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{
"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",
"<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)."
]
},
{
"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": [
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"Installing collected packages: mpmath, triton, sympy, nvidia-nvtx-cu12, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, 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",
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"\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",
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" 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",
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"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."
]
},
{
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"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"
]
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],
"source": [
"from unsloth import FastVisionModel\n",
"import torch\n",
"max_seq_length = 2048 # Choose any! We auto support RoPE Scaling internally!\n",
"dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+\n",
"load_in_4bit = True # Use 4bit quantization to reduce memory usage. Can be False.\n",
"\n",
"# 4bit pre quantized models we support for 4x faster downloading + 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/llama-2-13b-bnb-4bit\",\n",
" \"unsloth/codellama-34b-bnb-4bit\",\n",
" \"unsloth/tinyllama-bnb-4bit\",\n",
" \"unsloth/llama-3-8b-bnb-4bit\",\n",
" \"unsloth/llama-3-70b-bnb-4bit\",\n",
"] # More models at https://huggingface.co/unsloth\n",
"\n",
"model, tokenizer = FastVisionModel.from_pretrained(\n",
" model_name = \"unsloth/Llama-3.2-11B-Vision-Instruct-unsloth-bnb-4bit\", # Choose ANY! eg teknium/OpenHermes-2.5-Mistral-7B\n",
" use_gradient_checkpointing = \"unsloth\", # True or \"unsloth\" for long context\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",
"id": "f0a8b85e",
"metadata": {
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"status": "completed"
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"tags": []
},
"source": [
"We now add LoRA adapters so we only need to update 1 to 10% of all parameters!"
]
},
{
"cell_type": "code",
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"source": [
"model = FastVisionModel.get_peft_model(\n",
" model,\n",
" r = 32, # Choose any number > 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",
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"tags": []
},
"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)."
]
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"Generating train split: 0%| | 0/183 [00:00<?, ? examples/s]"
]
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},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/local/lib/python3.10/dist-packages/PIL/Image.py:3368: DecompressionBombWarning: Image size (101183704 pixels) exceeds limit of 89478485 pixels, could be decompression bomb DOS attack.\n",
" warnings.warn(\n",
"/usr/local/lib/python3.10/dist-packages/PIL/Image.py:3368: DecompressionBombWarning: Image size (101192432 pixels) exceeds limit of 89478485 pixels, could be decompression bomb DOS attack.\n",
" warnings.warn(\n"
]
}
],
"source": [
"from datasets import load_dataset, concatenate_datasets\n",
"from unsloth.chat_templates import get_chat_template\n",
"from PIL import Image\n",
"import io\n",
"import json\n",
"import random\n",
"\n",
"booru = load_dataset(\"scoliono/fembooru\", split=\"train\")\n",
"\n",
"visual_instructions = [\"Describe this image.\", \"What's going on in this picture?\", \"What is this?\", \"What's happening in this image?\", \"Tell me what's in this picture.\", \"Describe what's happening here.\"]\n",
"\n",
"def convert_to_conversation(sample):\n",
" conversation = [\n",
" { \"role\": \"user\",\n",
" \"content\" : [\n",
" {\"type\" : \"text\", \"text\" : random.choice(visual_instructions)},\n",
" {\"type\" : \"image\", \"image\" : Image.open(io.BytesIO(sample[\"image_data\"]))} ]\n",
" },\n",
" { \"role\" : \"assistant\",\n",
" \"content\" : [\n",
" {\"type\" : \"text\", \"text\" : sample[\"summary\"]} ]\n",
" },\n",
" ]\n",
" return { \"messages\" : conversation }\n",
"\n",
"def formatting_prompts_func(convos):\n",
" texts = [tokenizer.apply_chat_template(convo, tokenize = False, add_generation_prompt = False) for convo in convos]\n",
" return { \"text\" : texts, }\n",
"\n",
"with open(\"/kaggle/input/the-group-chat/output-10k-c-dropout-nonames-replies.json\") as chatfile:\n",
" convos = [json.loads(j) for j in chatfile.readlines()]\n",
"\n",
"with open(\"/kaggle/input/toxicqa/toxicQAfinal.json\") as chatfile:\n",
" convos += [json.loads(j) for j in chatfile.readlines()]\n",
"\n",
"# shim to convert our text-only json format into one consistent with multimodal training data\n",
"def text_to_visual_convo(convo):\n",
" return [\n",
" { \"role\": msg[\"role\"], \"content\": [{\"type\": \"text\", \"text\": msg[\"content\"]}] }\n",
" for msg in convo\n",
" ]\n",
"\n",
"fmt_convos = [ {\"messages\": text_to_visual_convo(convo)} for convo in convos ]\n",
"fmt_booru = [ convert_to_conversation(sample) for sample in booru ]\n",
"dataset = fmt_booru #+ fmt_convos"
]
},
{
"cell_type": "markdown",
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},
"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`!"
]
},
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"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",
")"
]
},
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"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": {
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},
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"outputId": "3cf26aac-6042-4458-c4a6-d8849efb6a95",
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"duration": 852.881724,
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"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",
" <div>\n",
" \n",
" <progress value='23' max='23' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
" [23/23 12:53, Epoch 1/1]\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>2.972900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2</td>\n",
" <td>2.893800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>3</td>\n",
" <td>2.855700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>4</td>\n",
" <td>2.607000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>5</td>\n",
" <td>2.517700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>6</td>\n",
" <td>1.886900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>7</td>\n",
" <td>1.887000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>8</td>\n",
" <td>1.712500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>9</td>\n",
" <td>1.482400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>10</td>\n",
" <td>1.447500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>11</td>\n",
" <td>1.375100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>12</td>\n",
" <td>1.479200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>13</td>\n",
" <td>1.241500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>14</td>\n",
" <td>1.298700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>15</td>\n",
" <td>1.174100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>16</td>\n",
" <td>1.193000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>17</td>\n",
" <td>1.151100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>18</td>\n",
" <td>1.173700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>19</td>\n",
" <td>0.891000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>20</td>\n",
" <td>0.976000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>21</td>\n",
" <td>1.198000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>22</td>\n",
" <td>1.212600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>23</td>\n",
" <td>1.119600</td>\n",
" </tr>\n",
" </tbody>\n",
"</table><p>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"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": [
"<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,
"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": [
"<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": 11,
"id": "aeb56f25",
"metadata": {
"execution": {
"iopub.execute_input": "2025-01-01T01:55:20.427410Z",
"iopub.status.busy": "2025-01-01T01:55:20.427199Z",
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},
"id": "upcOlWe7A1vc",
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"duration": 5.820743,
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"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<?, ?B/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "afa97f5c274d4598872842c4c1ea9441",
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"version_minor": 0
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"text/plain": [
" 0%| | 0/1 [00:00<?, ?it/s]"
]
},
"metadata": {},
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},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "481b462473694c2389decadd3ceca3f0",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"adapter_model.safetensors: 0%| | 0.00/538M [00:00<?, ?B/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Saved model to https://huggingface.co/scoliono/groupchat_lora_vision_instruct-3.2-11b\n"
]
}
],
"source": [
"#model.save_pretrained(\"lora_model\") # Local saving\n",
"from kaggle_secrets import UserSecretsClient\n",
"user_secrets = UserSecretsClient()\n",
"hf_token = user_secrets.get_secret(\"hf_token\")\n",
"\n",
"model.push_to_hub(\"scoliono/groupchat_lora_vision_instruct-3.2-11b\", token = hf_token)"
]
},
{
"cell_type": "markdown",
"id": "78938644",
"metadata": {
"id": "AEEcJ4qfC7Lp",
"papermill": {
"duration": 0.057915,
"end_time": "2025-01-01T01:55:26.307708",
"exception": false,
"start_time": "2025-01-01T01:55:26.249793",
"status": "completed"
},
"tags": []
},
"source": [
"Now if you want to load the LoRA adapters we just saved for inference, set `False` to `True`:"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "6e47051d",
"metadata": {
"execution": {
"iopub.execute_input": "2025-01-01T01:55:26.424453Z",
"iopub.status.busy": "2025-01-01T01:55:26.424204Z",
"iopub.status.idle": "2025-01-01T01:55:26.428411Z",
"shell.execute_reply": "2025-01-01T01:55:26.427776Z"
},
"id": "MKX_XKs_BNZR",
"outputId": "05e5a193-dab0-41db-e07c-4b3afbdd7932",
"papermill": {
"duration": 0.06427,
"end_time": "2025-01-01T01:55:26.429493",
"exception": false,
"start_time": "2025-01-01T01:55:26.365223",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"if False:\n",
" from unsloth import FastLanguageModel\n",
" model, tokenizer = FastLanguageModel.from_pretrained(\n",
" model_name = \"scoliono/groupchat_lora_abliterated_instruct-3.1-8b\", # YOUR MODEL YOU USED FOR TRAINING\n",
" max_seq_length = max_seq_length,\n",
" dtype = dtype,\n",
" load_in_4bit = load_in_4bit,\n",
" )\n",
" FastLanguageModel.for_inference(model) # Enable native 2x faster inference\n",
"\n",
" # alpaca_prompt = You MUST copy from above!\n",
"\n",
" inputs = tokenizer(\n",
" [\n",
" alpaca_prompt.format(\n",
" \"What is a famous tall tower in Paris?\", # instruction\n",
" \"\", # input\n",
" \"\", # output - leave this blank for generation!\n",
" )\n",
" ], return_tensors = \"pt\").to(\"cuda\")\n",
"\n",
" outputs = model.generate(**inputs, max_new_tokens = 64, use_cache = True)\n",
" tokenizer.batch_decode(outputs)"
]
},
{
"cell_type": "markdown",
"id": "c5fbcf25",
"metadata": {
"id": "QQMjaNrjsU5_",
"papermill": {
"duration": 0.057403,
"end_time": "2025-01-01T01:55:26.545335",
"exception": false,
"start_time": "2025-01-01T01:55:26.487932",
"status": "completed"
},
"tags": []
},
"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": 13,
"id": "ea72a298",
"metadata": {
"execution": {
"iopub.execute_input": "2025-01-01T01:55:26.662017Z",
"iopub.status.busy": "2025-01-01T01:55:26.661744Z",
"iopub.status.idle": "2025-01-01T01:55:26.665378Z",
"shell.execute_reply": "2025-01-01T01:55:26.664554Z"
},
"id": "yFfaXG0WsQuE",
"papermill": {
"duration": 0.06326,
"end_time": "2025-01-01T01:55:26.666488",
"exception": false,
"start_time": "2025-01-01T01:55:26.603228",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"if False:\n",
" # I highly do NOT suggest - use Unsloth if possible\n",
" from peft import AutoPeftModelForCausalLM\n",
" from transformers import AutoTokenizer\n",
" model = AutoPeftModelForCausalLM.from_pretrained(\n",
" \"groupchat_lora_abliterated_instruct-3.1-8b\", # YOUR MODEL YOU USED FOR TRAINING\n",
" load_in_4bit = load_in_4bit,\n",
" )\n",
" tokenizer = AutoTokenizer.from_pretrained(\"groupchat_lora_abliterated_instruct-3.1-8b\")"
]
},
{
"cell_type": "markdown",
"id": "d8795396",
"metadata": {
"id": "f422JgM9sdVT",
"papermill": {
"duration": 0.05765,
"end_time": "2025-01-01T01:55:26.782572",
"exception": false,
"start_time": "2025-01-01T01:55:26.724922",
"status": "completed"
},
"tags": []
},
"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": 14,
"id": "4d481a05",
"metadata": {
"execution": {
"iopub.execute_input": "2025-01-01T01:55:26.899112Z",
"iopub.status.busy": "2025-01-01T01:55:26.898836Z",
"iopub.status.idle": "2025-01-01T01:55:28.271476Z",
"shell.execute_reply": "2025-01-01T01:55:28.270269Z"
},
"id": "iHjt_SMYsd3P",
"papermill": {
"duration": 1.43243,
"end_time": "2025-01-01T01:55:28.272613",
"exception": true,
"start_time": "2025-01-01T01:55:26.840183",
"status": "failed"
},
"tags": []
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"No files have been modified since last commit. Skipping to prevent empty commit.\n"
]
},
{
"ename": "AttributeError",
"evalue": "'NoneType' object has no attribute 'name'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-14-5249ff9d6871>\u001b[0m in \u001b[0;36m<cell line: 7>\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 \u001b[0;32mFalse\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave_pretrained_merged\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"model\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtokenizer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msave_method\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"merged_4bit\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0;32mif\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpush_to_hub_merged\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"scoliono/miku_vision_instruct-3.2-11b\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtokenizer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msave_method\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"merged_4bit\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtoken\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhf_token\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 8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[0;31m# Just LoRA adapters\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/save.py\u001b[0m in \u001b[0;36munsloth_generic_push_to_hub_merged\u001b[0;34m(self, repo_id, tokenizer, save_method, use_temp_dir, commit_message, private, token, max_shard_size, create_pr, safe_serialization, revision, commit_description, tags, temporary_location, maximum_memory_usage)\u001b[0m\n\u001b[1;32m 2229\u001b[0m \u001b[0;32mdel\u001b[0m \u001b[0marguments\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"self\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2230\u001b[0m \u001b[0;32mdel\u001b[0m \u001b[0marguments\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"repo_id\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2231\u001b[0;31m \u001b[0munsloth_generic_save\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0marguments\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 2232\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0m_\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2233\u001b[0m \u001b[0mgc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcollect\u001b[0m\u001b[0;34m(\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/torch/utils/_contextlib.py\u001b[0m in \u001b[0;36mdecorate_context\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 114\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mdecorate_context\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 115\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mctx_factory\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 116\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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 117\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 118\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mdecorate_context\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/save.py\u001b[0m in \u001b[0;36munsloth_generic_save\u001b[0;34m(model, tokenizer, save_directory, save_method, push_to_hub, token, is_main_process, state_dict, save_function, max_shard_size, safe_serialization, variant, save_peft_format, use_temp_dir, commit_message, private, create_pr, revision, commit_description, tags, temporary_location, maximum_memory_usage)\u001b[0m\n\u001b[1;32m 2130\u001b[0m ):\n\u001b[1;32m 2131\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mtoken\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mpush_to_hub\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mtoken\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_token\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2132\u001b[0;31m merge_and_overwrite_lora(\n\u001b[0m\u001b[1;32m 2133\u001b[0m \u001b[0mget_model_name\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2134\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[0;32m/usr/local/lib/python3.10/dist-packages/torch/utils/_contextlib.py\u001b[0m in \u001b[0;36mdecorate_context\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 114\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mdecorate_context\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 115\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mctx_factory\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 116\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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 117\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 118\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mdecorate_context\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;36mmerge_and_overwrite_lora\u001b[0;34m(get_model_name, model, tokenizer, save_directory, push_to_hub, private, token, output_dtype, low_disk_space_usage, use_temp_file)\u001b[0m\n\u001b[1;32m 533\u001b[0m \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",
"<div class=\"align-center\">\n",
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