{ "cells": [ { "cell_type": "markdown", "source": [ "To run this, press \"Runtime\" and press \"Run all\" on a **free** Tesla T4 Google Colab instance!\n", "
\n", " \n", " \n", " Join our Discord if you need help!\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)." ], "metadata": { "id": "IqM-T1RTzY6C" } }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "2eSvM9zX_2d3" }, "outputs": [], "source": [ "%%capture\n", "import torch\n", "major_version, minor_version = torch.cuda.get_device_capability()\n", "if major_version >= 8:\n", " # Use this for new GPUs like Ampere, Hopper GPUs (RTX 30xx, RTX 40xx, A100, H100, L40)\n", " !pip install \"unsloth[colab_ampere] @ git+https://github.com/unslothai/unsloth.git\"\n", "else:\n", " # Use this for older GPUs (V100, Tesla T4, RTX 20xx)\n", " !pip install \"unsloth[colab] @ git+https://github.com/unslothai/unsloth.git\"\n", "pass\n", "\n", "!pip install \"git+https://github.com/huggingface/transformers.git\" # Native 4bit loading works!" ] }, { "cell_type": "markdown", "source": [ "* We support Llama, Mistral, CodeLlama, TinyLlama, Vicuna, Open Hermes etc\n", "* And Yi, Qwen ([llamafied](https://huggingface.co/models?sort=trending&search=qwen+llama)), Deepseek, all Llama, Mistral derived archs.\n", "* We support 16bit LoRA or 4bit QLoRA. Both 2x faster.\n", "* `max_seq_length` can be set to anything, since we do automatic RoPE Scaling via [kaiokendev's](https://kaiokendev.github.io/til) method.\n", "* [**NEW**] With [PR 26037](https://github.com/huggingface/transformers/pull/26037), we support downloading 4bit models **4x faster**! [Our repo](https://huggingface.co/unsloth) has Llama, Mistral 4bit models." ], "metadata": { "id": "r2v_X2fA0Df5" } }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 438, "referenced_widgets": [ "33654c38847c4fbea61fd33aba4897e6", "53b5f3f173794702b88f3ec02ea27c49", "e863762a7346415da9cebb7c89415b34", "20457665371a4183813d08b213b62936", "88f6415a2e3b4020a586c11d86bae0fb", "66a31666936342b882d8d677a2586eba", "c8e42da29b8843a1a91df4f62597f897", "0abfe64c39ce4984b2a7423af42c05e3", "8b0f6cadfb8f4b3ba14059629cb43ec7", "fbffe559df824fa392abaaafd5c27f4d", "c8fd636900ea4342927f7b929e1165cc", "f37f1288a1d345daac6f00df2148dffc", "022a936488a8492996a5177d8da49757", "83a02e755c73486ab97793e9aa2afdda", "bbc6352c1927436eb98df146ef13ed1a", "8adc8c844b914321b9ff037d5cf4004d", "a0aa772f33ff49c4810c89d239b8fd79", "de52728ff67d4147b4e3e48bd25e7db5", "14859da4f6db4ab7852ec47edca7a5c1", "2201cd0bb6f749e5ae7af6cfb6343c7f", "621d0e4a368542e0b97e74dba1f920e2", "f0b57eb2d38949078c6fde5254c4c409", "5171167f780b4fffb6a5cf20dcdd57cc", "537af784ede84b2f8c9068930b6083fa", "c4bfd4dca03848d7ac1d4e2b606add4e", "f8c61681037e4591bf7f566f4d96bde3", "6b3826d2e7cf462bb4f9a97bf1664d70", "ba6607388ed342ec846509c203a2e998", "b5e1f0fd17424ef89080b447d3d137ce", "b524938f90fe42b69ffd16b70986d8f1", "c0149ebc632c49069772ba66688578ee", "ff94abdbf39d40018a79ddfd47092f82", "c32910dcd97542648ea8a2f996747297", "a436d851fce3499094c0eecf714c7eea", "f0cd10765c9c49d7943ade76c0cbfe89", "8d21cccc680540acb0d58e564d80c740", "012e945a2d9541808753f9b807d1532b", "b1f0c6e80ae64aeaa6d80eb07ebf48a8", "00e619ebfdc242c88c11ce7571c06eab", "d2c2bb0c4ab54bdca2ed2d523e8de605", "aea3e1fda5c341be93d0d92fe8cec0c5", "7ac54d4690ae4c3ea9720c1f11b05e3c", "c68164601bdb4ae1930d63d9c5825527", "7d3df17cb7be4b54afd4b29671adcc0b", "03d87ed1a3034b41bced14363e6586a6", "d2181b8d49d74e4db99046fa984f84d6", "88d6253e09f9443b878e998cccd0475f", "ee947e65a2de4be3992d437255eb6b79", "9f7436db266549608914f19cb829f1a0", "2a220b5c6c3d4425ab0827f3d1c5f2d9", "acb48a7e500f44bdb2299dc2f5aaa234", "51a60298637942448b0c187064a0d787", "2dbaa15179e74c1989dd694b9dffade4", "d1e2cd8972ef4da980421db1b3e86790", "93d625452c35451e9bfe3e8251aa1834", "7ef49647229b4073995c9775a0c474d4", "33984170861b44c4bd414a1e646846e1", "acc25bb24ff14d40a6da7d645e3ad053", "77b4e0d785ee4f59b727d16977d5eeed", "cf210a991e494fb5ab4ca01e83c16799", "f076f7afba1948e0989af1421f55ebf3", "f7702ab51b3c49fe8840c7f48dca46ef", "7b232baef48445fe82298703baae6a3c", "965ab9d123cd46e58005c800c7b7ca35", "64b34e25b9dd4f9990f92fac2c0ca4d4", "66afc9d21d7a4576926aff070b01a170", "592e9bb20547467297a7221a8cff2515", "8ec79a75217c43699d48fbe0bd40416d", "44ea52d087eb4651b42096185032d43e", "b73741bf031e44b690bd758456f638bd", "a8ba82122b9e4fd8ae7265cd7d4cd6af", "c66855fa2e0d449ba328c82c322f424b", "9853a38b50024353aad069fb89b3eb7d", "5d78e463e5c44f4ea0e1c44a37fb8ae4", "3146bc1050644cf0a1bb16df0d5d5e44", "5902ba99a5f943aea539ca47e91cba5a", "5add234b97854f1db5e23cb7849e623e" ] }, "id": "QmUBVEnvCDJv", "outputId": "9e920276-3f96-48aa-c136-8e97d69e7c53" }, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.10/dist-packages/unsloth/__init__.py:67: UserWarning: CUDA is not linked properly.\n", "We shall run `ldconfig /usr/lib64-nvidia` to try to fix it.\n", " warnings.warn(\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "config.json: 0%| | 0.00/1.05k [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", " use_gradient_checkpointing = True,\n", " random_state = 3407,\n", " max_seq_length = max_seq_length,\n", ")" ] }, { "cell_type": "markdown", "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)." ], "metadata": { "id": "vITh0KVJ10qX" } }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "LjY75GoYUCB8", "colab": { "base_uri": "https://localhost:8080/", "height": 145, "referenced_widgets": [ "ea8764cf06604ba4ac96fd28a66a7461", "45992f61f8cf4da9825463f5ae290f66", "c5ff72ce12494a7eadbafc1192dac93c", "1bfb3f5439204526a36e6bfa2ecba5de", "82143e4f3d3c488b89628698b8970ab7", "cabfb611179549d4ac9a0537716b5929", "b0d4567f91b24ed4bf30d43bf728f87f", "9b24944c91ee45e9911a4d8ddef78e75", "7d5b4744fb934126afcdd430c8f748f6", "c56999b4c54244539faceb214625e8b7", "2b4f862927924016b3a99abd8b4808f7", "f6b71b55bc144a638cf642061d661354", "37b002b142a24b5e809446286eb54f41", "d1ecf548d52c47f5b4e55b2ecae44e7a", "ddc3498d387e48399d1d4370d4f49a4f", "71e7fbdf0c65424ba5ed7822437151bc", "0ed128938db14b6b8f0c865909ff836a", "e0ecdc1fb1404975a6076a41835aa473", "da967c697bb24274b63f4d59e5188524", "db887203ee8f46f4bf07530b529bd420", "5b91df05162c465f9a612c652cc4b2fa", "ca91be5dc4e2481c92a263d1e3c6fb68", "669ae84508644d1590c6240b53f75f2c", "2d04b4a899c44836b1f2685569b9fea5", "af88ece8f6b24b08a753b229a364d9f2", "388ca302d0674d069bef7fe6b876e064", "5ad0263fe4984b738ebe1d4d8b0377b7", "4db33220da1545618b782b30fab2bd88", "f2a6314d757b4da782f9f1a450d88583", "1d31160c12ad4a32b498323bfba257cf", "aa8a99500a98486d8039fa5fb9fdb1ea", "1cbbae94fcde414bb137e7cde45d3536", "f8cef4dedec84a83b2545319b4c6c042", "1c40faad752444a0830cb0476864b06a", "a5a25334b54f4082af27bff696c39132", "a8e388cca90846af9c32bdf372766b6e", "75e585db02e94b358408c8268ec8a26f", "893243916ac34cf4967075259f07aecd", "84922628bb85478eb2acae6082b11dd0", "0afc5bad19c8499ca44572d338fd0746", "4f0444c5544c48318e9c90f7265666f9", "2f175e4216694b04ac6a5f56aa51c29f", "1ed3b8290e604f67bf8461082a4d9e52", "bbb893b29e7045a99a6210af5e13faec" ] }, "cellView": "form", "outputId": "cd525794-f56f-4aaf-9d8f-4049d6390c42" }, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "Downloading readme: 0%| | 0.00/11.6k [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`!" ], "metadata": { "id": "idAEIeSQ3xdS" } }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "95_Nn-89DhsL", "colab": { "base_uri": "https://localhost:8080/", "height": 49, "referenced_widgets": [ "6990362a61794020b01d2cda10fbffcf", "ccae22f63683472e851fbf50c960d511", "d93703cc45164742ae17aac139b3f4d9", "5125d99599224261be728ccb82c8e228", "c8fb186fab87419dbef4c7061fc56a1b", "0edb2c7d169f4095a6f98b085c9db5b4", "efab873fe53d40929e2620f5e1466b62", "9dab8f7114fe48c9b7443579bd34cd47", "7852d732d8bc4b2a9ec14378ec3c2480", "f1a7c874ef0941b5b07d3c611329a291", "336de31e1f004f728ca58d71f128dec8" ] }, "outputId": "98a493f5-07aa-4c9c-d10d-359808726771" }, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "Map: 0%| | 0/51760 [00:00" ], "text/html": [ "\n", "
\n", " \n", " \n", " [60/60 07:46, 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
11.405300
21.828100
31.339200
41.318000
51.098300
61.023400
70.786400
80.950500
90.844500
100.938900
110.761300
120.865100
130.806400
140.925700
150.750900
160.767600
170.860900
181.050300
190.851200
200.713400
210.793700
220.846300
230.744200
240.804300
250.894200
260.905200
270.889600
280.738900
290.738500
300.773200
310.738800
320.746900
330.831700
340.715300
350.790600
360.739500
370.754900
380.632600
390.916500
400.968300
410.741100
420.820000
430.758800
440.735800
450.746500
460.792800
470.700500
480.964900
490.743800
500.848600
510.850200
520.795500
530.839400
540.982500
550.660100
560.875300
570.747800
580.676100
590.693200
600.753600

" ] }, "metadata": {} } ], "source": [ "trainer_stats = trainer.train()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "pCqnaKmlO1U9", "cellView": "form", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "3fc393e1-6f2b-43e5-9e51-5f74227d5e5f" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "479.1353 seconds used for training.\n", "7.99 minutes used for training.\n", "Peak reserved memory = 6.564 GB.\n", "Peak reserved memory for training = 1.939 GB.\n", "Peak reserved memory % of max memory = 44.508 %.\n", "Peak reserved memory for training % of max memory = 13.148 %.\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", "source": [ "\n", "### Inference\n", "Let's run the model! You can change the instruction and input - leave the output blank!" ], "metadata": { "id": "ekOmTR1hSNcr" } }, { "cell_type": "code", "source": [ "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", "]*1, return_tensors = \"pt\").to(\"cuda\")\n", "\n", "outputs = model.generate(**inputs, max_new_tokens = 128, use_cache = True)\n", "tokenizer.batch_decode(outputs)" ], "metadata": { "id": "kR3gIAX-SM2q", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "f3b0dba3-ad15-4a5b-bebf-dea3fba93645" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py:1547: UserWarning: You have modified the pretrained model configuration to control generation. This is a deprecated strategy to control generation and will be removed soon, in a future version. Please use and modify the model generation configuration (see https://huggingface.co/docs/transformers/generation_strategies#default-text-generation-configuration )\n", " warnings.warn(\n" ] }, { "output_type": "execute_result", "data": { "text/plain": [ "[' 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, 21, 34, 55, 89, 144, 233, 377, 610, 987, 1597, 2584, 4181, 6765, 10946, 17711, 28657, 46368, 75025, 121393, 196418, 317811, ']" ] }, "metadata": {}, "execution_count": 9 } ] }, { "cell_type": "markdown", "source": [ "\n", "### Saving, loading finetuned models\n", "To save the final model, either use Huggingface's `push_to_hub` for an online save or `save_pretrained` for a local save." ], "metadata": { "id": "uMuVrWbjAzhc" } }, { "cell_type": "code", "source": [ "model.save_pretrained(\"lora_model\") # Local saving\n", "# model.push_to_hub(\"your_name/lora_model\") # Online saving" ], "metadata": { "id": "upcOlWe7A1vc" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "Now if you want to load the LoRA adapters we just saved, we can!" ], "metadata": { "id": "AEEcJ4qfC7Lp" } }, { "cell_type": "code", "source": [ "from peft import PeftModel\n", "model = PeftModel.from_pretrained(model, \"lora_model\")" ], "metadata": { "id": "MKX_XKs_BNZR" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "Finally, we can now do some inference on the loaded model." ], "metadata": { "id": "f8pvYYN9DvbN" } }, { "cell_type": "code", "source": [ "inputs = tokenizer(\n", "[\n", " alpaca_prompt.format(\n", " \"What is the famous tower in France called?\", # instruction\n", " \"\", # input\n", " \"\", # output\n", " )\n", "]*1, return_tensors = \"pt\").to(\"cuda\")\n", "\n", "outputs = model.generate(**inputs, max_new_tokens = 128, use_cache = True)\n", "tokenizer.batch_decode(outputs)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "qWyyUoDq71jt", "outputId": "ab7ac76d-9e4b-4aab-9134-2e84527c0f25" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "[\" 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:\\nWhat is the famous tower in France called?\\n\\n### Input:\\n\\n\\n### Response:\\nThe famous tower in France is called the Eiffel Tower. It is a wrought-iron lattice tower located on the Champ de Mars in Paris, and is one of the most recognizable structures in the world. It was built in 1889 as the entrance arch to the 1889 World's Fair, and was named after its designer, Gustave Eiffel. The tower stands 324 meters (1,063 feet) tall, and is the tallest structure in Paris. It is a popular tourist attraction, and is one of the most visited monuments in the\"]" ] }, "metadata": {}, "execution_count": 13 } ] }, { "cell_type": "markdown", "source": [ "To save to `GGUF` / `llama.cpp`, we support it natively now! You can also go to our dedicated GGUF notebook [here](https://colab.research.google.com/drive/14DW0VwuqL2O3tqGlX7aUF6TOBA8S59M4?usp=sharing). Select either `save locally` for local saving or `save locally and quantize to 4bit` for 4bit quantization for llama.cpp / GGUF." ], "metadata": { "id": "TCv4vXHd61i7" } }, { "cell_type": "code", "source": [ "#@title Code for conversion to GGUF\n", "def colab_quantize_to_gguf(save_directory, quantization_method = \"q4_k_m\"):\n", " from transformers.models.llama.modeling_llama import logger\n", " import os\n", "\n", " logger.warning_once(\n", " \"Unsloth: `colab_quantize_to_gguf` is still in development mode.\\n\"\\\n", " \"If anything errors or breaks, please file a ticket on Github.\\n\"\\\n", " \"Also, if you used this successfully, please tell us on Discord!\"\n", " )\n", "\n", " # From https://mlabonne.github.io/blog/posts/Quantize_Llama_2_models_using_ggml.html\n", " ALLOWED_QUANTS = \\\n", " {\n", " \"q2_k\" : \"Uses Q4_K for the attention.vw and feed_forward.w2 tensors, Q2_K for the other tensors.\",\n", " \"q3_k_l\" : \"Uses Q5_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else Q3_K\",\n", " \"q3_k_m\" : \"Uses Q4_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else Q3_K\",\n", " \"q3_k_s\" : \"Uses Q3_K for all tensors\",\n", " \"q4_0\" : \"Original quant method, 4-bit.\",\n", " \"q4_1\" : \"Higher accuracy than q4_0 but not as high as q5_0. However has quicker inference than q5 models.\",\n", " \"q4_k_m\" : \"Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q4_K\",\n", " \"q4_k_s\" : \"Uses Q4_K for all tensors\",\n", " \"q5_0\" : \"Higher accuracy, higher resource usage and slower inference.\",\n", " \"q5_1\" : \"Even higher accuracy, resource usage and slower inference.\",\n", " \"q5_k_m\" : \"Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q5_K\",\n", " \"q5_k_s\" : \"Uses Q5_K for all tensors\",\n", " \"q6_k\" : \"Uses Q8_K for all tensors\",\n", " \"q8_0\" : \"Almost indistinguishable from float16. High resource use and slow. Not recommended for most users.\",\n", " }\n", "\n", " if quantization_method not in ALLOWED_QUANTS.keys():\n", " error = f\"Unsloth: Quant method = [{quantization_method}] not supported. Choose from below:\\n\"\n", " for key, value in ALLOWED_QUANTS.items():\n", " error += f\"[{key}] => {value}\\n\"\n", " raise RuntimeError(error)\n", " pass\n", "\n", " print_info = \\\n", " f\"==((====))== Unsloth: Conversion from QLoRA to GGUF information\\n\"\\\n", " f\" \\\\\\ /| [0] Installing llama.cpp will take 3 minutes.\\n\"\\\n", " f\"O^O/ \\_/ \\\\ [1] Converting HF to GUUF 16bits will take 3 minutes.\\n\"\\\n", " f\"\\ / [2] Converting GGUF 16bits to q4_k_m will take 20 minutes.\\n\"\\\n", " f' \"-____-\" In total, you will have to wait around 26 minutes.\\n'\n", " print(print_info)\n", "\n", " if not os.path.exists(\"llama.cpp\"):\n", " print(\"Unsloth: [0] Installing llama.cpp. This will take 3 minutes...\")\n", " !git clone https://github.com/ggerganov/llama.cpp\n", " !cd llama.cpp && make clean && LLAMA_CUBLAS=1 make -j\n", " !pip install gguf protobuf\n", " pass\n", "\n", " print(\"Unsloth: [1] Converting HF into GGUF 16bit. This will take 3 minutes...\")\n", " !python llama.cpp/convert.py {save_directory} \\\n", " --outfile {save_directory}-unsloth.gguf \\\n", " --outtype f16\n", "\n", " print(\"Unsloth: [2] Converting GGUF 16bit into q4_k_m. This will take 20 minutes...\")\n", " final_location = f\"./{save_directory}-{quantization_method}-unsloth.gguf\"\n", " !./llama.cpp/quantize ./{save_directory}-unsloth.gguf \\\n", " {final_location} {quantization_method}\n", "\n", " print(f\"Unsloth: Output location: {final_location}\")\n", "pass\n" ], "metadata": { "cellView": "form", "id": "nCVtR2ElF1GX" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "from unsloth import unsloth_save_model\n", "\n", "# Change to `save locally` to save a float16 GGUF file or `\"save locally and quantize to 4bit\"`\n", "# to quantize down to 4bit\n", "SAVE_STRATEGY = \"none\"\n", "\n", "if SAVE_STRATEGY == \"save locally\":\n", "\n", " unsloth_save_model(model, tokenizer, \"output_model\")\n", "\n", "elif SAVE_STRATEGY == \"save locally and quantize to 4bit\":\n", "\n", " unsloth_save_model(model, tokenizer, \"output_model\")\n", " colab_quantize_to_gguf(\"output_model\", quantization_method = \"q4_k_m\")" ], "metadata": { "id": "FqfebeAdT073" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "Otherwise, to merge the LoRA adapters into the 4bit model:" ], "metadata": { "id": "acUVCgzzU1Wv" } }, { "cell_type": "code", "source": [ "model = model.merge_and_unload()" ], "metadata": { "id": "xcRjsZe0RK1b", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "b40d4730-b9dc-4a4b-e42c-c191060ff66c" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.10/dist-packages/peft/tuners/lora/bnb.py:229: UserWarning: Merge lora module to 4-bit linear may get different generations due to rounding errors.\n", " warnings.warn(\n" ] } ] }, { "cell_type": "markdown", "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. Llama 7b [free Kaggle](https://www.kaggle.com/danielhanchen/unsloth-alpaca-t4-ddp)\n", "6. We also did a [blog](https://huggingface.co/blog/unsloth-trl) with 🤗 HuggingFace, and we're in the TRL [docs](https://huggingface.co/docs/trl/main/en/sft_trainer#accelerate-fine-tuning-2x-using-unsloth)!\n", "\n", "

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