{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "view-in-github", "colab_type": "text" }, "source": [ "\"Open" ] }, { "cell_type": "markdown", "metadata": { "id": "fl_n1V3tuno6" }, "source": [ "To run this, press \"*Runtime*\" and press \"*Run all*\" on a **free** Tesla T4 Google Colab instance!\n", "
\n", "\n", "\n", " Join Discord if you need help + ⭐ Star us on Github ⭐\n", "
\n", "\n", "To install Unsloth on your own computer, follow the installation instructions on our Github page [here](https://docs.unsloth.ai/get-started/installing-+-updating).\n", "\n", "You will learn how to do [data prep](#Data), how to [train](#Train), how to [run the model](#Inference), & [how to save it](#Save)\n" ] }, { "cell_type": "markdown", "metadata": { "id": "xSqIx2upunpB" }, "source": [ "### News" ] }, { "cell_type": "markdown", "metadata": { "id": "BrOVX8HuunpD" }, "source": [ "Unsloth now supports Text-to-Speech (TTS) models. Read our [guide here](https://docs.unsloth.ai/basics/text-to-speech-tts-fine-tuning).\n", "\n", "Read our **[Qwen3 Guide](https://docs.unsloth.ai/basics/qwen3-how-to-run-and-fine-tune)** and check out our new **[Dynamic 2.0](https://docs.unsloth.ai/basics/unsloth-dynamic-2.0-ggufs)** quants which outperforms other quantization methods!\n", "\n", "Visit our docs for all our [model uploads](https://docs.unsloth.ai/get-started/all-our-models) and [notebooks](https://docs.unsloth.ai/get-started/unsloth-notebooks).\n" ] }, { "cell_type": "markdown", "metadata": { "id": "6-qvfCwVunpE" }, "source": [ "### Installation" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "id": "S_sOjj5bunpE" }, "outputs": [], "source": [ "%%capture\n", "import os\n", "if \"COLAB_\" not in \"\".join(os.environ.keys()):\n", " !pip install unsloth\n", "else:\n", " # Do this only in Colab notebooks! Otherwise use pip install unsloth\n", " !pip install --no-deps bitsandbytes accelerate xformers==0.0.29.post3 peft trl triton cut_cross_entropy unsloth_zoo\n", " !pip install sentencepiece protobuf \"datasets>=3.4.1\" huggingface_hub hf_transfer\n", " !pip install --no-deps unsloth" ] }, { "cell_type": "markdown", "metadata": { "id": "3y7wgJwfunpG" }, "source": [ "### Unsloth" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 508, "referenced_widgets": [ "428f6f60cb124f228e0dd090ee570f0a", "21da14174ab049abb21894d190d85bd3", "56c729f70ee74210ae8c2c1dfb42797f", "47f1cd78991644449502fe1b588a9374", "b112ca19fca04425b29bf2fd410ff24b", "28b296508b294c9e99af732e3f08aad3", "b6cc5e69c644442fbf4ddf2a97ff0a4d", "dee748a368e14c289e8482e4de791d1a", "ba8afbfa5178459a933551020c59ef91", "50f6c476eca14d148e4af2282a7fac2b", "31746d7949d7481e893885ee1d618418", "cb8afcc84c4444f882f6eaf66bd38378", "d8d913689841406c8e075c90b65306d0", "40b016d7acb84878abfdfedad882cbbf", "67de74bd33e949d3b07ade3de21081a2", "a0f3b07676934a7e85c4e308cfb86ec6", "a4d2ab12fe9d40d6a24d6730d5cb5938", "00f8a168c28e4cf5bbe0abf1cf9428bf", "8d604d909bbe4608ab3ec2667b4eb285", "7b8ddd30bc5e4559a0b43767425816a0", "cb9a5a5d5def4260a897d399ee061dcf", "2cd4e8b3fb20444f8bc1149207bc858c", "d2f8e42e71f343959d1147d958660b65", "65dce4154f6244f8881d54a52fe556fd", "fddcbfbdff60444fa3351736ec4bb6e9", "23bd011e1c4547f9b86dda5159ad1bd8", "6c2fd27f7b4042e8b1dac992300b1988", "5d51c1babc7e433eb8938f78737623b3", "027b8ec62840421381d5353b77873818", "4041997279fb4c73977a0274e98e1b51", "f7098cff8bbc46b69f4b8c8e95d0aac2", "7a3461ce90e441609e618efaf3052ca4", "1792c323dcee4e1194a0f6f3145d028d", "db0cef4c9c004f529f8eae72434e0ddc", "98c0a232e95244509f5438f690dc48cf", "88ef1e6461744d66be94b45e59cf57da", "474e07de72bd46f185d1155b7ca921fd", "2166187ccabe4a4dbf999fb8a85a0612", "d34172d1fde74da695ea957826a6b93b", "9637afffeb754d7f82712c6e1ac563dd", 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"0ffa911cb9ef4fe6ae24db48455eadbb", "61dea32e0eba4e3b81a882d7a7849116", "e1fa090639a34f6b96ef77a91b9c455a", "9280848c16b0460f9ee9d72282026bdd", "d3af2065d0be4f0bb93713c70dd10edd", "d764434ac81e431c9ad30b645da1143b", "bc78b62433694331a8b769db83acca6b", "12d45ec3838a43f6abf3d8bc1a4f3e93", "8415461077ba436c97ad370b3eff2e5f", "6a839284b2b14a1588b6a8b4c2f3b411", "1a075f87ec7844798aa4c0047e4eb77a", "c0125f107d224c4f9408ec43a26e8e9e", "4f82e098820c4259a682356bfeb357a9", "0c7c879def644cb594afad9916dddd1c", "293162ff8ed64ef79d65927c9723d268", "882cac223521478ebfbba3a08a7f55cb", "4d59ea1ec490424c80a32a5473573635", "36ad26fea3f84c4e8b1d9cd3d9b8da99", "49072833194c4f40873485a7e3f3e3d1", "48042174addd4629aa1d83b95c5e0b14", "03f92590271d4d6191ecc9ff268ae9d5", "e326143602344f1d9f0698a16d3c97ed", "74c7cd705b3c401688b82c6a2ebfde97", "995f6589ade9486d80b0588b0faddb5f", "20d88510bb5f4cf7b57260c7122af054", "4dccdf9e595647e495d0f22e46b436bf", "4c46e5e1372348d7b44c54b3452a385f", 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"outputId": "766b6dab-7b11-4d07-8573-54c641077840" }, "outputs": [ { "output_type": "stream", "name": "stdout", "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 2025.6.2: Fast Llama patching. Transformers: 4.52.4.\n", " \\\\ /| Tesla T4. Num GPUs = 1. Max memory: 14.741 GB. Platform: Linux.\n", "O^O/ \\_/ \\ Torch: 2.6.0+cu124. CUDA: 7.5. CUDA Toolkit: 12.4. Triton: 3.2.0\n", "\\ / Bfloat16 = FALSE. FA [Xformers = 0.0.29.post3. FA2 = False]\n", " \"-____-\" Free license: http://github.com/unslothai/unsloth\n", "Unsloth: Fast downloading is enabled - ignore downloading bars which are red colored!\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "model.safetensors.index.json: 0%| | 0.00/160k [00:00 0 ! Suggested 8, 16, 32, 64, 128\n", " target_modules = [\"q_proj\", \"k_proj\", \"v_proj\", \"o_proj\",\n", " \"gate_proj\", \"up_proj\", \"down_proj\",],\n", " lora_alpha = 16,\n", " lora_dropout = 0, # Supports any, but = 0 is optimized\n", " bias = \"none\", # Supports any, but = \"none\" is optimized\n", " # [NEW] \"unsloth\" uses 30% less VRAM, fits 2x larger batch sizes!\n", " use_gradient_checkpointing = \"unsloth\", # True or \"unsloth\" for very long context\n", " random_state = 3407,\n", " use_rslora = False, # We support rank stabilized LoRA\n", " loftq_config = None, # And LoftQ\n", ")" ] }, { "cell_type": "markdown", "metadata": { "id": "vITh0KVJ10qX" }, "source": [ "\n", "### Data Prep\n", "We now use the `Phi-4` format for conversation style finetunes. We use [Maxime Labonne's FineTome-100k](https://huggingface.co/datasets/mlabonne/FineTome-100k) dataset in ShareGPT style. But we convert it to HuggingFace's normal multiturn format `(\"role\", \"content\")` instead of `(\"from\", \"value\")`/ Phi-4 renders multi turn conversations like below:\n", "\n", "```\n", "<|im_start|>user<|im_sep|>Hello!<|im_end|>\n", "<|im_start|>assistant<|im_sep|>Hi! How can I help?<|im_end|>\n", "<|im_start|>user<|im_sep|>What is 2+2?<|im_end|>\n", "```\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, phi4, llama3` and more." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "id": "LjY75GoYUCB8", "colab": { "base_uri": "https://localhost:8080/", "height": 113, "referenced_widgets": [ "f4363df9143346389a0d3bc7bf99b14a", "a0d4937e62aa4db09fab9a85ca998b62", "22a1cc9a0b4a4f8bb7071fead584f43b", "36b3bb3a7d7743ee9e12e86e2d20a53e", "c7cf772baf9a449c8da854f791052622", "e9f62da38e2c4bcf90b0a3c229b5bd96", "a3ee0bef33d94dd5a2e235809913ef38", "f00733d1138147609f9b9d0449a3cf98", "c70f83a393b44424be9c63dcceae967e", "b1f7f036461c4edcad2bdf66e13f0a00", "f0fde22420904e0f978c0599b34cb445", "72b7856e3f0e44e981353dc2bd13c9a6", "e15c13fe53c04c2bb321fb7bf3a7cb34", "c8e431a3fed748fc992d224453d2bfc6", "bcea0d84e1b540a38536c36a950fcb6e", "21c59e6eda434805ac1186a293b69c0c", "7d16e2565d754b54a1ea029f7839a755", "af10fccfd3364b35917da1041c7f056e", "4a3fadfe95ef42618a529e2735fb7511", "e07b9809dede4ed6a59a99bd8ac0c26b", "0b5576d1e013408caaf0f3c2ce152790", "912c36319ca8405ab48047710c8d4594", "fc0fdf54f94b4d19b4ce6d7c7cc6edca", "c97f0b831e6a42ddb2db90b4100f3147", "c7ab18d6a94c4b39a9537c5236dbfee8", "a5bb389c0f5a498ea671ec4b912bb767", "583180378211481daf85fb9ab521a25b", "82f61ff11eb54b5fbaea034beb8d9b83", "5de8e656e16a4d8f87a23e48a33c6a15", "6bb46329855247599735b05e979daaeb", "b402d005930249f7ac109ff9213eaf10", "1dc489a27c984915af43c13196efe80f", "432e76fe9bbe49f68a2fc366b36f85f2" ] }, "outputId": "2b925a6b-ebaf-41fe-f56b-4ca8f12b94a0" }, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "README.md: 0%| | 0.00/982 [00:00user<|im_sep|>How do astronomers determine the original wavelength of light emitted by a celestial body at rest, which is necessary for measuring its speed using the Doppler effect?<|im_end|><|im_start|>assistant<|im_sep|>Astronomers make use of the unique spectral fingerprints of elements found in stars. These elements emit and absorb light at specific, known wavelengths, forming an absorption spectrum. By analyzing the light received from distant stars and comparing it to the laboratory-measured spectra of these elements, astronomers can identify the shifts in these wavelengths due to the Doppler effect. The observed shift tells them the extent to which the light has been redshifted or blueshifted, thereby allowing them to calculate the speed of the star along the line of sight relative to Earth.<|im_end|>'" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "string" } }, "metadata": {}, "execution_count": 7 } ], "source": [ "dataset[5][\"text\"]" ] }, { "cell_type": "markdown", "metadata": { "id": "idAEIeSQ3xdS" }, "source": [ "\n", "### Train the model\n", "Now let's use Huggingface TRL's `SFTTrainer`! More docs here: [TRL SFT docs](https://huggingface.co/docs/trl/sft_trainer). We do 60 steps to speed things up, but you can set `num_train_epochs=1` for a full run, and turn off `max_steps=None`. We also support TRL's `DPOTrainer`!" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 49, "referenced_widgets": [ "6bb88dfebb844d0ca8e4a3a138bf9f11", "c807d78351d541ab89d24a2fc6efd142", "21d0c9234ea840e7b4016d59ff87e482", "2a2da6b91b6a428b941242627538855b", "29acff7760274c98b0237b6b91659351", "300e59716fca43dd9f1b278d323e0044", "10b4a44bfaf041b39812ef53fe350752", "5bffd4daedfd4ad0a0d5ee5910f8b423", "680ca256611246b1a5c7f6069d11c531", "ea034ca6fb7d41c2b0b3c52d511a5765", "8f0358d24d0c40dcb7357d5ae6e2233f" ] }, "id": "95_Nn-89DhsL", "outputId": "3031e65e-7140-47bb-e175-cbd45bbf0083" }, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "Unsloth: Tokenizing [\"text\"]: 0%| | 0/100000 [00:00user<|im_sep|>\",\n", " response_part=\"<|im_start|>assistant<|im_sep|>\",\n", ")" ] }, { "cell_type": "markdown", "metadata": { "id": "Dv1NBUozV78l" }, "source": [ "We verify masking is actually done:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 142 }, "id": "LtsMVtlkUhja", "outputId": "6fa2529b-d480-49b3-b62a-1b7a38e5d4ba" }, "outputs": [ { "data": { "application/vnd.google.colaboratory.intrinsic+json": { "type": "string" }, "text/plain": [ "'<|im_start|>user<|im_sep|>How do astronomers determine the original wavelength of light emitted by a celestial body at rest, which is necessary for measuring its speed using the Doppler effect?<|im_end|><|im_start|>assistant<|im_sep|>Astronomers make use of the unique spectral fingerprints of elements found in stars. These elements emit and absorb light at specific, known wavelengths, forming an absorption spectrum. By analyzing the light received from distant stars and comparing it to the laboratory-measured spectra of these elements, astronomers can identify the shifts in these wavelengths due to the Doppler effect. The observed shift tells them the extent to which the light has been redshifted or blueshifted, thereby allowing them to calculate the speed of the star along the line of sight relative to Earth.<|im_end|>'" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tokenizer.decode(trainer.train_dataset[5][\"input_ids\"])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 124 }, "id": "_rD6fl8EUxnG", "outputId": "f8602f78-9674-4911-d37b-03fc203e00b3" }, "outputs": [ { "data": { "application/vnd.google.colaboratory.intrinsic+json": { "type": "string" }, "text/plain": [ "' Astronomers make use of the unique spectral fingerprints of elements found in stars. These elements emit and absorb light at specific, known wavelengths, forming an absorption spectrum. By analyzing the light received from distant stars and comparing it to the laboratory-measured spectra of these elements, astronomers can identify the shifts in these wavelengths due to the Doppler effect. The observed shift tells them the extent to which the light has been redshifted or blueshifted, thereby allowing them to calculate the speed of the star along the line of sight relative to Earth.<|im_end|>'" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "space = tokenizer(\" \", add_special_tokens = False).input_ids[0]\n", "tokenizer.decode([space if x == -100 else x for x in trainer.train_dataset[5][\"labels\"]])" ] }, { "cell_type": "markdown", "metadata": { "id": "3enWUM0jV-jV" }, "source": [ "We can see the System and Instruction prompts are successfully masked!" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "cellView": "form", "colab": { "base_uri": "https://localhost:8080/" }, "id": "2ejIt2xSNKKp", "outputId": "94e771b7-a863-4759-f5c5-390574cf8338" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "GPU = Tesla T4. Max memory = 14.748 GB.\n", "10.043 GB of memory reserved.\n" ] } ], "source": [ "# @title Show current memory stats\n", "gpu_stats = torch.cuda.get_device_properties(0)\n", "start_gpu_memory = round(torch.cuda.max_memory_reserved() / 1024 / 1024 / 1024, 3)\n", "max_memory = round(gpu_stats.total_memory / 1024 / 1024 / 1024, 3)\n", "print(f\"GPU = {gpu_stats.name}. Max memory = {max_memory} GB.\")\n", "print(f\"{start_gpu_memory} GB of memory reserved.\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "id": "yqxqAZ7KJ4oL", "outputId": "62ec7355-f48e-4615-a94c-4127bb68801b" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "==((====))== Unsloth - 2x faster free finetuning | Num GPUs = 1\n", " \\\\ /| Num examples = 100,000 | Num Epochs = 1\n", "O^O/ \\_/ \\ Batch size per device = 2 | Gradient Accumulation steps = 4\n", "\\ / Total batch size = 8 | Total steps = 30\n", " \"-____-\" Number of trainable parameters = 65,536,000\n" ] }, { "data": { "text/html": [ "\n", "
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StepTraining Loss
10.746700
20.660800
31.035700
40.767600
50.693200
60.818100
70.464400
80.831800
90.600800
100.525800
110.700200
120.760500
130.774900
140.452600
150.634000
160.395300
170.783800
180.656200
190.605400
200.770700
210.732600
220.535400
230.910900
240.721200
250.500100
260.590200
270.658900
280.657500
290.698500
300.770000

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "trainer_stats = trainer.train()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "cellView": "form", "colab": { "base_uri": "https://localhost:8080/" }, "id": "pCqnaKmlO1U9", "outputId": "b2d8b834-7957-4bb2-abc8-200b44280ab6" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "959.8611 seconds used for training.\n", "16.0 minutes used for training.\n", "Peak reserved memory = 12.361 GB.\n", "Peak reserved memory for training = 2.318 GB.\n", "Peak reserved memory % of max memory = 83.815 %.\n", "Peak reserved memory for training % of max memory = 15.717 %.\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", "metadata": { "id": "ekOmTR1hSNcr" }, "source": [ "\n", "### Inference\n", "Let's run the model! You can change the instruction and input - leave the output blank!\n", "\n", "\n", "\n", "We use `min_p = 0.1` and `temperature = 1.5`." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "kR3gIAX-SM2q", "outputId": "aaff4281-4838-4bdf-f8c3-15136cc63398" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "The attention mask is not set and cannot be inferred from input because pad token is same as eos token. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n" ] }, { "data": { "text/plain": [ "['<|im_start|>user<|im_sep|>Continue the fibonnaci sequence: 1, 1, 2, 3, 5, 8,<|im_end|><|im_start|>assistant<|im_sep|>The next number in the Fibonacci sequence is 13. The Fibonacci sequence is a series of numbers in which each number is the sum of the two preceding ones, usually starting with 0 and 1. In this case, the sequence starts with 1 and 1, and each subsequent number is the sum of the']" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from unsloth.chat_templates import get_chat_template\n", "\n", "tokenizer = get_chat_template(\n", " tokenizer,\n", " chat_template = \"phi-4\",\n", ")\n", "FastLanguageModel.for_inference(model) # Enable native 2x faster inference\n", "\n", "messages = [\n", " {\"role\": \"user\", \"content\": \"Continue the fibonnaci sequence: 1, 1, 2, 3, 5, 8,\"},\n", "]\n", "inputs = tokenizer.apply_chat_template(\n", " messages,\n", " tokenize = True,\n", " add_generation_prompt = True, # Must add for generation\n", " return_tensors = \"pt\",\n", ").to(\"cuda\")\n", "\n", "outputs = model.generate(\n", " input_ids = inputs, max_new_tokens = 64, use_cache = True, temperature = 1.5, min_p = 0.1\n", ")\n", "tokenizer.batch_decode(outputs)" ] }, { "cell_type": "markdown", "metadata": { "id": "CrSvZObor0lY" }, "source": [ " You can also use a `TextStreamer` for continuous inference - so you can see the generation token by token, instead of waiting the whole time!" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "e2pEuRb1r2Vg", "outputId": "812b3637-3aa2-446e-c01b-f50e5787927b" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The next number in the Fibonacci sequence is 13. The Fibonacci sequence is a series of numbers in which each number is the sum of the two preceding ones, usually starting with 0 and 1. In this case, the sequence starts with 1 and 1, and each subsequent number is the sum of the two preceding numbers. So, the next number after 8 is 13, which is the sum of 5 and 8.<|im_end|>\n" ] } ], "source": [ "FastLanguageModel.for_inference(model) # Enable native 2x faster inference\n", "\n", "messages = [\n", " {\"role\": \"user\", \"content\": \"Continue the fibonnaci sequence: 1, 1, 2, 3, 5, 8,\"},\n", "]\n", "inputs = tokenizer.apply_chat_template(\n", " messages,\n", " tokenize = True,\n", " add_generation_prompt = True, # Must add for generation\n", " return_tensors = \"pt\",\n", ").to(\"cuda\")\n", "\n", "from transformers import TextStreamer\n", "text_streamer = TextStreamer(tokenizer, skip_prompt = True)\n", "_ = model.generate(\n", " input_ids = inputs, streamer = text_streamer, max_new_tokens = 128,\n", " use_cache = True, temperature = 1.5, min_p = 0.1\n", ")" ] }, { "cell_type": "markdown", "metadata": { "id": "uMuVrWbjAzhc" }, "source": [ "\n", "### Saving, loading finetuned models\n", "To save the final model as LoRA adapters, either use Huggingface's `push_to_hub` for an online save or `save_pretrained` for a local save.\n", "\n", "**[NOTE]** This ONLY saves the LoRA adapters, and not the full model. To save to 16bit or GGUF, scroll down!" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "upcOlWe7A1vc", "outputId": "82b0db99-a5df-4ab8-a73a-ecfe1174a022" }, "outputs": [ { "data": { "text/plain": [ "('lora_model/tokenizer_config.json',\n", " 'lora_model/special_tokens_map.json',\n", " 'lora_model/vocab.json',\n", " 'lora_model/merges.txt',\n", " 'lora_model/added_tokens.json',\n", " 'lora_model/tokenizer.json')" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.save_pretrained(\"lora_model\") # Local saving\n", "tokenizer.save_pretrained(\"lora_model\")\n", "# model.push_to_hub(\"your_name/lora_model\", token = \"...\") # Online saving\n", "# tokenizer.push_to_hub(\"your_name/lora_model\", token = \"...\") # Online saving" ] }, { "cell_type": "markdown", "metadata": { "id": "AEEcJ4qfC7Lp" }, "source": [ "Now if you want to load the LoRA adapters we just saved for inference, set `False` to `True`:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "MKX_XKs_BNZR", "outputId": "bba3c600-0fb1-4e09-da10-2415d4463232" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The Eiffel Tower is a tall tower located in the capital of France, Paris. It stands at a height of 324 meters (1,063 feet) and was completed in 1889. The tower was designed by Gustave Eiffel and his team for the 1889 Exposition Universelle (World's Fair) to celebrate the 100th anniversary of the French Revolution. It is made of wrought iron and consists of three levels that are accessible to visitors. The Eiffel Tower is one of the most recognizable landmarks in the world and attracts millions of tourists each year.<|im_end|>\n" ] } ], "source": [ "if False:\n", " from unsloth import FastLanguageModel\n", " model, tokenizer = FastLanguageModel.from_pretrained(\n", " model_name = \"lora_model\", # YOUR MODEL YOU USED FOR TRAINING\n", " max_seq_length = max_seq_length,\n", " dtype = dtype,\n", " load_in_4bit = load_in_4bit,\n", " )\n", " FastLanguageModel.for_inference(model) # Enable native 2x faster inference\n", "\n", "messages = [\n", " {\"role\": \"user\", \"content\": \"Describe a tall tower in the capital of France.\"},\n", "]\n", "inputs = tokenizer.apply_chat_template(\n", " messages,\n", " tokenize = True,\n", " add_generation_prompt = True, # Must add for generation\n", " return_tensors = \"pt\",\n", ").to(\"cuda\")\n", "\n", "from transformers import TextStreamer\n", "text_streamer = TextStreamer(tokenizer, skip_prompt = True)\n", "_ = model.generate(\n", " input_ids = inputs, streamer = text_streamer, max_new_tokens = 128,\n", " use_cache = True, temperature = 1.5, min_p = 0.1\n", ")" ] }, { "cell_type": "markdown", "metadata": { "id": "QQMjaNrjsU5_" }, "source": [ "You can also use Hugging Face's `AutoModelForPeftCausalLM`. Only use this if you do not have `unsloth` installed. It can be hopelessly slow, since `4bit` model downloading is not supported, and Unsloth's **inference is 2x faster**." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "yFfaXG0WsQuE" }, "outputs": [], "source": [ "if False:\n", " # I highly do NOT suggest - use Unsloth if possible\n", " from peft import AutoPeftModelForCausalLM\n", " from transformers import AutoTokenizer\n", "\n", " model = AutoPeftModelForCausalLM.from_pretrained(\n", " \"lora_model\", # YOUR MODEL YOU USED FOR TRAINING\n", " load_in_4bit=load_in_4bit,\n", " )\n", " tokenizer = AutoTokenizer.from_pretrained(\"lora_model\")" ] }, { "cell_type": "markdown", "metadata": { "id": "f422JgM9sdVT" }, "source": [ "### Saving to float16 for VLLM\n", "\n", "We also support saving to `float16` directly. Select `merged_16bit` for float16 or `merged_4bit` for int4. We also allow `lora` adapters as a fallback. Use `push_to_hub_merged` to upload to your Hugging Face account! You can go to https://huggingface.co/settings/tokens for your personal tokens." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "iHjt_SMYsd3P" }, "outputs": [], "source": [ "from google.colab import userdata\n", "# Merge to 16bit\n", "if False: model.save_pretrained_merged(\"model\", tokenizer, save_method = \"merged_16bit\",)\n", "if False: model.push_to_hub_merged(\"hf/model\", tokenizer, save_method = \"merged_16bit\", token = \"\")\n", "\n", "# Merge to 4bit\n", "if False: model.save_pretrained_merged(\"model\", tokenizer, save_method = \"merged_4bit\",)\n", "if False: model.push_to_hub_merged(\"hf/model\", tokenizer, save_method = \"merged_4bit\", token = \"\")\n", "\n", "# Just LoRA adapters\n", "if False:\n", " model.save_pretrained(\"model\")\n", " tokenizer.save_pretrained(\"model\")\n", "if False:\n", " model.push_to_hub(\"hf/model\", token = \"\")\n", " tokenizer.push_to_hub(\"hf/model\", token = \"\")\n" ] }, { "cell_type": "markdown", "metadata": { "id": "TCv4vXHd61i7" }, "source": [ "### GGUF / llama.cpp Conversion\n", "To save to `GGUF` / `llama.cpp`, we support it natively now! We clone `llama.cpp` and we default save it to `q8_0`. We allow all methods like `q4_k_m`. Use `save_pretrained_gguf` for local saving and `push_to_hub_gguf` for uploading to HF.\n", "\n", "Some supported quant methods (full list in our [Docs](https://docs.unsloth.ai/basics/saving-and-using-models/saving-to-gguf)):\n", "* `q8_0` - Fast conversion. High resource use, but generally acceptable.\n", "* `q4_k_m` - Recommended. Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q4_K.\n", "* `q5_k_m` - Recommended. Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q5_K.\n", "\n", "[**NEW**] To finetune and auto export to Ollama, try our [Ollama notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3_(8B)-Ollama.ipynb)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "FqfebeAdT073" }, "outputs": [], "source": [ "# Save to 8bit Q8_0\n", "if False: model.save_pretrained_gguf(\"model\", tokenizer,)\n", "# Remember to go to https://huggingface.co/settings/tokens for a token!\n", "# And change hf to your username!\n", "if False: model.push_to_hub_gguf(\"hf/model\", tokenizer, quantization_method = \"q4_k_m\", token = \"\")\n", "\n", "# Save to 16bit GGUF\n", "if False: model.save_pretrained_gguf(\"model\", tokenizer, quantization_method = \"f16\")\n", "if False: model.push_to_hub_gguf(\"hf/model\", tokenizer, quantization_method = \"f16\", token = \"\")\n", "\n", "# Save to q4_k_m GGUF\n", "if False: model.save_pretrained_gguf(\"model\", tokenizer, quantization_method = \"q4_k_m\")\n", "if False: model.push_to_hub_gguf(\"hf/model\", tokenizer, quantization_method = \"q4_k_m\", token = \"\")\n", "\n", "# Save to multiple GGUF options - much faster if you want multiple!\n", "if False:\n", " model.push_to_hub_gguf(\n", " \"hf/model\", # Change hf to your username!\n", " tokenizer,\n", " quantization_method = [\"q4_k_m\", \"q8_0\", \"q5_k_m\",],\n", " token = \"\", # Get a token at https://huggingface.co/settings/tokens\n", " )" ] }, { "cell_type": "markdown", "metadata": { "id": "7ZU_OyVhunpo" }, "source": [ "Now, use the `model-unsloth.gguf` file or `model-unsloth-Q4_K_M.gguf` file in llama.cpp or a UI based system like Jan or Open WebUI. You can install Jan [here](https://github.com/janhq/jan) and Open WebUI [here](https://github.com/open-webui/open-webui)\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 links:\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", "6. See notebooks for DPO, ORPO, Continued pretraining, conversational finetuning and more on our [documentation](https://docs.unsloth.ai/get-started/unsloth-notebooks)!\n", "\n", "

\n", " \n", " \n", " \n", "\n", " Join Discord if you need help + ⭐️ Star us on Github ⭐️\n", "
\n" ] } ], "metadata": { "accelerator": "GPU", "colab": { "gpuType": "T4", "provenance": [], "include_colab_link": true }, "kernelspec": { "display_name": "Python 3", "name": "python3" }, "language_info": { "name": "python" }, "widgets": { "application/vnd.jupyter.widget-state+json": { "0262bf690a744e34b348a1a7efb74b1f": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HTMLView", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_fa980b3860db4e508140ec6073462cc9", "placeholder": "​", "style": "IPY_MODEL_7153553bfd334094bcb67f8f4cc5d991", "value": "Map: 100%" } }, "623f6d9c54a6482c853233eb80799fe4": { "model_module": "@jupyter-widgets/controls", 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