{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "IqM-T1RTzY6C" }, "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://github.com/unslothai/unsloth#installation-instructions---conda).\n", "\n", "You will learn how to do [data prep](#Data), how to [train](#Train), how to [run the model](#Inference), & [how to save it](#Save) (eg for Llama.cpp).\n", "\n", "**[NEW] Llama-3 8b is trained on a crazy 15 trillion tokens! Llama-2 was 2 trillion.**\n", "\n", "Use our [Llama-3 8b Instruct](https://colab.research.google.com/drive/1XamvWYinY6FOSX9GLvnqSjjsNflxdhNc?usp=sharing) notebook for conversational style finetunes." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "id": "2eSvM9zX_2d3" }, "outputs": [], "source": [ "%%capture\n", "# Installs Unsloth, Xformers (Flash Attention) and all other packages!\n", "!pip install \"unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git\"\n", "!pip install --no-deps \"xformers<0.0.27\" \"trl<0.9.0\" peft accelerate bitsandbytes" ] }, { "cell_type": "markdown", "metadata": { "id": "r2v_X2fA0Df5" }, "source": [ "* We support Llama, Mistral, Phi-3, Gemma, Yi, DeepSeek, Qwen, TinyLlama, Vicuna, Open Hermes etc\n", "* We support 16bit LoRA or 4bit QLoRA. Both 2x faster.\n", "* `max_seq_length` can be set to anything, since we do automatic RoPE Scaling via [kaiokendev's](https://kaiokendev.github.io/til) method.\n", "* With [PR 26037](https://github.com/huggingface/transformers/pull/26037), we support downloading 4bit models **4x faster**! [Our repo](https://huggingface.co/unsloth) has Llama, Mistral 4bit models.\n", "* [**NEW**] We make Phi-3 Medium / Mini **2x faster**! See our [Phi-3 Medium notebook](https://colab.research.google.com/drive/1hhdhBa1j_hsymiW9m-WzxQtgqTH_NHqi?usp=sharing)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 317, "referenced_widgets": [ "33895f09c4e64e6684406cdcd5f563fd", "0445e5c28e524074963f64a149bf3cfa", "3b46850152f945b4bcee867a9ff14c2a", "073e484f01db4e649b66db2072827f72", "47f09df713a74b0cafbfdc7c2a0e85c7", "6554dd4fae784997ba07a35bc07edee3", "37feb436305b455c86729a84089c6aa5", "1de19f5998674812a7ab5f685a45ade4", "247fff3855a745d4937852de9b6f9843", "4e0ec430c99345f8b60e1659295ea2a2", "3907c9b53e5340ba85d51438d2ae266f", "55d6eda3c12a4449af7763ccb7a49f00", "d3bd08957d1c4879952af4cf693c17b8", "72fb43b649e0439c81b4e53b0823c1eb", "47ab2cae5911474d8aca8c094a898d23", "7f9dddd6dee045b38c05cf939b2789c8", "66be1b3de29b4e909ce747a372c7c008", "ec2ab0f18ec0464c9c7d7ce78472c659", "1d23ff49c2fb4e368928c4deedae1e76", "a97b4621e6f44fddaf3ddafe9f46152f", "542dc10170534e7196810310356e1b20", "80baff9c0fdb4374b37896550cc220dc", "b4fc4ce48f62475a9107f913295ff7cf", "a2436af0db914999b2a8c2c18afbbf73", "3ae8111fe0b74b8ea424cfebc24632b9", "262fcd0f446f4ab69d92905d6e8a2602", "a3d25194f86442eda20e09f401bcd3c1", "fafd843122bb409fbd146c5dbac3cb78", "28baa1c95e954ad6a666d3793c2a882d", "96a4c7f788a549b1b19ea2f13f825ca1", "c8bb106712c84c108cf0749ee63ff75d", "6fb140805e9f4bdf898358644d8d734a", "985c9a53ad8b4fbe9cf0e1d05e9ff56f", "b058085bbeca46caa4896edfe860a623", "7243abd22e1e4642b71d17d14812ca70", "3a0cf2b2f57342808a971d98ab1b7972", "d715ad9cb0584b4686a237af22561b4e", "4443f46dafc84c20a44c7785724af67d", "d4ef604990454fd1923318f7da4ff8b9", "b0c327bc5c6f4eb1a6b802b41d557874", "92e3e3bda7a84b95bce5e0d00cd64beb", "46d99c8975c14aa5ad2f50cf815db37a", "62ca245a446941109da866001624d06b", "4606cd60b1534e42bbaf704623946c36", "0e40ad039ec34cd2aa27d028fc36ee92", "5e3da8a2d6e44bef96e3876fe4823122", "9ba26b96c0074174a03226f756d3632d", "7ff52d7bc0f44bf29b17f96468e91bef", "c4130e0aa98d40a5ad4bc874dc4f98c3", "fe99021bf8fe4b6e8436cc5a89d7bdbf", "7e138dcd88e94d7b83cf7b13c1035e4a", "b0e36ff8a7674b438cbeb246b6a3a9c0", "6bf7f4b5fb0e4d3b8784f24c001d50b1", "40d3a07645024300a7535d1e89aef589", "1c811454dbfc4e6bb1ee2b2803b25103", "16ef819900f445838f6176cf2ed8faa1", "16c27aede36546cb86fd184b7add6fa4", "36762799aea142ddb0b62560eae1313e", "880bd546988b482389f94bb98445a2ec", "4664462e2e0741cba072945c6e7a7db8", "2a6d76700c8347c28d76d0e59375d1f6", "5ba9a1a9db8b4c0bbd314c1eb0d57c38", "87556af1ea8f454db21ce343cdc68bef", "778f09d2dbc94865841b7efb92e7059a", "a6b3ac77f4924620b5943682d9ca6533", "6c19c528f3384c23b7b505b13e96e466" ] }, "id": "QmUBVEnvCDJv", "outputId": "62e39098-c428-4111-8f24-897c2258d3bc" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "πŸ¦₯ Unsloth: Will patch your computer to enable 2x faster free finetuning.\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "33895f09c4e64e6684406cdcd5f563fd", "version_major": 2, "version_minor": 0 }, "text/plain": [ "config.json: 0%| | 0.00/1.20k [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 Alpaca dataset from [yahma](https://huggingface.co/datasets/yahma/alpaca-cleaned), which is a filtered version of 52K of the original [Alpaca dataset](https://crfm.stanford.edu/2023/03/13/alpaca.html). You can replace this code section with your own data prep.\n", "\n", "**[NOTE]** To train only on completions (ignoring the user's input) read TRL's docs [here](https://huggingface.co/docs/trl/sft_trainer#train-on-completions-only).\n", "\n", "**[NOTE]** Remember to add the **EOS_TOKEN** to the tokenized output!! Otherwise you'll get infinite generations!\n", "\n", "If you want to use the `llama-3` template for ShareGPT datasets, try our conversational [notebook](https://colab.research.google.com/drive/1XamvWYinY6FOSX9GLvnqSjjsNflxdhNc?usp=sharing).\n", "\n", "For text completions like novel writing, try this [notebook](https://colab.research.google.com/drive/1ef-tab5bhkvWmBOObepl1WgJvfvSzn5Q?usp=sharing)." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 177, "referenced_widgets": [ "2f43333451054b3a81602f20b9ad1000", "6010e32dd91047c48afbe13eb6de3595", "ec7c2d0e03d444378dd96ac90b0ee759", "ceff5d4cb783406299fa8c136415976e", "71827793051c44f0b8aa7737e5dda0dc", "93ca967d9f5a43cc95e06bdaab341963", "36a5d81916ad4194a1fc169037d53b45", "cc17de8b4a9e4036bcbe53e45f62dffb", "c453f53075924ba0aca4dab0d00275a7", "fa9e20693fa949d08dc4d3fba7f7bda2", "9efefaf8db344b1b9519b91241481ce3", "b15a4d3ba9844225a6877a1d0e9336b3", "0e6babfc1b2e4221b01d01689602db9b", "2765ffc2c0214b9e9f79b283d80446e2", "b9b55b7bbc0249b2999fb08d0251db9d", "325a003f04d843df94a1b2e0312e06b1", "fe0d8b18c38b484794f72d3f2a4ee32f", "c8a2974771d842e4b748fdfffb9eb919", "b2f9babcc6b842358b5fe5b4ba335ad7", "286538ed23864103947bc362e677c70c", "1e28af0eb5e342b086e52f6258df3633", "46fe84463f14477c89005a6532e8d10d", "38adcfbc030748719d9e37b11813b9dd", "64b89675756045a78e3cc183b1f72d8d", "098c94df9b8c441dad02ff8b309e208f", "e6c84d21b17b4615ae6af39a13cff09e", "a2a924461a064c09a8aa83a903513aca", "7854886150904782afcc5ffccdfc72a0", "b615d3fa5dc941ff96c943540a2c3870", "6f668baf9d3f4e0f85d1a5023da1af27", "843984997cef4c0f9999445c195d7f73", "cb4f137b6f024656b534f8e51ffe50d0", "185467553ac9481eb6d56c73f0ee4d61", "b6de4ef3f2164f92a86bc5777dfa5658", "4bb0887e1b7c4f38a510fb36ad9fb58d", "200170cca8464e5aa25038605de5c813", "e423c91917404fe3b6ef0a31d764abc8", "f2d71e9ec7ab443292ddfe62f779a06a", "6de52155b5084ca3b5a22138f2afca22", "963e4bbd59c5467388dbb3f534e26ef6", "444ee42f095c47fda7b13587c4d82bec", "9df36cfe31784b999419c9a8682c7d9b", "975d0500395b40f397e535794ecb9efb", "d6c914e55ba542dba80852641777ffba", "9f34edb2574d4554b5b6deac493ea171", "6485e9a7ff4c4ee0be483103eb169856", "05543bc6c3b2464db3ae37a062ff35cc", "9575228eb4b34611be18725c11606205", "ded47baa43b54346bc38030f6b306bba", "82db31fa6e494025bdb7391d8559fadf", "2f96eaa6ecef4744af8829f92875750b", "d254414578a84b7d89051f485fa04bd5", "7eb27d398a33469f928ebc2ff666c276", "81a2fe74684f42d6ac6c86e0b788ab9f", "89c7b4190ae047ac82eb432c3812b80b" ] }, "id": "LjY75GoYUCB8", "outputId": "871d3a98-6b6f-4b4e-f491-3187ddb50813" }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "2f43333451054b3a81602f20b9ad1000", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Downloading readme: 0%| | 0.00/450 [00:00\n", "### Train the model\n", "Now let's use Huggingface TRL's `SFTTrainer`! More docs here: [TRL SFT docs](https://huggingface.co/docs/trl/sft_trainer). We do 60 steps to speed things up, but you can set `num_train_epochs=1` for a full run, and turn off `max_steps=None`. We also support TRL's `DPOTrainer`!" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 67, "referenced_widgets": [ "4c49f0cc71b841deae67b2fc8bcbd4f9", "9311cbe24c50407fa0b6d023cd9fd0af", "f32cd58aa555487692de6123cc084d27", "ac3769f04bdb4471b088d71966709e6a", "11225c6f40d7402baab08988eb2408c5", "9221d8c6e23e4dd3ad4b19ef0d20f6ef", "8f4e746d95b94993bc37367beb8d340f", "14c9396eceaa451b9d10a3a70bd870fd", "8fe7c1dfc36c476482001d2f499027a9", "d93551db6f844b3babe9472210ffa133", "275c797e689e43dca3a43a554f43bab7" ] }, "id": "95_Nn-89DhsL", "outputId": "ba24ce50-6d08-4e0e-ad33-73c767a3603a" }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "4c49f0cc71b841deae67b2fc8bcbd4f9", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Map (num_proc=2): 0%| | 0/172026 [00:00\n", " \n", " \n", " [60/60 30:55, 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
10.541900
20.539500
30.645700
40.658600
50.582600
60.501800
70.545300
80.565200
90.596400
100.476300
110.632000
120.476300
130.609900
140.550300
150.568000
160.469300
170.567600
180.602300
190.489600
200.464300
210.485100
220.516300
230.597500
240.493700
250.508600
260.551900
270.386700
280.504400
290.585600
300.507200
310.389000
320.494800
330.513800
340.617400
350.702300
360.494700
370.506600
380.494600
390.496900
400.321300
410.599100
420.558100
430.336700
440.444500
450.515100
460.355000
470.365200
480.503300
490.521500
500.570200
510.607700
520.512800
530.541300
540.537500
550.570200
560.582000
570.543300
580.386400
590.524000
600.566700

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "trainer_stats = trainer.train()" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "cellView": "form", "colab": { "base_uri": "https://localhost:8080/" }, "id": "pCqnaKmlO1U9", "outputId": "7ef8384d-7d69-4826-da34-285f575c8f6b" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1893.6377 seconds used for training.\n", "31.56 minutes used for training.\n", "Peak reserved memory = 12.762 GB.\n", "Peak reserved memory for training = 7.168 GB.\n", "Peak reserved memory % of max memory = 86.534 %.\n", "Peak reserved memory for training % of max memory = 48.603 %.\n" ] } ], "source": [ "#@title Show final memory and time stats\n", "used_memory = round(torch.cuda.max_memory_reserved() / 1024 / 1024 / 1024, 3)\n", "used_memory_for_lora = round(used_memory - start_gpu_memory, 3)\n", "used_percentage = round(used_memory /max_memory*100, 3)\n", "lora_percentage = round(used_memory_for_lora/max_memory*100, 3)\n", "print(f\"{trainer_stats.metrics['train_runtime']} seconds used for training.\")\n", "print(f\"{round(trainer_stats.metrics['train_runtime']/60, 2)} minutes used for training.\")\n", "print(f\"Peak reserved memory = {used_memory} GB.\")\n", "print(f\"Peak reserved memory for training = {used_memory_for_lora} GB.\")\n", "print(f\"Peak reserved memory % of max memory = {used_percentage} %.\")\n", "print(f\"Peak reserved memory for training % of max memory = {lora_percentage} %.\")" ] }, { "cell_type": "markdown", "metadata": { "id": "ekOmTR1hSNcr" }, "source": [ "\n", "### Inference\n", "Let's run the model! You can change the instruction and input - leave the output blank!" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "kR3gIAX-SM2q", "outputId": "3bd13909-66e6-4d8c-9c90-1e02467e833e" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n" ] }, { "data": { "text/plain": [ "['<|begin_of_text|>Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\\n\\n### Instruction:\\nContinue the fibonnaci sequence.\\n\\n### Input:\\n1, 1, 2, 3, 5, 8\\n\\n### Response:\\n13<|end_of_text|>']" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# alpaca_prompt = Copied from above\n", "FastLanguageModel.for_inference(model) # Enable native 2x faster inference\n", "inputs = tokenizer(\n", "[\n", " alpaca_prompt.format(\n", " \"Continue the fibonnaci sequence.\", # instruction\n", " \"1, 1, 2, 3, 5, 8\", # input\n", " \"\", # output - leave this blank for generation!\n", " )\n", "], return_tensors = \"pt\").to(\"cuda\")\n", "\n", "outputs = model.generate(**inputs, max_new_tokens = 64, use_cache = True)\n", "tokenizer.batch_decode(outputs)" ] }, { "cell_type": "markdown", "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": 12, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "e2pEuRb1r2Vg", "outputId": "0132bcfc-d98e-497e-97d2-b08196d823e5" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "<|begin_of_text|>Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n", "\n", "### Instruction:\n", "Continue the fibonnaci sequence.\n", "\n", "### Input:\n", "1, 1, 2, 3, 5, 8\n", "\n", "### Response:\n", "13<|end_of_text|>\n" ] } ], "source": [ "# alpaca_prompt = Copied from above\n", "FastLanguageModel.for_inference(model) # Enable native 2x faster inference\n", "inputs = tokenizer(\n", "[\n", " alpaca_prompt.format(\n", " \"Continue the fibonnaci sequence.\", # instruction\n", " \"1, 1, 2, 3, 5, 8\", # input\n", " \"\", # output - leave this blank for generation!\n", " )\n", "], return_tensors = \"pt\").to(\"cuda\")\n", "\n", "from transformers import TextStreamer\n", "text_streamer = TextStreamer(tokenizer)\n", "_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 128)" ] }, { "cell_type": "markdown", "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": 13, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 99, "referenced_widgets": [ "a1ee3240cd8c4183a719df09de46e491", "3e494d24bbe24dff85a001ed48109a06", "57d7a9c5be8b447bb45aa6fcc689586e", "93b5d48d1bf5417e809f4d645d881c26", "f12416b8f9f048d2a5eaf01ec2979fab", "289740c23da04084b83eac5502782bd7", "793e73558d864b94a792ca4bfe5a7ae9", "171894e8268049f48fbc687624099138", "7e0221e68aee43d3aa5a9517d18bf3a6", "dd7ab5ccb0a64affbad49d29a3171a30", "c9ec98826de54d73bdec3f373a931249", "d7c7b4e679654b76b37677567238ef74", "8380f0fc107441908efbcc44898a6550", "5286e69ef9654cc6b5197475884435f4", "d08a898152fc4fc39f7a7f9c79c05ea1", "e667ff7f116a4a0cb9f37c5528bd73c5", "00e078f9c387482aa9a90b08d3fc6975", "c84126601bea4f7da2b3a04ed541650a", "3aef958eb4744c329cf254575ae14724", "742ae2669af546318d94eac084d80860", "4b031fccd97a4a5fb07bfdaa60c63fe1", "4fb338d473f4419e91a084703e27b29d" ] }, "id": "upcOlWe7A1vc", "outputId": "213f871d-82c0-47c5-ea64-ec260c039a7d" }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "a1ee3240cd8c4183a719df09de46e491", "version_major": 2, "version_minor": 0 }, "text/plain": [ "README.md: 0%| | 0.00/574 [00:00\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0munsloth\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mFastLanguageModel\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m model, tokenizer = FastLanguageModel.from_pretrained(\n\u001b[0m\u001b[1;32m 5\u001b[0m \u001b[0mmodel_name\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"BanglaLama3\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;31m# YOUR MODEL YOU USED FOR TRAINING\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0mmax_seq_length\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmax_seq_length\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/unsloth/models/loader.py\u001b[0m in \u001b[0;36mfrom_pretrained\u001b[0;34m(model_name, max_seq_length, dtype, load_in_4bit, token, device_map, rope_scaling, fix_tokenizer, trust_remote_code, use_gradient_checkpointing, resize_model_vocab, revision, *args, **kwargs)\u001b[0m\n\u001b[1;32m 170\u001b[0m \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 171\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 172\u001b[0;31m model, tokenizer = dispatch_model.from_pretrained(\n\u001b[0m\u001b[1;32m 173\u001b[0m \u001b[0mmodel_name\u001b[0m \u001b[0;34m=\u001b[0m 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Make sure you have enough GPU RAM to fit the \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 88\u001b[0m \u001b[0;34m\"quantized model. If you want to dispatch the model on the CPU or the disk while keeping these modules \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mValueError\u001b[0m: Some modules are dispatched on the CPU or the disk. Make sure you have enough GPU RAM to fit the quantized model. If you want to dispatch the model on the CPU or the disk while keeping these modules in 32-bit, you need to set `load_in_8bit_fp32_cpu_offload=True` and pass a custom `device_map` to `from_pretrained`. Check https://huggingface.co/docs/transformers/main/en/main_classes/quantization#offload-between-cpu-and-gpu for more details. " ] } ], "source": [ "# if False:\n", "if True:\n", " from unsloth import FastLanguageModel\n", " model, tokenizer = FastLanguageModel.from_pretrained(\n", " model_name = \"BanglaLama3\", # YOUR MODEL YOU USED FOR TRAINING\n", " max_seq_length = max_seq_length,\n", " dtype = dtype,\n", " load_in_4bit = load_in_4bit,\n", " )\n", " FastLanguageModel.for_inference(model) # Enable native 2x faster inference\n", "\n", "# alpaca_prompt = You MUST copy from above!\n", "\n", "inputs = tokenizer(\n", "[\n", " alpaca_prompt.format(\n", " \"What is a famous tall tower in Paris?\", # instruction\n", " \"\", # input\n", " \"\", # output - leave this blank for generation!\n", " )\n", "], return_tensors = \"pt\").to(\"cuda\")\n", "\n", "outputs = model.generate(**inputs, max_new_tokens = 64, use_cache = True)\n", "tokenizer.batch_decode(outputs)" ] }, { "cell_type": "markdown", "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": 19, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "yFfaXG0WsQuE", "outputId": "aca11729-045c-4b6d-9a81-3e0f4f5fd193" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "The `load_in_4bit` and `load_in_8bit` arguments are deprecated and will be removed in the future versions. Please, pass a `BitsAndBytesConfig` object in `quantization_config` argument instead.\n", "`low_cpu_mem_usage` was None, now set to True since model is quantized.\n" ] } ], "source": [ "# if False:\n", "if True:\n", " # I highly do NOT suggest - use Unsloth if possible\n", " from peft import AutoPeftModelForCausalLM\n", " from transformers import AutoTokenizer\n", " model = AutoPeftModelForCausalLM.from_pretrained(\n", " \"BanglaLama3\", # YOUR MODEL YOU USED FOR TRAINING\n", " load_in_4bit = load_in_4bit,\n", " )\n", " tokenizer = AutoTokenizer.from_pretrained(\"BanglaLama3\")" ] }, { "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": 16, "metadata": { "id": "iHjt_SMYsd3P" }, "outputs": [], "source": [ "# Merge to 16bit\n", "if False: model.save_pretrained_merged(\"model\", tokenizer, save_method = \"merged_16bit\",)\n", "if False: model.push_to_hub_merged(\"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: model.save_pretrained_merged(\"model\", tokenizer, save_method = \"lora\",)\n", "if False: model.push_to_hub_merged(\"hf/model\", tokenizer, save_method = \"lora\", token = \"\")" ] }, { "cell_type": "markdown", "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 on our [Wiki page](https://github.com/unslothai/unsloth/wiki#gguf-quantization-options)):\n", "* `q8_0` - Fast conversion. High resource use, but generally acceptable.\n", "* `q4_k_m` - Recommended. Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q4_K.\n", "* `q5_k_m` - Recommended. Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q5_K." ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "id": "FqfebeAdT073" }, "outputs": [], "source": [ "# Save to 8bit Q8_0\n", "if False: model.save_pretrained_gguf(\"model\", tokenizer,)\n", "if False: model.push_to_hub_gguf(\"hf/model\", tokenizer, token = \"\")\n", "\n", "# Save to 16bit GGUF\n", "if False: model.save_pretrained_gguf(\"model\", tokenizer, quantization_method = \"f16\")\n", "if False: model.push_to_hub_gguf(\"hf/model\", tokenizer, quantization_method = \"f16\", token = \"\")\n", "\n", "# Save to q4_k_m GGUF\n", "if False: model.save_pretrained_gguf(\"model\", tokenizer, quantization_method = \"q4_k_m\")\n", "if False: model.push_to_hub_gguf(\"hf/model\", tokenizer, quantization_method = \"q4_k_m\", token = \"\")" ] }, { "cell_type": "markdown", "metadata": { "id": "bDp0zNpwe6U_" }, "source": [ "Now, use the `model-unsloth.gguf` file or `model-unsloth-Q4_K_M.gguf` file in `llama.cpp` or a UI based system like `GPT4All`. You can install GPT4All by going [here](https://gpt4all.io/index.html)." ] }, { "cell_type": "markdown", "metadata": { "id": "Zt9CHJqO6p30" }, "source": [ "And we're done! If you have any questions on Unsloth, we have a [Discord](https://discord.gg/u54VK8m8tk) channel! If you find any bugs or want to keep updated with the latest LLM stuff, or need help, join projects etc, feel free to join our Discord!\n", "\n", "Some other links:\n", "1. Zephyr DPO 2x faster [free Colab](https://colab.research.google.com/drive/15vttTpzzVXv_tJwEk-hIcQ0S9FcEWvwP?usp=sharing)\n", "2. Llama 7b 2x faster [free Colab](https://colab.research.google.com/drive/1lBzz5KeZJKXjvivbYvmGarix9Ao6Wxe5?usp=sharing)\n", "3. TinyLlama 4x faster full Alpaca 52K in 1 hour [free Colab](https://colab.research.google.com/drive/1AZghoNBQaMDgWJpi4RbffGM1h6raLUj9?usp=sharing)\n", "4. CodeLlama 34b 2x faster [A100 on Colab](https://colab.research.google.com/drive/1y7A0AxE3y8gdj4AVkl2aZX47Xu3P1wJT?usp=sharing)\n", "5. Mistral 7b [free Kaggle version](https://www.kaggle.com/code/danielhanchen/kaggle-mistral-7b-unsloth-notebook)\n", "6. We also did a [blog](https://huggingface.co/blog/unsloth-trl) with πŸ€— HuggingFace, and we're in the TRL [docs](https://huggingface.co/docs/trl/main/en/sft_trainer#accelerate-fine-tuning-2x-using-unsloth)!\n", "7. `ChatML` for ShareGPT datasets, [conversational notebook](https://colab.research.google.com/drive/1Aau3lgPzeZKQ-98h69CCu1UJcvIBLmy2?usp=sharing)\n", "8. Text completions like novel writing [notebook](https://colab.research.google.com/drive/1ef-tab5bhkvWmBOObepl1WgJvfvSzn5Q?usp=sharing)\n", "9. [**NEW**] We make Phi-3 Medium / Mini **2x faster**! See our [Phi-3 Medium notebook](https://colab.research.google.com/drive/1hhdhBa1j_hsymiW9m-WzxQtgqTH_NHqi?usp=sharing)\n", "\n", "

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