2025-01-01T01:55:20.312023", "exception": false, "start_time": "2025-01-01T01:55:20.254620", "status": "completed" }, "tags": [] }, "source": [ "\n", "### Saving, loading finetuned models\n", "To save the final model as LoRA adapters, either use Huggingface's `push_to_hub` for an online save or `save_pretrained` for a local save.\n", "\n", "**[NOTE]** This ONLY saves the LoRA adapters, and not the full model. To save to 16bit or GGUF, scroll down!" ] }, { "cell_type": "code", "execution_count": 11, "id": "aeb56f25", "metadata": { "execution": { "iopub.execute_input": "2025-01-01T01:55:20.427410Z", "iopub.status.busy": "2025-01-01T01:55:20.427199Z", "iopub.status.idle": "2025-01-01T01:55:26.188793Z", "shell.execute_reply": "2025-01-01T01:55:26.188001Z" }, "id": "upcOlWe7A1vc", "papermill": { "duration": 5.820743, "end_time": "2025-01-01T01:55:26.190033", "exception": false, "start_time": "2025-01-01T01:55:20.369290", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "cbde2bfd51214dc6b88f049830a8902c", "version_major": 2, "version_minor": 0 }, "text/plain": [ "README.md: 0%| | 0.00/631 [00:00\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;31m# Merge to 4bit\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0;32mif\u001b[0m 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\u001b[0mtemp_file\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msave_directory\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnew_use_temp_file\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 534\u001b[0m \u001b[0mlow_disk_space_usage\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_shard_size_in_bytes\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 535\u001b[0;31m \u001b[0;34m)\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mprepare_saving\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 536\u001b[0m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 537\u001b[0m \u001b[0msave_directory\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msave_directory\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/unsloth_zoo/saving_utils.py\u001b[0m in \u001b[0;36mprepare_saving\u001b[0;34m(model, save_directory, push_to_hub, max_shard_size, private, token, output_dtype, merge_into_original, low_disk_space_usage, min_size_in_bytes, use_temp_file)\u001b[0m\n\u001b[1;32m 460\u001b[0m \u001b[0;31m# Too small - try using the temporary file system (sometimes large like Kaggle)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 461\u001b[0m \u001b[0mtry_temp_file\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtempfile\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTemporaryDirectory\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mignore_cleanup_errors\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 462\u001b[0;31m \u001b[0mtry_save_directory\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtemp_file\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 463\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 464\u001b[0m \u001b[0mtotal\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mused\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfree\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mshutil\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdisk_usage\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msave_directory\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mAttributeError\u001b[0m: 'NoneType' object has no attribute 'name'" ] } ], "source": [ "# Merge to 16bit\n", "if False: model.save_pretrained_merged(\"model\", tokenizer, save_method = \"merged_16bit\",)\n", "if False: model.push_to_hub_merged(\"scoliono/miku_vision_instruct-3.2-11b\", tokenizer, save_method = \"merged_16bit\", token = hf_token)\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(\"scoliono/miku_vision_instruct-3.2-11b\", tokenizer, save_method = \"merged_4bit\", token = hf_token)\n", "\n", "# Just LoRA adapters\n", "if False: model.save_pretrained_merged(\"model\", tokenizer, save_method = \"lora\",)\n", "if False: model.push_to_hub_merged(\"hf/model\", tokenizer, save_method = \"lora\", token = \"\")" ] }, { "cell_type": "markdown", "id": "0614a39c", "metadata": { "id": "TCv4vXHd61i7", "papermill": { "duration": null, "end_time": null, "exception": null, "start_time": null, "status": "pending" }, "tags": [] }, "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": null, "id": "2641cfb7", "metadata": { "execution": { "iopub.status.busy": "2024-05-31T21:47:32.211354Z", "iopub.status.idle": "2024-05-31T21:47:32.211744Z", "shell.execute_reply": "2024-05-31T21:47:32.211556Z", "shell.execute_reply.started": "2024-05-31T21:47:32.211541Z" }, "id": "FqfebeAdT073", "papermill": { "duration": null, "end_time": null, "exception": null, "start_time": null, "status": "pending" }, "tags": [] }, "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", "id": "a66fdd0a", "metadata": { "id": "bDp0zNpwe6U_", "papermill": { "duration": null, "end_time": null, "exception": null, "start_time": null, "status": "pending" }, "tags": [] }, "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", "id": "66b076c5", "metadata": { "id": "Zt9CHJqO6p30", "papermill": { "duration": null, "end_time": null, "exception": null, "start_time": null, "status": "pending" }, "tags": [] }, "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", "

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