{ "cells": [ { "cell_type": "markdown", "id": "9b84b389", "metadata": { "id": "3FgedoPTwNIy", "papermill": { "duration": 0.02114, "end_time": "2026-04-09T14:15:04.157266+00:00", "exception": false, "start_time": "2026-04-09T14:15:04.136126+00:00", "status": "completed" }, "tags": [] }, "source": [ "To run this, press \"*Runtime*\" and press \"*Run all*\" on a Google Colab L4 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": "7bf3fc9f", "metadata": { "id": "kD8yWWE9wNIz", "papermill": { "duration": 0.017711, "end_time": "2026-04-09T14:15:04.192885+00:00", "exception": false, "start_time": "2026-04-09T14:15:04.175174+00:00", "status": "completed" }, "tags": [] }, "source": [ "### News" ] }, { "cell_type": "markdown", "id": "bc8ec7ab", "metadata": { "id": "dYFNULMnwNIz", "papermill": { "duration": 0.01829, "end_time": "2026-04-09T14:15:04.229560+00:00", "exception": false, "start_time": "2026-04-09T14:15:04.211270+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": "e7bd6a4f", "metadata": { "id": "DQ6w6D0UwNIz", "papermill": { "duration": 0.017554, "end_time": "2026-04-09T14:15:04.265115+00:00", "exception": false, "start_time": "2026-04-09T14:15:04.247561+00:00", "status": "completed" }, "tags": [] }, "source": [ "### Installation" ] }, { "cell_type": "code", "execution_count": 1, "id": "28bc6243", "metadata": { "execution": { "iopub.execute_input": "2026-04-09T14:15:04.302392Z", "iopub.status.busy": "2026-04-09T14:15:04.301722Z", "iopub.status.idle": "2026-04-09T14:15:08.424803Z", "shell.execute_reply": "2026-04-09T14:15:08.423967Z" }, "id": "LSA1qFrKwNIz", "papermill": { "duration": 4.143759, "end_time": "2026-04-09T14:15:08.426762+00:00", "exception": false, "start_time": "2026-04-09T14:15:04.283003+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>=2026.4.5\" transformers==5.5.0 torchcodec timm" ] }, { "cell_type": "markdown", "id": "f6b4946d", "metadata": { "id": "TGMWlrRdzwgf", "papermill": { "duration": 0.018261, "end_time": "2026-04-09T14:15:08.464472+00:00", "exception": false, "start_time": "2026-04-09T14:15:08.446211+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": "0da09ebe", "metadata": { "execution": { "iopub.execute_input": "2026-04-09T14:15:08.501803Z", "iopub.status.busy": "2026-04-09T14:15:08.501126Z", "iopub.status.idle": "2026-04-09T14:18:12.449859Z", "shell.execute_reply": "2026-04-09T14:18:12.449089Z" }, "id": "-Xbb0cuLzwgf", "outputId": "b1936459-858a-460f-d39c-fe4d41114da8", "papermill": { "duration": 183.969777, "end_time": "2026-04-09T14:18:12.451994+00:00", "exception": false, "start_time": "2026-04-09T14:15:08.482217+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": "3f9356b907ff4aeb8a45acce1e82e41b", "version_major": 2, "version_minor": 0 }, "text/plain": [ "model.safetensors: 0%| | 0.00/16.0G [00:00" ] }, { "cell_type": "code", "execution_count": 4, "id": "3991f051", "metadata": { "execution": { "iopub.execute_input": "2026-04-09T14:18:12.637777Z", "iopub.status.busy": "2026-04-09T14:18:12.637023Z", "iopub.status.idle": "2026-04-09T14:18:56.352960Z", "shell.execute_reply": "2026-04-09T14:18:56.352104Z" }, "id": "9jGeSb9bWe0k", "outputId": "289205bd-b890-4659-8c82-8c7674befdfe", "papermill": { "duration": 43.740329, "end_time": "2026-04-09T14:18:56.354671+00:00", "exception": false, "start_time": "2026-04-09T14:18:12.614342+00:00", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "I am sorry, but I cannot answer that question. The image you provided is a photograph of a sloth, and it is not a film poster or a scene from a movie, so it does not feature in any films.\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": "13c09053", "metadata": { "id": "eh0BzbZPWtRD", "papermill": { "duration": 0.021173, "end_time": "2026-04-09T14:18:56.397839+00:00", "exception": false, "start_time": "2026-04-09T14:18:56.376666+00:00", "status": "completed" }, "tags": [] }, "source": [ "Let's make a poem about sloths!" ] }, { "cell_type": "code", "execution_count": 5, "id": "f05f68d7", "metadata": { "execution": { "iopub.execute_input": "2026-04-09T14:18:56.442084Z", "iopub.status.busy": "2026-04-09T14:18:56.441720Z", "iopub.status.idle": "2026-04-09T14:19:19.809135Z", "shell.execute_reply": "2026-04-09T14:19:19.808438Z" }, "id": "R3ExuK8cWuT3", "outputId": "d63b198a-00c6-49a5-d0e5-78cbd18d217f", "papermill": { "duration": 23.39163, "end_time": "2026-04-09T14:19:19.810646+00:00", "exception": false, "start_time": "2026-04-09T14:18:56.419016+00:00", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "## The Gentle Pace\n", "\n", "In emerald woods, where mosses softly cling,\n", "And dappled sunlight makes the shadows swing,\n", "There moves a creature, slow beneath the green,\n", "The sloth, a masterpiece of patient scene.\n", "\n", "A hanging silhouette, a mossy grace,\n", "Adorning time within its tranquil space.\n", "No hurried breath disturbs its languid guise,\n", "It moves as if the forest breathes through sighs.\n", "\n", "The world rushes by—a frantic, buzzing flight—\n", "Of birds in chorus, bathed in blinding light.\n", "But the sloth observes it with a knowing eye,\n", "Content within its slow eternity.\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": "3b172504", "metadata": { "id": "wZrmFRZpZtGf", "papermill": { "duration": 0.025014, "end_time": "2026-04-09T14:19:19.861788+00:00", "exception": false, "start_time": "2026-04-09T14:19:19.836774+00:00", "status": "completed" }, "tags": [] }, "source": [ "# Gemma 4 can also hear!" ] }, { "cell_type": "code", "execution_count": 6, "id": "6285a30a", "metadata": { "execution": { "iopub.execute_input": "2026-04-09T14:19:19.912674Z", "iopub.status.busy": "2026-04-09T14:19:19.912128Z", "iopub.status.idle": "2026-04-09T14:19:20.258124Z", "shell.execute_reply": "2026-04-09T14:19:20.257342Z" }, "id": "68crYajNZtw1", "outputId": "e8ffe42f-79d8-473c-8b2b-8d4d05acc549", "papermill": { "duration": 0.376154, "end_time": "2026-04-09T14:19:20.262661+00:00", "exception": false, "start_time": "2026-04-09T14:19:19.886507+00:00", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from IPython.display import Audio, display\n", "Audio(\"https://www.nasa.gov/wp-content/uploads/2015/01/591240main_JFKmoonspeech.mp3\")" ] }, { "cell_type": "code", "execution_count": 7, "id": "4fdc2f3c", "metadata": { "execution": { "iopub.execute_input": "2026-04-09T14:19:20.323617Z", "iopub.status.busy": "2026-04-09T14:19:20.323336Z", "iopub.status.idle": "2026-04-09T14:19:20.800752Z", "shell.execute_reply": "2026-04-09T14:19:20.799625Z" }, "id": "k3vrdoa0Z01X", "papermill": { "duration": 0.509763, "end_time": "2026-04-09T14:19:20.802792+00:00", "exception": false, "start_time": "2026-04-09T14:19:20.293029+00:00", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "!wget -qqq https://www.nasa.gov/wp-content/uploads/2015/01/591240main_JFKmoonspeech.mp3 -O audio.mp3" ] }, { "cell_type": "code", "execution_count": 8, "id": "95b24a57", "metadata": { "execution": { "iopub.execute_input": "2026-04-09T14:19:20.862299Z", "iopub.status.busy": "2026-04-09T14:19:20.861816Z", "iopub.status.idle": "2026-04-09T14:19:39.539296Z", "shell.execute_reply": "2026-04-09T14:19:39.538406Z" }, "id": "BJr_D4O9Z2Zh", "outputId": "d6517a81-1d77-41dc-b2dd-2e6ab1aa3a29", "papermill": { "duration": 18.708402, "end_time": "2026-04-09T14:19:39.540895+00:00", "exception": false, "start_time": "2026-04-09T14:19:20.832493+00:00", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "This audio is about the Apollo program.\n" ] } ], "source": [ "audio_file = \"audio.mp3\"\n", "\n", "messages = [{\n", " \"role\" : \"user\",\n", " \"content\": [\n", " { \"type\": \"audio\", \"audio\" : audio_file },\n", " { \"type\": \"text\", \"text\" : \"What is this audio about?\" }\n", " ]\n", "}]\n", "do_gemma_4_inference(messages, max_new_tokens = 256)" ] }, { "cell_type": "markdown", "id": "20e934e5", "metadata": { "id": "L15JuAmmaOkB", "papermill": { "duration": 0.027251, "end_time": "2026-04-09T14:19:39.597028+00:00", "exception": false, "start_time": "2026-04-09T14:19:39.569777+00:00", "status": "completed" }, "tags": [] }, "source": [ "# Let's combine all 3 modalities together!" ] }, { "cell_type": "code", "execution_count": 9, "id": "7d2bde68", "metadata": { "execution": { "iopub.execute_input": "2026-04-09T14:19:39.653187Z", "iopub.status.busy": "2026-04-09T14:19:39.652613Z", "iopub.status.idle": "2026-04-09T14:19:56.511776Z", "shell.execute_reply": "2026-04-09T14:19:56.511109Z" }, "id": "is37bsDZaRwV", "outputId": "37235929-0289-4ba6-c352-da6d389cb5a0", "papermill": { "duration": 16.889309, "end_time": "2026-04-09T14:19:56.513361+00:00", "exception": false, "start_time": "2026-04-09T14:19:39.624052+00:00", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "This appears to be a snippet of a speech, likely from a historical or political context, given the formal language and the ambitious goal mentioned. The image is a picture of a sloth.\n", "\n", "**Relationship:**\n", "\n", "There is no apparent relationship between the audio and the image. The audio is a serious, goal-oriented statement about a national endeavor, while the image is a photograph of an animal.\n" ] } ], "source": [ "messages = [{\n", " \"role\" : \"user\",\n", " \"content\": [\n", " { \"type\": \"audio\", \"audio\" : audio_file },\n", " { \"type\": \"image\", \"image\" : sloth_link },\n", " { \"type\": \"text\", \"text\" : \"What is this audio and image about? \"\\\n", " \"How are they related?\" }\n", " ]\n", "}]\n", "do_gemma_4_inference(messages, max_new_tokens = 256)" ] }, { "cell_type": "markdown", "id": "17fa13a3", "metadata": { "id": "Bw5XPyYFajyM", "papermill": { "duration": 0.032942, "end_time": "2026-04-09T14:19:56.579220+00:00", "exception": false, "start_time": "2026-04-09T14:19:56.546278+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": "3666efda", "metadata": { "id": "SXd9bTZd1aaL", "papermill": { "duration": 0.031294, "end_time": "2026-04-09T14:19:56.642703+00:00", "exception": false, "start_time": "2026-04-09T14:19:56.611409+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": 10, "id": "eb65be92", "metadata": { "execution": { "iopub.execute_input": "2026-04-09T14:19:56.719906Z", "iopub.status.busy": "2026-04-09T14:19:56.718703Z", "iopub.status.idle": "2026-04-09T14:20:02.557870Z", "shell.execute_reply": "2026-04-09T14:20:02.556932Z" }, "id": "6bZsfBuZDeCL", "papermill": { "duration": 5.88583, "end_time": "2026-04-09T14:20:02.559982+00:00", "exception": false, "start_time": "2026-04-09T14:19:56.674152+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": "a1bf9b7c", "metadata": { "id": "vITh0KVJ10qX", "papermill": { "duration": 0.029681, "end_time": "2026-04-09T14:20:02.620946+00:00", "exception": false, "start_time": "2026-04-09T14:20:02.591265+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": 11, "id": "a1cb35e2", "metadata": { "execution": { "iopub.execute_input": "2026-04-09T14:20:02.685772Z", "iopub.status.busy": "2026-04-09T14:20:02.685316Z", "iopub.status.idle": "2026-04-09T14:20:02.690342Z", "shell.execute_reply": "2026-04-09T14:20:02.689588Z" }, "id": "LjY75GoYUCB8", "papermill": { "duration": 0.039865, "end_time": "2026-04-09T14:20:02.691841+00:00", "exception": false, "start_time": "2026-04-09T14:20:02.651976+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\",\n", ")" ] }, { "cell_type": "markdown", "id": "f0482b8e", "metadata": { "id": "ZQkXuGYxbJ-e", "papermill": { "duration": 0.029969, "end_time": "2026-04-09T14:20:02.751701+00:00", "exception": false, "start_time": "2026-04-09T14:20:02.721732+00:00", "status": "completed" }, "tags": [] }, "source": [ "We get the first 3000 rows of the dataset" ] }, { "cell_type": "code", "execution_count": 12, "id": "3c26afca", "metadata": { "execution": { "iopub.execute_input": "2026-04-09T14:20:02.811731Z", "iopub.status.busy": "2026-04-09T14:20:02.811310Z", "iopub.status.idle": "2026-04-09T14:20:06.302247Z", "shell.execute_reply": "2026-04-09T14:20:06.301342Z" }, "id": "Mkq4RvEq7FQr", "outputId": "e78c54dd-f931-4c00-a73d-e37f3eff19e8", "papermill": { "duration": 3.522975, "end_time": "2026-04-09T14:20:06.303736+00:00", "exception": false, "start_time": "2026-04-09T14:20:02.780761+00:00", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "1bd2499e051c47fe835936333918511a", "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": 15, "id": "2fd69cec", "metadata": { "execution": { "iopub.execute_input": "2026-04-09T14:20:07.839111Z", "iopub.status.busy": "2026-04-09T14:20:07.838390Z", "iopub.status.idle": "2026-04-09T14:20:12.567650Z", "shell.execute_reply": "2026-04-09T14:20:12.566972Z" }, "id": "1ahE8Ys37JDJ", "outputId": "13973ed3-779d-485e-92aa-33b6925f1737", "papermill": { "duration": 4.762099, "end_time": "2026-04-09T14:20:12.569118+00:00", "exception": false, "start_time": "2026-04-09T14:20:07.807019+00:00", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "65a0b27e6c4247a080a54ed6b18b656b", "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": "6fbe8d8f", "metadata": { "id": "ndDUB23CGAC5", "papermill": { "duration": 0.030617, "end_time": "2026-04-09T14:20:12.631702+00:00", "exception": false, "start_time": "2026-04-09T14:20:12.601085+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": 16, "id": "84476bb5", "metadata": { "execution": { "iopub.execute_input": "2026-04-09T14:20:12.697330Z", "iopub.status.busy": "2026-04-09T14:20:12.696528Z", "iopub.status.idle": "2026-04-09T14:20:12.702078Z", "shell.execute_reply": "2026-04-09T14:20:12.701418Z" }, "id": "gGFzmplrEy9I", "outputId": "36882276-635b-45f5-f9d8-3764373dc35e", "papermill": { "duration": 0.03974, "end_time": "2026-04-09T14:20:12.703464+00:00", "exception": false, "start_time": "2026-04-09T14:20:12.663724+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\\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": [ "dataset[100][\"text\"]" ] }, { "cell_type": "markdown", "id": "029e0426", "metadata": { "id": "idAEIeSQ3xdS", "papermill": { "duration": 0.030334, "end_time": "2026-04-09T14:20:12.764440+00:00", "exception": false, "start_time": "2026-04-09T14:20:12.734106+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": 17, "id": "2063eb63", "metadata": { "execution": { "iopub.execute_input": "2026-04-09T14:20:12.827234Z", "iopub.status.busy": "2026-04-09T14:20:12.826505Z", "iopub.status.idle": "2026-04-09T14:20:58.477258Z", "shell.execute_reply": "2026-04-09T14:20:58.476282Z" }, "id": "95_Nn-89DhsL", "outputId": "87503724-3283-49e4-fec6-4b2c4e084fca", "papermill": { "duration": 45.684477, "end_time": "2026-04-09T14:20:58.479229+00:00", "exception": false, "start_time": "2026-04-09T14:20:12.794752+00:00", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "5a7b3ac7349846deb9064be7c434d764", "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": "5b1a7cc7", "metadata": { "id": "Dv1NBUozV78l", "papermill": { "duration": 0.030613, "end_time": "2026-04-09T14:21:02.511624+00:00", "exception": false, "start_time": "2026-04-09T14:21:02.481011+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": 19, "id": "9fb4df86", "metadata": { "execution": { "iopub.execute_input": "2026-04-09T14:21:02.575287Z", "iopub.status.busy": "2026-04-09T14:21:02.574549Z", "iopub.status.idle": "2026-04-09T14:21:02.581906Z", "shell.execute_reply": "2026-04-09T14:21:02.581204Z" }, "id": "LtsMVtlkUhja", "outputId": "d3126757-2a14-4c6d-99f8-98b3bf91be55", "papermill": { "duration": 0.041188, "end_time": "2026-04-09T14:21:02.583417+00:00", "exception": false, "start_time": "2026-04-09T14:21:02.542229+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\\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": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tokenizer.decode(trainer.train_dataset[100][\"input_ids\"])" ] }, { "cell_type": "markdown", "id": "f3972b9e", "metadata": { "id": "4Kyjy__m9KY3", "papermill": { "duration": 0.030403, "end_time": "2026-04-09T14:21:02.645269+00:00", "exception": false, "start_time": "2026-04-09T14:21:02.614866+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": 20, "id": "165d2951", "metadata": { "execution": { "iopub.execute_input": "2026-04-09T14:21:02.708036Z", "iopub.status.busy": "2026-04-09T14:21:02.707522Z", "iopub.status.idle": "2026-04-09T14:21:02.715319Z", "shell.execute_reply": "2026-04-09T14:21:02.714464Z" }, "id": "_rD6fl8EUxnG", "outputId": "e579a2b3-9df6-4c40-ef7c-98d7c9cd9d82", "papermill": { "duration": 0.041393, "end_time": "2026-04-09T14:21:02.716862+00:00", "exception": false, "start_time": "2026-04-09T14:21:02.675469+00:00", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "' In 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": 20, "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": 21, "id": "73ec0163", "metadata": { "cellView": "form", "execution": { "iopub.execute_input": "2026-04-09T14:21:02.783682Z", "iopub.status.busy": "2026-04-09T14:21:02.783109Z", "iopub.status.idle": "2026-04-09T14:21:02.788999Z", "shell.execute_reply": "2026-04-09T14:21:02.788020Z" }, "id": "2ejIt2xSNKKp", "outputId": "2ecea13f-2faa-4c7d-e006-a0a1cb90612a", "papermill": { "duration": 0.039208, "end_time": "2026-04-09T14:21:02.790686+00:00", "exception": false, "start_time": "2026-04-09T14:21:02.751478+00:00", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "GPU = Tesla T4. Max memory = 14.563 GB.\n", "9.891 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": "eee6fad9", "metadata": { "id": "CNP1Uidk9mrz", "papermill": { "duration": 0.030451, "end_time": "2026-04-09T14:21:02.852194+00:00", "exception": false, "start_time": "2026-04-09T14:21:02.821743+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": 22, "id": "d83b4edc", "metadata": { "execution": { "iopub.execute_input": "2026-04-09T14:21:02.917281Z", "iopub.status.busy": "2026-04-09T14:21:02.916648Z", "iopub.status.idle": "2026-04-09T14:27:25.935388Z", "shell.execute_reply": "2026-04-09T14:27:25.934474Z" }, "id": "yqxqAZ7KJ4oL", "outputId": "180092e1-ae76-449a-9f2d-0dbc3894b8f8", "papermill": { "duration": 383.053463, "end_time": "2026-04-09T14:27:25.937341+00:00", "exception": false, "start_time": "2026-04-09T14:21:02.883878+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 = 1\n", " \\\\ /| Num examples = 2,991 | 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 = 18,350,080 of 8,014,506,528 (0.23% trained)\n", "Caching is incompatible with gradient checkpointing in Gemma4TextDecoderLayer. Setting `past_key_values=None`.\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 05:53, Epoch 0/1]\n", "
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StepTraining Loss
110.523083
210.053993
312.042599
49.989523
510.095123
610.694252
710.365056
89.754820
98.308989
107.331110
117.254261
126.173282
136.381964
144.963476
155.690104
164.411693
174.552224
184.913744
195.021698
204.934382
213.959793
223.978175
234.443007
244.595473
253.987100
263.831797
273.935458
283.008995
293.504218
303.710511
313.277609
323.731925
333.324703
343.664254
353.287769
363.606441
373.251672
383.009572
393.222754
402.543038
413.437666
423.372575
432.745370
442.456408
452.818116
463.419305
472.893388
483.205531
492.713997
502.372560
512.854839
522.170771
533.100059
542.641205
552.271563
562.701172
572.765497
582.787446
592.603349
602.821406

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "trainer_stats = trainer.train()" ] }, { "cell_type": "code", "execution_count": 23, "id": "ba3079a2", "metadata": { "cellView": "form", "execution": { "iopub.execute_input": "2026-04-09T14:27:26.004799Z", "iopub.status.busy": "2026-04-09T14:27:26.004480Z", "iopub.status.idle": "2026-04-09T14:27:26.012023Z", "shell.execute_reply": "2026-04-09T14:27:26.011040Z" }, "id": "pCqnaKmlO1U9", "outputId": "585888de-dbd8-4562-c6e5-a5f0dbf26dc7", "papermill": { "duration": 0.042476, "end_time": "2026-04-09T14:27:26.013461+00:00", "exception": false, "start_time": "2026-04-09T14:27:25.970985+00:00", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "379.4784 seconds used for training.\n", "6.32 minutes used for training.\n", "Peak reserved memory = 10.715 GB.\n", "Peak reserved memory for training = 0.824 GB.\n", "Peak reserved memory % of max memory = 73.577 %.\n", "Peak reserved memory for training % of max memory = 5.658 %.\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": "78fe5ece", "metadata": { "id": "ekOmTR1hSNcr", "papermill": { "duration": 0.031646, "end_time": "2026-04-09T14:27:26.078664+00:00", "exception": false, "start_time": "2026-04-09T14:27:26.047018+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": 24, "id": "789f3453", "metadata": { "execution": { "iopub.execute_input": "2026-04-09T14:27:26.144119Z", "iopub.status.busy": "2026-04-09T14:27:26.143382Z", "iopub.status.idle": "2026-04-09T14:27:38.238264Z", "shell.execute_reply": "2026-04-09T14:27:38.237354Z" }, "id": "kR3gIAX-SM2q", "outputId": "42097560-b2b6-4d32-a9f8-160db24434cc", "papermill": { "duration": 12.129782, "end_time": "2026-04-09T14:27:38.240040+00:00", "exception": false, "start_time": "2026-04-09T14:27:26.110258+00:00", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "['<|turn>user\\nContinue the sequence: 1, 1, 2, 3, 5, 8,\\n<|turn>model\\n13, 21, 34, 55, 89, ...\\n\\nThis is the **Fibonacci sequence**, where each number is the sum of the two preceding ones.']" ] }, "execution_count": 24, "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\",\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 = tokenizer.apply_chat_template(\n", " messages,\n", " add_generation_prompt = True, # Must add for generation\n", " return_tensors = \"pt\",\n", " tokenize = True,\n", " return_dict = True,\n", ").to(\"cuda\")\n", "outputs = model.generate(\n", " **inputs,\n", " max_new_tokens = 64, # Increase for longer outputs!\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": "d14c972d", "metadata": { "id": "CrSvZObor0lY", "papermill": { "duration": 0.03125, "end_time": "2026-04-09T14:27:38.303897+00:00", "exception": false, "start_time": "2026-04-09T14:27:38.272647+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": 25, "id": "80b77f9d", "metadata": { "execution": { "iopub.execute_input": "2026-04-09T14:27:38.368840Z", "iopub.status.busy": "2026-04-09T14:27:38.367877Z", "iopub.status.idle": "2026-04-09T14:27:53.797997Z", "shell.execute_reply": "2026-04-09T14:27:53.797095Z" }, "id": "e2pEuRb1r2Vg", "outputId": "a648ff4f-3762-44d5-eb8b-5045a5c01a50", "papermill": { "duration": 15.464494, "end_time": "2026-04-09T14:27:53.799621+00:00", "exception": false, "start_time": "2026-04-09T14:27:38.335127+00:00", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The sky appears blue primarily due to a phenomenon called **Rayleigh scattering**. Here's a breakdown of how this works:\n", "\n", "1. **Sunlight is composed of different wavelengths:** Sunlight, which comes from the sun, is made up of various colors, each with a different wavelength. Blue light has a shorter wavelength\n" ] } ], "source": [ "messages = [{\n", " \"role\": \"user\",\n", " \"content\": [{\"type\" : \"text\", \"text\" : \"Why is the sky blue?\",}]\n", "}]\n", "inputs = tokenizer.apply_chat_template(\n", " messages,\n", " add_generation_prompt = True, # Must add for generation\n", " return_tensors = \"pt\",\n", " tokenize = True,\n", " return_dict = True,\n", ").to(\"cuda\")\n", "\n", "from transformers import TextStreamer\n", "_ = model.generate(\n", " **inputs,\n", " max_new_tokens = 64, # Increase for longer outputs!\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": "ec07e9bd", "metadata": { "id": "uMuVrWbjAzhc", "papermill": { "duration": 0.035117, "end_time": "2026-04-09T14:27:53.868536+00:00", "exception": false, "start_time": "2026-04-09T14:27:53.833419+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": 26, "id": "c841a798", "metadata": { "execution": { "iopub.execute_input": "2026-04-09T14:27:53.936332Z", "iopub.status.busy": "2026-04-09T14:27:53.935832Z", "iopub.status.idle": "2026-04-09T14:27:57.309758Z", "shell.execute_reply": "2026-04-09T14:27:57.308857Z" }, "id": "upcOlWe7A1vc", "outputId": "b38635d3-0eae-40ed-93f1-771f04c205c0", "papermill": { "duration": 3.410314, "end_time": "2026-04-09T14:27:57.311357+00:00", "exception": false, "start_time": "2026-04-09T14:27:53.901043+00:00", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "['gemma_4_lora/processor_config.json']" ] }, "execution_count": 26, "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": "c0280a59", "metadata": { "id": "AEEcJ4qfC7Lp", "papermill": { "duration": 0.03305, "end_time": "2026-04-09T14:27:57.379393+00:00", "exception": false, "start_time": "2026-04-09T14:27:57.346343+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": 27, "id": "4e76e14f", "metadata": { "execution": { "iopub.execute_input": "2026-04-09T14:27:57.446364Z", "iopub.status.busy": "2026-04-09T14:27:57.445610Z", "iopub.status.idle": "2026-04-09T14:28:03.411741Z", "shell.execute_reply": "2026-04-09T14:28:03.411099Z" }, "id": "MKX_XKs_BNZR", "outputId": "c6380f12-92c3-44da-ee71-c6895cb19a95", "papermill": { "duration": 6.001005, "end_time": "2026-04-09T14:28:03.413384+00:00", "exception": false, "start_time": "2026-04-09T14:27:57.412379+00:00", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "I am Gemma 4, a Large Language Model developed by Google DeepMind. I am an open weights model.\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 = tokenizer.apply_chat_template(\n", " messages,\n", " add_generation_prompt = True, # Must add for generation\n", " return_tensors = \"pt\",\n", " tokenize = True,\n", " return_dict = True,\n", ").to(\"cuda\")\n", "\n", "from transformers import TextStreamer\n", "_ = model.generate(\n", " **inputs,\n", " max_new_tokens = 128, # Increase for longer outputs!\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": "2c10654e", "metadata": { "id": "f422JgM9sdVT", "papermill": { "duration": 0.033782, "end_time": "2026-04-09T14:28:03.482906+00:00", "exception": false, "start_time": "2026-04-09T14:28:03.449124+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": 28, "id": "b579db68", "metadata": { "execution": { "iopub.execute_input": "2026-04-09T14:28:03.553382Z", "iopub.status.busy": "2026-04-09T14:28:03.552682Z", "iopub.status.idle": "2026-04-09T14:28:03.556813Z", "shell.execute_reply": "2026-04-09T14:28:03.555982Z" }, "id": "iHjt_SMYsd3P", "papermill": { "duration": 0.040718, "end_time": "2026-04-09T14:28:03.558346+00:00", "exception": false, "start_time": "2026-04-09T14:28:03.517628+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": "ec2d7240", "metadata": { "id": "z6O48DbNIAr0", "papermill": { "duration": 0.033577, "end_time": "2026-04-09T14:28:03.626993+00:00", "exception": false, "start_time": "2026-04-09T14:28:03.593416+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": 29, "id": "301ea027", "metadata": { "execution": { "iopub.execute_input": "2026-04-09T14:28:03.695829Z", "iopub.status.busy": "2026-04-09T14:28:03.695545Z", "iopub.status.idle": "2026-04-09T14:28:03.699618Z", "shell.execute_reply": "2026-04-09T14:28:03.698865Z" }, "id": "ZV-CiKPrIFG0", "papermill": { "duration": 0.040418, "end_time": "2026-04-09T14:28:03.700987+00:00", "exception": false, "start_time": "2026-04-09T14:28:03.660569+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": "f1580211", "metadata": { "id": "TCv4vXHd61i7", "papermill": { "duration": 0.034175, "end_time": "2026-04-09T14:28:03.769687+00:00", "exception": false, "start_time": "2026-04-09T14:28:03.735512+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": 30, "id": "a11309e2", "metadata": { "execution": { "iopub.execute_input": "2026-04-09T14:28:03.838541Z", "iopub.status.busy": "2026-04-09T14:28:03.837824Z", "iopub.status.idle": "2026-04-09T14:28:03.842150Z", "shell.execute_reply": "2026-04-09T14:28:03.841298Z" }, "id": "FqfebeAdT073", "papermill": { "duration": 0.040287, "end_time": "2026-04-09T14:28:03.843691+00:00", "exception": false, "start_time": "2026-04-09T14:28:03.803404+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": "a9ac86d1", "metadata": { "id": "Q974YEVPI7JS", "papermill": { "duration": 0.033488, "end_time": "2026-04-09T14:28:03.911272+00:00", "exception": false, "start_time": "2026-04-09T14:28:03.877784+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": 31, "id": "163597b1", "metadata": { "execution": { "iopub.execute_input": "2026-04-09T14:28:03.982798Z", "iopub.status.busy": "2026-04-09T14:28:03.982206Z", "iopub.status.idle": "2026-04-09T14:28:03.986398Z", "shell.execute_reply": "2026-04-09T14:28:03.985558Z" }, "id": "ZgcJIhJ0I_es", "papermill": { "duration": 0.041947, "end_time": "2026-04-09T14:28:03.988402+00:00", "exception": false, "start_time": "2026-04-09T14:28:03.946455+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": "bedaf816", "metadata": { "id": "pnz9QOYTMvbH", "papermill": { "duration": 0.03474, "end_time": "2026-04-09T14:28:04.058824+00:00", "exception": false, "start_time": "2026-04-09T14:28:04.024084+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": "T4", "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", "language": "python", "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": 785.802226, "end_time": "2026-04-09T14:28:07.416534+00:00", "environment_variables": {}, "exception": null, 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