{ "cells": [ { "cell_type": "markdown", "id": "c19cd9ee", "metadata": { "id": "n5NrROnmsJP4", "papermill": { "duration": 0.021851, "end_time": "2026-04-09T14:46:31.545467+00:00", "exception": false, "start_time": "2026-04-09T14:46:31.523616+00:00", "status": "completed" }, "tags": [] }, "source": [ "To run this, press \"*Runtime*\" and press \"*Run all*\" on a Google Colab A100 instance!\n", "
\n", "\n", "\n", " Join Discord if you need help + ⭐ Star us on Github ⭐\n", "
\n", "\n", "To install Unsloth on your local device, follow [our guide](https://unsloth.ai/docs/get-started/install). This notebook is licensed [LGPL-3.0](https://github.com/unslothai/notebooks?tab=LGPL-3.0-1-ov-file#readme).\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" ] }, { "cell_type": "markdown", "id": "c5dbe248", "metadata": { "id": "jjzmSqm7sJP4", "papermill": { "duration": 0.018413, "end_time": "2026-04-09T14:46:31.583432+00:00", "exception": false, "start_time": "2026-04-09T14:46:31.565019+00:00", "status": "completed" }, "tags": [] }, "source": [ "### News" ] }, { "cell_type": "markdown", "id": "0cad8cc5", "metadata": { "id": "WDatJHaBsJP5", "papermill": { "duration": 0.018268, "end_time": "2026-04-09T14:46:31.620424+00:00", "exception": false, "start_time": "2026-04-09T14:46:31.602156+00:00", "status": "completed" }, "tags": [] }, "source": [ "Introducing **Unsloth Studio** - a new open source, no-code web UI to train and run LLMs. [Blog](https://unsloth.ai/docs/new/studio) • [Notebook](https://colab.research.google.com/github/unslothai/unsloth/blob/main/studio/Unsloth_Studio_Colab.ipynb)\n", "\n", "\n", "\n", "\n", "
\"Unsloth
Train models — no code needed		\"Unsloth
Run GGUF models on Mac, Windows & Linux
\n", "\n", "Train MoEs - DeepSeek, GLM, Qwen and gpt-oss 12x faster with 35% less VRAM. [Blog](https://unsloth.ai/docs/new/faster-moe)\n", "\n", "Ultra Long-Context Reinforcement Learning is here with 7x more context windows! [Blog](https://unsloth.ai/docs/new/grpo-long-context)\n", "\n", "New in Reinforcement Learning: [FP8 RL](https://unsloth.ai/docs/new/fp8-reinforcement-learning) • [Vision RL](https://unsloth.ai/docs/new/vision-reinforcement-learning-vlm-rl) • [Standby](https://unsloth.ai/docs/basics/memory-efficient-rl) • [gpt-oss RL](https://unsloth.ai/docs/new/gpt-oss-reinforcement-learning)\n", "\n", "Visit our docs for all our [model uploads](https://unsloth.ai/docs/get-started/unsloth-model-catalog) and [notebooks](https://unsloth.ai/docs/get-started/unsloth-notebooks)." ] }, { "cell_type": "markdown", "id": "acd0984d", "metadata": { "id": "9TnXzZYMsJP5", "papermill": { "duration": 0.018422, "end_time": "2026-04-09T14:46:31.657445+00:00", "exception": false, "start_time": "2026-04-09T14:46:31.639023+00:00", "status": "completed" }, "tags": [] }, "source": [ "### Installation" ] }, { "cell_type": "code", "execution_count": 1, "id": "cf52976a", "metadata": { "execution": { "iopub.execute_input": "2026-04-09T14:46:31.696042Z", "iopub.status.busy": "2026-04-09T14:46:31.695595Z", "iopub.status.idle": "2026-04-09T14:46:36.809838Z", "shell.execute_reply": "2026-04-09T14:46:36.808831Z" }, "id": "B2T3Z8F2sJP5", "papermill": { "duration": 5.1357, "end_time": "2026-04-09T14:46:36.811554+00:00", "exception": false, "start_time": "2026-04-09T14:46:31.675854+00:00", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "%%capture\n", "try: import numpy, PIL; _numpy = f\"numpy=={numpy.__version__}\"; _pil = f\"pillow=={PIL.__version__}\"\n", "except: _numpy = \"numpy\"; _pil = \"pillow\"\n", "!uv pip install -qqq \\\n", " \"torch>=2.8.0\" \"triton>=3.4.0\" {_numpy} {_pil} torchvision bitsandbytes \\\n", " unsloth \"unsloth_zoo>=2026.4.6\" transformers==5.5.0 torchcodec timm" ] }, { "cell_type": "markdown", "id": "77bdc102", "metadata": { "id": "TGMWlrRdzwgf", "papermill": { "duration": 0.018575, "end_time": "2026-04-09T14:46:36.849477+00:00", "exception": false, "start_time": "2026-04-09T14:46:36.830902+00:00", "status": "completed" }, "tags": [] }, "source": [ "### Unsloth\n", "\n", "`FastModel` supports loading nearly any model now! This includes Vision and Text models!" ] }, { "cell_type": "code", "execution_count": 2, "id": "1c116df3", "metadata": { "execution": { "iopub.execute_input": "2026-04-09T14:46:36.887792Z", "iopub.status.busy": "2026-04-09T14:46:36.887484Z", "iopub.status.idle": "2026-04-09T14:56:32.181348Z", "shell.execute_reply": "2026-04-09T14:56:32.180581Z" }, "id": "-Xbb0cuLzwgf", "outputId": "3d9968a4-cb68-4112-aa41-29a78c0c8ae0", "papermill": { "duration": 595.315071, "end_time": "2026-04-09T14:56:32.183208+00:00", "exception": false, "start_time": "2026-04-09T14:46:36.868137+00:00", "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 2026.4.4: Fast Gemma4 patching. Transformers: 5.5.0.\n", " \\\\ /| Tesla T4. Num GPUs = 2. Max memory: 14.563 GB. Platform: Linux.\n", "O^O/ \\_/ \\ Torch: 2.10.0+cu128. CUDA: 7.5. CUDA Toolkit: 12.8. Triton: 3.6.0\n", "\\ / Bfloat16 = FALSE. FA [Xformers = 0.0.35. FA2 = False]\n", " \"-____-\" Free 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": "9979852f7f1e4abfaab19c8fe7df76bf", "version_major": 2, "version_minor": 0 }, "text/plain": [ "model.safetensors.index.json: 0.00B [00:00, ?B/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "000e4832d81f42fd81d516e06acf2981", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Downloading (incomplete total...): 0.00B [00:00, ?B/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "3c3e50bf8e254e9e9f5b14e4bc314c03", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Fetching 2 files: 0%| | 0/2 [00:00" ] }, { "cell_type": "code", "execution_count": 4, "id": "3a992f62", "metadata": { "execution": { "iopub.execute_input": "2026-04-09T14:56:32.361453Z", "iopub.status.busy": "2026-04-09T14:56:32.360986Z", "iopub.status.idle": "2026-04-09T14:59:10.830340Z", "shell.execute_reply": "2026-04-09T14:59:10.829715Z" }, "id": "9jGeSb9bWe0k", "outputId": "e95da582-2668-4936-9aec-3ffdde2bb771", "papermill": { "duration": 158.492174, "end_time": "2026-04-09T14:59:10.832047+00:00", "exception": false, "start_time": "2026-04-09T14:56:32.339873+00:00", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The animal in the image is a sloth. Sloths have appeared in several popular films, most notably:\n", "\n", "* **Zootopia (2016):** Featuring the character Flash, a slow-moving sloth who works at the Department of Mammal Vehicles.\n", "* **Ice Age (2002):** Featuring Sid, a ground sloth who is one of the main protagonists of the series.\n" ] } ], "source": [ "sloth_link = \"https://files.worldwildlife.org/wwfcmsprod/images/Sloth_Sitting_iStock_3_12_2014/story_full_width/8l7pbjmj29_iStock_000011145477Large_mini__1_.jpg\"\n", "\n", "messages = [{\n", " \"role\" : \"user\",\n", " \"content\": [\n", " { \"type\": \"image\", \"image\" : sloth_link },\n", " { \"type\": \"text\", \"text\" : \"Which films does this animal feature in?\" }\n", " ]\n", "}]\n", "# You might have to wait 1 minute for Unsloth's auto compiler\n", "do_gemma_4_inference(messages, max_new_tokens = 256)" ] }, { "cell_type": "markdown", "id": "0a1450eb", "metadata": { "id": "eh0BzbZPWtRD", "papermill": { "duration": 0.021884, "end_time": "2026-04-09T14:59:10.878105+00:00", "exception": false, "start_time": "2026-04-09T14:59:10.856221+00:00", "status": "completed" }, "tags": [] }, "source": [ "Let's make a poem about sloths!" ] }, { "cell_type": "code", "execution_count": 5, "id": "a72ce8c6", "metadata": { "execution": { "iopub.execute_input": "2026-04-09T14:59:10.923282Z", "iopub.status.busy": "2026-04-09T14:59:10.923007Z", "iopub.status.idle": "2026-04-09T14:59:48.128345Z", "shell.execute_reply": "2026-04-09T14:59:48.127693Z" }, "id": "R3ExuK8cWuT3", "outputId": "3a0a6496-79ef-4921-8dd5-96ba5ba77fc9", "papermill": { "duration": 37.229725, "end_time": "2026-04-09T14:59:48.129962+00:00", "exception": false, "start_time": "2026-04-09T14:59:10.900237+00:00", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "In the emerald heart of the canopy high,\n", "Where the orchids bloom and the parrots fly,\n", "Dwells a gentle soul in a velvet coat,\n", "In a slow-motion world, a drifting boat.\n", "\n", "With curved, steady claws and a sleepy gaze,\n", "He wanders through gold-dappled, humid haze.\n", "No rush for the fruit, no race for the prize,\n", "Just the soft, steady blink of heavy-lidded eyes.\n", "\n", "He is the master of the patient art,\n", "With a rhythmic beat in a quiet heart.\n", "While the monkeys chatter and the jaguars leap,\n", "The sloth\n" ] } ], "source": [ "messages = [{\n", " \"role\": \"user\",\n", " \"content\": [{ \"type\" : \"text\",\n", " \"text\" : \"Write a poem about sloths.\" }]\n", "}]\n", "do_gemma_4_inference(messages)" ] }, { "cell_type": "markdown", "id": "e77fbff4", "metadata": { "id": "Bw5XPyYFajyM", "papermill": { "duration": 0.025126, "end_time": "2026-04-09T14:59:48.181360+00:00", "exception": false, "start_time": "2026-04-09T14:59:48.156234+00:00", "status": "completed" }, "tags": [] }, "source": [ "# Let's finetune Gemma 4!\n", "\n", "You can finetune the vision and text parts for now through selection - the audio part can also be finetuned - we're working to make it selectable as well!" ] }, { "cell_type": "markdown", "id": "acb794c4", "metadata": { "id": "SXd9bTZd1aaL", "papermill": { "duration": 0.025143, "end_time": "2026-04-09T14:59:48.231346+00:00", "exception": false, "start_time": "2026-04-09T14:59:48.206203+00:00", "status": "completed" }, "tags": [] }, "source": [ "We now add LoRA adapters so we only need to update a small amount of parameters!" ] }, { "cell_type": "code", "execution_count": 6, "id": "6fd5b894", "metadata": { "execution": { "iopub.execute_input": "2026-04-09T14:59:48.282613Z", "iopub.status.busy": "2026-04-09T14:59:48.282332Z", "iopub.status.idle": "2026-04-09T14:59:57.633319Z", "shell.execute_reply": "2026-04-09T14:59:57.632673Z" }, "id": "6bZsfBuZDeCL", "papermill": { "duration": 9.379235, "end_time": "2026-04-09T14:59:57.635217+00:00", "exception": false, "start_time": "2026-04-09T14:59:48.255982+00:00", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "model = FastModel.get_peft_model(\n", " model,\n", " finetune_vision_layers = False, # Turn off for just text!\n", " finetune_language_layers = True, # Should leave on!\n", " finetune_attention_modules = True, # Attention good for GRPO\n", " finetune_mlp_modules = True, # Should leave on always!\n", "\n", " r = 8, # Larger = higher accuracy, but might overfit\n", " lora_alpha = 8, # Recommended alpha == r at least\n", " lora_dropout = 0,\n", " bias = \"none\",\n", " random_state = 3407,\n", ")" ] }, { "cell_type": "markdown", "id": "5dc4ec82", "metadata": { "id": "vITh0KVJ10qX", "papermill": { "duration": 0.024706, "end_time": "2026-04-09T14:59:57.686384+00:00", "exception": false, "start_time": "2026-04-09T14:59:57.661678+00:00", "status": "completed" }, "tags": [] }, "source": [ "\n", "### Data Prep\n", "We now use the `Gemma-4` format for conversation style finetunes. We use [Maxime Labonne's FineTome-100k](https://huggingface.co/datasets/mlabonne/FineTome-100k) dataset in ShareGPT style. Gemma-4 renders multi turn conversations like below:\n", "\n", "```\n", "<|turn>user\n", "Hello\n", "<|turn>model\n", "Hey there!\n", "```\n", "We use our `get_chat_template` function to get the correct chat template. We support `zephyr, chatml, mistral, llama, alpaca, vicuna, vicuna_old, phi3, llama3, phi4, qwen2.5, gemma3, gemma-4` and more." ] }, { "cell_type": "code", "execution_count": 7, "id": "4fc98513", "metadata": { "execution": { "iopub.execute_input": "2026-04-09T14:59:57.737313Z", "iopub.status.busy": "2026-04-09T14:59:57.737037Z", "iopub.status.idle": "2026-04-09T14:59:57.741858Z", "shell.execute_reply": "2026-04-09T14:59:57.741196Z" }, "id": "LjY75GoYUCB8", "papermill": { "duration": 0.032282, "end_time": "2026-04-09T14:59:57.743246+00:00", "exception": false, "start_time": "2026-04-09T14:59:57.710964+00:00", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "from unsloth.chat_templates import get_chat_template\n", "tokenizer = get_chat_template(\n", " tokenizer,\n", " chat_template = \"gemma-4-thinking\",\n", ")" ] }, { "cell_type": "markdown", "id": "63328983", "metadata": { "id": "ZQkXuGYxbJ-e", "papermill": { "duration": 0.024742, "end_time": "2026-04-09T14:59:57.793028+00:00", "exception": false, "start_time": "2026-04-09T14:59:57.768286+00:00", "status": "completed" }, "tags": [] }, "source": [ "We get the first 3000 rows of the dataset" ] }, { "cell_type": "code", "execution_count": 8, "id": "8e94eaa0", "metadata": { "execution": { "iopub.execute_input": "2026-04-09T14:59:57.844051Z", "iopub.status.busy": "2026-04-09T14:59:57.843793Z", "iopub.status.idle": "2026-04-09T15:00:01.697881Z", "shell.execute_reply": "2026-04-09T15:00:01.696967Z" }, "id": "Mkq4RvEq7FQr", "outputId": "43449058-686e-430f-c009-c3e795af23ac", "papermill": { "duration": 3.881705, "end_time": "2026-04-09T15:00:01.699372+00:00", "exception": false, "start_time": "2026-04-09T14:59:57.817667+00:00", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "60adcfaac9cc483284f4cdc1cead9de4", "version_major": 2, "version_minor": 0 }, "text/plain": [ "README.md: 0%| | 0.00/982 [00:00` token using removeprefix(`''`) since we're finetuning. The Processor will add this token before training and the model expects only one." ] }, { "cell_type": "code", "execution_count": 11, "id": "5cc67ac7", "metadata": { "execution": { "iopub.execute_input": "2026-04-09T15:00:03.355512Z", "iopub.status.busy": "2026-04-09T15:00:03.354667Z", "iopub.status.idle": "2026-04-09T15:00:07.915139Z", "shell.execute_reply": "2026-04-09T15:00:07.914463Z" }, "id": "1ahE8Ys37JDJ", "outputId": "11a09b9e-9fab-45cc-aef4-81b027ebc64b", "papermill": { "duration": 4.589949, "end_time": "2026-04-09T15:00:07.916517+00:00", "exception": false, "start_time": "2026-04-09T15:00:03.326568+00:00", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "c9cb90312b97442bb895e73d6329c5df", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Map: 0%| | 0/3000 [00:00') for convo in convos]\n", " return { \"text\" : texts, }\n", "\n", "dataset = dataset.map(formatting_prompts_func, batched = True)" ] }, { "cell_type": "markdown", "id": "bfb67b68", "metadata": { "id": "ndDUB23CGAC5", "papermill": { "duration": 0.025205, "end_time": "2026-04-09T15:00:07.968708+00:00", "exception": false, "start_time": "2026-04-09T15:00:07.943503+00:00", "status": "completed" }, "tags": [] }, "source": [ "Let's see how the chat template did! Notice there is no `` token as the processor tokenizer will be adding one." ] }, { "cell_type": "code", "execution_count": 12, "id": "54b06431", "metadata": { "execution": { "iopub.execute_input": "2026-04-09T15:00:08.021699Z", "iopub.status.busy": "2026-04-09T15:00:08.021002Z", "iopub.status.idle": "2026-04-09T15:00:08.026602Z", "shell.execute_reply": "2026-04-09T15:00:08.025687Z" }, "id": "gGFzmplrEy9I", "outputId": "7d50b4dc-d3ff-49fd-dba6-7511ee9c7c07", "papermill": { "duration": 0.034027, "end_time": "2026-04-09T15:00:08.028170+00:00", "exception": false, "start_time": "2026-04-09T15:00:07.994143+00:00", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "'<|turn>user\\nWhat is the modulus operator in programming and how can I use it to calculate the modulus of two given numbers?\\n<|turn>model\\n<|channel>thought\\nIn programming, the modulus operator is represented by the \\'%\\' symbol. It calculates the remainder when one number is divided by another. To calculate the modulus of two given numbers, you can use the modulus operator in the following way:\\n\\n```python\\n# Calculate the modulus\\nModulus = a % b\\n\\nprint(\"Modulus of the given numbers is: \", Modulus)\\n```\\n\\nIn this code snippet, the variables \\'a\\' and \\'b\\' represent the two given numbers for which you want to calculate the modulus. By using the modulus operator \\'%\\', we calculate the remainder when \\'a\\' is divided by \\'b\\'. The result is then stored in the variable \\'Modulus\\'. Finally, the modulus value is printed using the \\'print\\' statement.\\n\\nFor example, if \\'a\\' is 10 and \\'b\\' is 4, the modulus calculation would be 10 % 4, which equals 2. Therefore, the output of the above code would be:\\n\\n```\\nModulus of the given numbers is: 2\\n```\\n\\nThis means that the modulus of 10 and 4 is 2.\\n'" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dataset[100][\"text\"]" ] }, { "cell_type": "markdown", "id": "c7255ddf", "metadata": { "id": "idAEIeSQ3xdS", "papermill": { "duration": 0.025334, "end_time": "2026-04-09T15:00:08.080059+00:00", "exception": false, "start_time": "2026-04-09T15:00:08.054725+00:00", "status": "completed" }, "tags": [] }, "source": [ "\n", "### Train the model\n", "Now let's train our model. 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`." ] }, { "cell_type": "code", "execution_count": 13, "id": "124b95e5", "metadata": { "execution": { "iopub.execute_input": "2026-04-09T15:00:08.132853Z", "iopub.status.busy": "2026-04-09T15:00:08.132156Z", "iopub.status.idle": "2026-04-09T15:00:52.584313Z", "shell.execute_reply": "2026-04-09T15:00:52.583576Z" }, "id": "95_Nn-89DhsL", "outputId": "369cc7d6-dfd2-4e4a-c117-495ffd50d06d", "papermill": { "duration": 44.480351, "end_time": "2026-04-09T15:00:52.586015+00:00", "exception": false, "start_time": "2026-04-09T15:00:08.105664+00:00", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "a1beb7697e21449a90a6dfdb59e2eb63", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Unsloth: Tokenizing [\"text\"] (num_proc=8): 0%| | 0/3000 [00:00user\\n\",\n", " response_part = \"<|turn>model\\n\",\n", ")" ] }, { "cell_type": "markdown", "id": "992d039a", "metadata": { "id": "Dv1NBUozV78l", "papermill": { "duration": 0.027396, "end_time": "2026-04-09T15:00:57.191777+00:00", "exception": false, "start_time": "2026-04-09T15:00:57.164381+00:00", "status": "completed" }, "tags": [] }, "source": [ "Let's verify masking the instruction part is done! Let's print the 100th row again. Notice how the sample only has a single `` as expected!" ] }, { "cell_type": "code", "execution_count": 15, "id": "ab585e6a", "metadata": { "execution": { "iopub.execute_input": "2026-04-09T15:00:57.245666Z", "iopub.status.busy": "2026-04-09T15:00:57.245320Z", "iopub.status.idle": "2026-04-09T15:00:57.252022Z", "shell.execute_reply": "2026-04-09T15:00:57.251352Z" }, "id": "LtsMVtlkUhja", "outputId": "68552bcc-1c20-48e9-8a78-505dced229c9", "papermill": { "duration": 0.035425, "end_time": "2026-04-09T15:00:57.253447+00:00", "exception": false, "start_time": "2026-04-09T15:00:57.218022+00:00", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "'<|turn>user\\nWhat is the modulus operator in programming and how can I use it to calculate the modulus of two given numbers?\\n<|turn>model\\n<|channel>thought\\nIn programming, the modulus operator is represented by the \\'%\\' symbol. It calculates the remainder when one number is divided by another. To calculate the modulus of two given numbers, you can use the modulus operator in the following way:\\n\\n```python\\n# Calculate the modulus\\nModulus = a % b\\n\\nprint(\"Modulus of the given numbers is: \", Modulus)\\n```\\n\\nIn this code snippet, the variables \\'a\\' and \\'b\\' represent the two given numbers for which you want to calculate the modulus. By using the modulus operator \\'%\\', we calculate the remainder when \\'a\\' is divided by \\'b\\'. The result is then stored in the variable \\'Modulus\\'. Finally, the modulus value is printed using the \\'print\\' statement.\\n\\nFor example, if \\'a\\' is 10 and \\'b\\' is 4, the modulus calculation would be 10 % 4, which equals 2. Therefore, the output of the above code would be:\\n\\n```\\nModulus of the given numbers is: 2\\n```\\n\\nThis means that the modulus of 10 and 4 is 2.\\n'" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tokenizer.decode(trainer.train_dataset[100][\"input_ids\"])" ] }, { "cell_type": "markdown", "id": "2cbf36da", "metadata": { "id": "4Kyjy__m9KY3", "papermill": { "duration": 0.025784, "end_time": "2026-04-09T15:00:57.306088+00:00", "exception": false, "start_time": "2026-04-09T15:00:57.280304+00:00", "status": "completed" }, "tags": [] }, "source": [ "Now let's print the masked out example - you should see only the answer is present:" ] }, { "cell_type": "code", "execution_count": 16, "id": "989de0a0", "metadata": { "execution": { "iopub.execute_input": "2026-04-09T15:00:57.359466Z", "iopub.status.busy": "2026-04-09T15:00:57.358849Z", "iopub.status.idle": "2026-04-09T15:00:57.364903Z", "shell.execute_reply": "2026-04-09T15:00:57.364262Z" }, "id": "_rD6fl8EUxnG", "outputId": "b44c5e2f-068f-4a31-a31e-34ed10678a6d", "papermill": { "duration": 0.034134, "end_time": "2026-04-09T15:00:57.366221+00:00", "exception": false, "start_time": "2026-04-09T15:00:57.332087+00:00", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "' <|channel>thought\\nIn programming, the modulus operator is represented by the \\'%\\' symbol. It calculates the remainder when one number is divided by another. To calculate the modulus of two given numbers, you can use the modulus operator in the following way:\\n\\n```python\\n# Calculate the modulus\\nModulus = a % b\\n\\nprint(\"Modulus of the given numbers is: \", Modulus)\\n```\\n\\nIn this code snippet, the variables \\'a\\' and \\'b\\' represent the two given numbers for which you want to calculate the modulus. By using the modulus operator \\'%\\', we calculate the remainder when \\'a\\' is divided by \\'b\\'. The result is then stored in the variable \\'Modulus\\'. Finally, the modulus value is printed using the \\'print\\' statement.\\n\\nFor example, if \\'a\\' is 10 and \\'b\\' is 4, the modulus calculation would be 10 % 4, which equals 2. Therefore, the output of the above code would be:\\n\\n```\\nModulus of the given numbers is: 2\\n```\\n\\nThis means that the modulus of 10 and 4 is 2.\\n'" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tokenizer.decode([tokenizer.pad_token_id if x == -100 else x for x in trainer.train_dataset[100][\"labels\"]]).replace(tokenizer.pad_token, \" \")" ] }, { "cell_type": "code", "execution_count": 17, "id": "05fd3572", "metadata": { "cellView": "form", "execution": { "iopub.execute_input": "2026-04-09T15:00:57.420509Z", "iopub.status.busy": "2026-04-09T15:00:57.419810Z", "iopub.status.idle": "2026-04-09T15:00:57.425475Z", "shell.execute_reply": "2026-04-09T15:00:57.424748Z" }, "id": "2ejIt2xSNKKp", "outputId": "7a278f07-915e-44b9-d018-c342eb3d9256", "papermill": { "duration": 0.034909, "end_time": "2026-04-09T15:00:57.427558+00:00", "exception": false, "start_time": "2026-04-09T15:00:57.392649+00:00", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "GPU = Tesla T4. Max memory = 14.563 GB.\n", "8.258 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": "markdown", "id": "8927128e", "metadata": { "id": "CNP1Uidk9mrz", "papermill": { "duration": 0.028863, "end_time": "2026-04-09T15:00:57.486133+00:00", "exception": false, "start_time": "2026-04-09T15:00:57.457270+00:00", "status": "completed" }, "tags": [] }, "source": [ "# Let's train the model!\n", "\n", "To resume a training run, set `trainer.train(resume_from_checkpoint = True)`" ] }, { "cell_type": "code", "execution_count": 18, "id": "8bbfadef", "metadata": { "execution": { "iopub.execute_input": "2026-04-09T15:00:57.545312Z", "iopub.status.busy": "2026-04-09T15:00:57.544669Z", "iopub.status.idle": "2026-04-09T15:45:32.096909Z", "shell.execute_reply": "2026-04-09T15:45:32.095920Z" }, "id": "yqxqAZ7KJ4oL", "outputId": "bed52b39-9792-4141-e4aa-3a7f8e3e6d49", "papermill": { "duration": 2674.583964, "end_time": "2026-04-09T15:45:32.098942+00:00", "exception": false, "start_time": "2026-04-09T15:00:57.514978+00:00", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "The tokenizer has new PAD/BOS/EOS tokens that differ from the model config and generation config. The model config and generation config were aligned accordingly, being updated with the tokenizer's values. Updated tokens: {'bos_token_id': 2}.\n", "==((====))== Unsloth - 2x faster free finetuning | Num GPUs used = 2\n", " \\\\ /| Num examples = 3,000 | Num Epochs = 1 | Total steps = 60\n", "O^O/ \\_/ \\ Batch size per device = 1 | Gradient accumulation steps = 4\n", "\\ / Data Parallel GPUs = 1 | Total batch size (1 x 4 x 1) = 4\n", " \"-____-\" Trainable parameters = 61,214,720 of 31,334,301,232 (0.20% trained)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Unsloth: Will smartly offload gradients to save VRAM!\n" ] }, { "data": { "text/html": [ "\n", "
\n", " \n", " \n", " [60/60 40:27, Epoch 0/1]\n", "
\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
StepTraining Loss
12.129590
20.628091
30.911727
41.568517
51.258429
60.759183
70.853166
80.742576
91.026424
101.295154
111.213621
120.669095
130.747510
141.042578
150.837716
160.966854
171.068619
181.088091
191.109440
200.957984
210.633893
221.168129
230.992129
240.891402
250.573305
260.488871
270.609662
280.634687
290.555076
300.802394
310.726872
320.673458
331.081852
340.485952
350.927314
360.931723
370.678981
380.992566
390.968166
400.671915
410.837725
420.872944
430.632054
440.529681
450.553375
461.237677
470.634191
480.646122
490.912598
501.033742
510.518066
520.603996
530.598058
540.576573
550.776600
560.912896
570.451140
580.760951
590.855676
600.749862

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "trainer_stats = trainer.train()" ] }, { "cell_type": "code", "execution_count": 19, "id": "3947b6c8", "metadata": { "cellView": "form", "execution": { "iopub.execute_input": "2026-04-09T15:45:32.155807Z", "iopub.status.busy": "2026-04-09T15:45:32.155341Z", "iopub.status.idle": "2026-04-09T15:45:32.162079Z", "shell.execute_reply": "2026-04-09T15:45:32.161176Z" }, "id": "pCqnaKmlO1U9", "outputId": "881756ba-9573-4a68-ec6e-14e4cf325d93", "papermill": { "duration": 0.036173, "end_time": "2026-04-09T15:45:32.163732+00:00", "exception": false, "start_time": "2026-04-09T15:45:32.127559+00:00", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2668.3194 seconds used for training.\n", "44.47 minutes used for training.\n", "Peak reserved memory = 10.949 GB.\n", "Peak reserved memory for training = 2.691 GB.\n", "Peak reserved memory % of max memory = 75.184 %.\n", "Peak reserved memory for training % of max memory = 18.478 %.\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(\n", " f\"{round(trainer_stats.metrics['train_runtime']/60, 2)} minutes used for training.\"\n", ")\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": "b53d19a6", "metadata": { "id": "ekOmTR1hSNcr", "papermill": { "duration": 0.026517, "end_time": "2026-04-09T15:45:32.218053+00:00", "exception": false, "start_time": "2026-04-09T15:45:32.191536+00:00", "status": "completed" }, "tags": [] }, "source": [ "\n", "### Inference\n", "Let's run the model via Unsloth native inference! According to the `Gemma-4` team, the recommended settings for inference are `temperature = 1.0, top_p = 0.95, top_k = 64`" ] }, { "cell_type": "code", "execution_count": 20, "id": "d13acec7", "metadata": { "execution": { "iopub.execute_input": "2026-04-09T15:45:32.273858Z", "iopub.status.busy": "2026-04-09T15:45:32.272928Z", "iopub.status.idle": "2026-04-09T15:47:52.202477Z", "shell.execute_reply": "2026-04-09T15:47:52.201712Z" }, "id": "kR3gIAX-SM2q", "outputId": "cdfa1bd8-62cd-4355-cd4a-1026a16807ea", "papermill": { "duration": 139.987318, "end_time": "2026-04-09T15:47:52.232546+00:00", "exception": false, "start_time": "2026-04-09T15:45:32.245228+00:00", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "['<|turn>user\\nContinue the sequence: 1, 1, 2, 3, 5, 8,\\n<|turn>model\\n<|channel>thought\\n13']" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from unsloth.chat_templates import get_chat_template\n", "tokenizer = get_chat_template(\n", " tokenizer,\n", " chat_template = \"gemma-4-thinking\",\n", ")\n", "messages = [{\n", " \"role\": \"user\",\n", " \"content\": [{\n", " \"type\" : \"text\",\n", " \"text\" : \"Continue the sequence: 1, 1, 2, 3, 5, 8,\",\n", " }]\n", "}]\n", "inputs = prepare_gemma_4_inputs(messages)\n", "outputs = model.generate(\n", " **inputs,\n", " max_new_tokens = 64, # Increase for longer outputs!\n", " use_cache = True,\n", " # Recommended Gemma-4 settings!\n", " temperature = 1.0, top_p = 0.95, top_k = 64,\n", ")\n", "tokenizer.batch_decode(outputs)" ] }, { "cell_type": "markdown", "id": "0e5235a5", "metadata": { "id": "CrSvZObor0lY", "papermill": { "duration": 0.026931, "end_time": "2026-04-09T15:47:52.286412+00:00", "exception": false, "start_time": "2026-04-09T15:47:52.259481+00:00", "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": 21, "id": "d145525c", "metadata": { "execution": { "iopub.execute_input": "2026-04-09T15:47:52.342005Z", "iopub.status.busy": "2026-04-09T15:47:52.341337Z", "iopub.status.idle": "2026-04-09T15:48:17.844338Z", "shell.execute_reply": "2026-04-09T15:48:17.843706Z" }, "id": "e2pEuRb1r2Vg", "outputId": "4c132d52-60a3-4024-8500-3a779f3dc9d3", "papermill": { "duration": 25.53225, "end_time": "2026-04-09T15:48:17.845774+00:00", "exception": false, "start_time": "2026-04-09T15:47:52.313524+00:00", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<|channel>thought\n", "The short answer is a phenomenon called **Rayleigh scattering**.\n", "\n", "Here is the step-by-step breakdown of why it happens:\n", "\n", "### 1. Sunlight is a spectrum of colors\n", "Although sunlight looks white, it is actually made up of all the colors of the rainbow (red, orange\n" ] } ], "source": [ "messages = [{\n", " \"role\": \"user\",\n", " \"content\": [{\"type\" : \"text\", \"text\" : \"Why is the sky blue?\",}]\n", "}]\n", "inputs = prepare_gemma_4_inputs(messages)\n", "\n", "from transformers import TextStreamer\n", "_ = model.generate(\n", " **inputs,\n", " max_new_tokens = 64, # Increase for longer outputs!\n", " use_cache = True,\n", " # Recommended Gemma-4 settings!\n", " temperature = 1.0, top_p = 0.95, top_k = 64,\n", " streamer = TextStreamer(tokenizer, skip_prompt = True),\n", ")" ] }, { "cell_type": "markdown", "id": "81ad9893", "metadata": { "id": "uMuVrWbjAzhc", "papermill": { "duration": 0.029207, "end_time": "2026-04-09T15:48:17.904807+00:00", "exception": false, "start_time": "2026-04-09T15:48:17.875600+00:00", "status": "completed" }, "tags": [] }, "source": [ "\n", "### Saving, loading finetuned models\n", "To save the final model as LoRA adapters, either use Hugging Face'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": 22, "id": "d62d52f4", "metadata": { "execution": { "iopub.execute_input": "2026-04-09T15:48:17.963190Z", "iopub.status.busy": "2026-04-09T15:48:17.962913Z", "iopub.status.idle": "2026-04-09T15:48:21.490170Z", "shell.execute_reply": "2026-04-09T15:48:21.489520Z" }, "id": "upcOlWe7A1vc", "outputId": "f2c735b5-ddc0-4664-a256-72143545e918", "papermill": { "duration": 3.558637, "end_time": "2026-04-09T15:48:21.491692+00:00", "exception": false, "start_time": "2026-04-09T15:48:17.933055+00:00", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "['gemma_4_lora/processor_config.json']" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.save_pretrained(\"gemma_4_lora\") # Local saving\n", "tokenizer.save_pretrained(\"gemma_4_lora\")\n", "# model.push_to_hub(\"HF_ACCOUNT/gemma_4_lora\", token = \"YOUR_HF_TOKEN\") # Online saving\n", "# tokenizer.push_to_hub(\"HF_ACCOUNT/gemma_4_lora\", token = \"YOUR_HF_TOKEN\") # Online saving" ] }, { "cell_type": "markdown", "id": "c2603e60", "metadata": { "id": "AEEcJ4qfC7Lp", "papermill": { "duration": 0.029644, "end_time": "2026-04-09T15:48:21.625446+00:00", "exception": false, "start_time": "2026-04-09T15:48:21.595802+00:00", "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": 23, "id": "f2cb4825", "metadata": { "execution": { "iopub.execute_input": "2026-04-09T15:48:21.685738Z", "iopub.status.busy": "2026-04-09T15:48:21.685047Z", "iopub.status.idle": "2026-04-09T15:49:12.173570Z", "shell.execute_reply": "2026-04-09T15:49:12.172820Z" }, "id": "MKX_XKs_BNZR", "outputId": "b12693a9-bca7-4e37-e9d4-1ceeed5b6907", "papermill": { "duration": 50.519795, "end_time": "2026-04-09T15:49:12.175128+00:00", "exception": false, "start_time": "2026-04-09T15:48:21.655333+00:00", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<|channel>thought\n", "As of my current knowledge, **there is no official \"Gemma-4\" model released by Google.**\n", "\n", "The current state of the Gemma family (Google's open-weights model series) is as follows:\n", "\n", "1. **Gemma (v1):** The initial release of open-weights models based on the same technology as Gemini.\n", "2. **Gemma 2:** The second generation, which introduced a different architecture (including sliding window attention and logit soft-capping) and offered significantly improved performance, particularly in the 9B and 27B parameter sizes.\n" ] } ], "source": [ "if False:\n", " from unsloth import FastModel\n", " model, tokenizer = FastModel.from_pretrained(\n", " model_name = \"gemma_4_lora\", # YOUR MODEL YOU USED FOR TRAINING\n", " max_seq_length = 2048,\n", " load_in_4bit = True,\n", " )\n", "\n", "messages = [{\n", " \"role\": \"user\",\n", " \"content\": [{\"type\" : \"text\", \"text\" : \"What is Gemma-4?\",}]\n", "}]\n", "inputs = prepare_gemma_4_inputs(messages)\n", "\n", "from transformers import TextStreamer\n", "_ = model.generate(\n", " **inputs,\n", " max_new_tokens = 128, # Increase for longer outputs!\n", " use_cache = True,\n", " # Recommended Gemma-4 settings!\n", " temperature = 1.0, top_p = 0.95, top_k = 64,\n", " streamer = TextStreamer(tokenizer, skip_prompt = True),\n", ")" ] }, { "cell_type": "markdown", "id": "bb8349cb", "metadata": { "id": "f422JgM9sdVT", "papermill": { "duration": 0.031637, "end_time": "2026-04-09T15:49:12.239235+00:00", "exception": false, "start_time": "2026-04-09T15:49:12.207598+00:00", "status": "completed" }, "tags": [] }, "source": [ "### Saving to float16 for VLLM\n", "\n", "We also support saving to `float16` directly for deployment! We save it in the folder `gemma-4-finetune`. Set `if False` to `if True` to let it run!" ] }, { "cell_type": "code", "execution_count": 24, "id": "10b57d3a", "metadata": { "execution": { "iopub.execute_input": "2026-04-09T15:49:12.302340Z", "iopub.status.busy": "2026-04-09T15:49:12.301943Z", "iopub.status.idle": "2026-04-09T15:49:12.305586Z", "shell.execute_reply": "2026-04-09T15:49:12.304927Z" }, "id": "iHjt_SMYsd3P", "papermill": { "duration": 0.036802, "end_time": "2026-04-09T15:49:12.307072+00:00", "exception": false, "start_time": "2026-04-09T15:49:12.270270+00:00", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "if False: # Change to True to save finetune!\n", " model.save_pretrained_merged(\"gemma-4-finetune\", tokenizer)" ] }, { "cell_type": "markdown", "id": "a0acca1d", "metadata": { "id": "z6O48DbNIAr0", "papermill": { "duration": 0.030509, "end_time": "2026-04-09T15:49:12.370071+00:00", "exception": false, "start_time": "2026-04-09T15:49:12.339562+00:00", "status": "completed" }, "tags": [] }, "source": [ "If you want to upload / push to your Hugging Face account, set `if False` to `if True` and add your Hugging Face token and upload location!" ] }, { "cell_type": "code", "execution_count": 25, "id": "bca9bebf", "metadata": { "execution": { "iopub.execute_input": "2026-04-09T15:49:12.433167Z", "iopub.status.busy": "2026-04-09T15:49:12.432708Z", "iopub.status.idle": "2026-04-09T15:49:12.436285Z", "shell.execute_reply": "2026-04-09T15:49:12.435702Z" }, "id": "ZV-CiKPrIFG0", "papermill": { "duration": 0.036759, "end_time": "2026-04-09T15:49:12.437534+00:00", "exception": false, "start_time": "2026-04-09T15:49:12.400775+00:00", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "if False: # Change to True to upload finetune\n", " model.push_to_hub_merged(\n", " \"HF_ACCOUNT/gemma-4-finetune\", tokenizer,\n", " token = \"YOUR_HF_TOKEN\"\n", " )" ] }, { "cell_type": "markdown", "id": "4054eea7", "metadata": { "id": "TCv4vXHd61i7", "papermill": { "duration": 0.031055, "end_time": "2026-04-09T15:49:12.500516+00:00", "exception": false, "start_time": "2026-04-09T15:49:12.469461+00:00", "status": "completed" }, "tags": [] }, "source": [ "### GGUF / llama.cpp Conversion\n", "To save to `GGUF` / `llama.cpp`, we support it natively now for all models! For now, you can convert easily to `Q8_0, F16 or BF16` precision. `Q4_K_M` for 4bit will come later!" ] }, { "cell_type": "code", "execution_count": 26, "id": "e762204f", "metadata": { "execution": { "iopub.execute_input": "2026-04-09T15:49:12.566397Z", "iopub.status.busy": "2026-04-09T15:49:12.565692Z", "iopub.status.idle": "2026-04-09T15:49:12.569767Z", "shell.execute_reply": "2026-04-09T15:49:12.568969Z" }, "id": "FqfebeAdT073", "papermill": { "duration": 0.037988, "end_time": "2026-04-09T15:49:12.571165+00:00", "exception": false, "start_time": "2026-04-09T15:49:12.533177+00:00", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "if False: # Change to True to save to GGUF\n", " model.save_pretrained_gguf(\n", " \"gemma_4_finetune\",\n", " tokenizer,\n", " quantization_method = \"Q8_0\", # For now only Q8_0, BF16, F16 supported\n", " )" ] }, { "cell_type": "markdown", "id": "b216983e", "metadata": { "id": "Q974YEVPI7JS", "papermill": { "duration": 0.031342, "end_time": "2026-04-09T15:49:12.634542+00:00", "exception": false, "start_time": "2026-04-09T15:49:12.603200+00:00", "status": "completed" }, "tags": [] }, "source": [ "Likewise, if you want to instead push to GGUF to your Hugging Face account, set `if False` to `if True` and add your Hugging Face token and upload location!" ] }, { "cell_type": "code", "execution_count": 27, "id": "26217777", "metadata": { "execution": { "iopub.execute_input": "2026-04-09T15:49:12.699012Z", "iopub.status.busy": "2026-04-09T15:49:12.698323Z", "iopub.status.idle": "2026-04-09T15:49:12.702407Z", "shell.execute_reply": "2026-04-09T15:49:12.701764Z" }, "id": "ZgcJIhJ0I_es", "papermill": { "duration": 0.038027, "end_time": "2026-04-09T15:49:12.703918+00:00", "exception": false, "start_time": "2026-04-09T15:49:12.665891+00:00", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "if False: # Change to True to upload GGUF\n", " model.push_to_hub_gguf(\n", " \"HF_ACCOUNT/gemma_4_finetune\",\n", " tokenizer,\n", " quantization_method = \"Q8_0\", # Only Q8_0, BF16, F16 supported\n", " token = \"YOUR_HF_TOKEN\",\n", " )" ] }, { "cell_type": "markdown", "id": "6d9d4e67", "metadata": { "id": "pnz9QOYTMvbH", "papermill": { "duration": 0.031057, "end_time": "2026-04-09T15:49:12.766911+00:00", "exception": false, "start_time": "2026-04-09T15:49:12.735854+00:00", "status": "completed" }, "tags": [] }, "source": [ "Now, use the `gemma-4-finetune.gguf` file or `gemma-4-finetune-Q4_K_M.gguf` file in llama.cpp.\n", "\n", "And we're done! If you have any questions on Unsloth, we have a [Discord](https://discord.gg/unsloth) 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 resources:\n", "1. Train your own reasoning model - Llama GRPO notebook [Free Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.1_(8B)-GRPO.ipynb)\n", "2. Saving finetunes to Ollama. [Free notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3_(8B)-Ollama.ipynb)\n", "3. Llama 3.2 Vision finetuning - Radiography use case. [Free Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.2_(11B)-Vision.ipynb)\n", "4. See notebooks for DPO, ORPO, Continued pretraining, conversational finetuning and more on our [documentation](https://unsloth.ai/docs/get-started/unsloth-notebooks)!\n", "\n", "

\n", " \n", " \n", " \n", "\n", " Join Discord if you need help + ⭐️ Star us on Github ⭐️\n", "
\n", "\n", " This notebook and all Unsloth notebooks are licensed [LGPL-3.0](https://github.com/unslothai/notebooks?tab=LGPL-3.0-1-ov-file#readme)." ] } ], "metadata": { "accelerator": "GPU", "colab": { "gpuType": "A100", "provenance": [] }, "kaggle": { "accelerator": "nvidiaTeslaT4", "dataSources": [ { "databundleVersionId": 16421787, "sourceId": 134561, "sourceType": "competition" } ], "dockerImageVersionId": 31329, "isGpuEnabled": true, "isInternetEnabled": true, "language": "python", "sourceType": "notebook" }, "kernelspec": { "display_name": "Python 3", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.12" }, "papermill": { "default_parameters": {}, "duration": 3769.199235, "end_time": "2026-04-09T15:49:17.022422+00:00", "environment_variables": {}, "exception": null, "input_path": 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