{ "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": "8LKIA_qnVKOz" } }, { "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": [ "1ddd1a7f731a42ea86f8f38907200c0d", "b06f3ae2d51748649473e5fd50c7d965", "df8d35e0ac574c9e86feb01568070798", "acd1e9b8d7804b00903d131cf0375df4", "ffa07e2231044b77acd56ebc2d41de21", "6a93358b89fd48abae84e563f1c32a36", "9c1c57caebfb4974abf75895be7e8a3d", "ef7532bbe1cd49dd96e432c597ec6dfa", "17784e5eadeb416fbb000dcfc66f4699", "1f97459b01b340038dd02120127472c3", "6cd5a1dd2da3469bbf2d16d12147fa50", "21a946400f2d4b3b9abc6074f9ac9dbd", "1522d662921b4e4485b8e5da3680ba24", "3aba6932c94645be9956bc2bcc1cf6f7", "c1775a590f0e4ea0abb982beb2000505", "fea68882982841b2a01ee4e435ffca87", "a0eb963673924ae3924c0ad37c40b32b", "dcafeb2b8a544ee7afc7570d38d1d9b0", "43ff3d3bc58e4e248d13a7a43d567e17", "a9a7e603562b4989804f33f74d6d5abb", "6e4a9de6ddee4f449d1a8b9c3e169b28", "8a1672d5c6cd4cac9f68e204c640e452", "fba46c7d2ad54a429ee903d865effc9f", "2e4a655856e9418286cd9db6b0c1cad8", "893318c3ee734f66afa41f8ee171a1dd", "e3ecf8bf5fbe4d8aa2443fe793d1e0a0", "db1e295ae0a3440c8b89c11c45145d2d", "219e0398ef174b7f83e71aab9b648457", "9f48d5d3a1484b4ea8bf3dbdcd4daaf4", "78533fbf91114a6c96dfdb91652dbc49", "9356237ba6c54248a06740fc5a77c0c3", "54a1c824681a43e58249fc8a234099e2", "74b4889ab46a4d8c89d71923b507bb30", "adca77c0f10941f68b210db4f70c223b", "00f3afe5af164337b85c24e9121c1a49", "d147d316cf594e1581f5048bd166f94b", "1ba54d426db74897934536ca7f5a7a94", "51f80559018c4e69ad42c9059a288068", "5a32d969c46c4db589c8c18c2e126e96", "04ac6e789d4b42268dce88e4d220ab24", "cf941c99606b4b04b8f7e3fd4222d4a5", "2b6dd1d1c91f4c59b7bfa0c12183b49a", "90ec3c2f4d1b4637ade135e6cd850fe3", "3bd2b78275cd46bfbe34234cab80a0a4", "fde24c4c5fdb4f3eb9a9e9ded49e0514", "aeb6fbb915d2403cab1cf9e01fd920cb", "672efe4e856b475d9e9f8f4b21de77cd", "f6f55e4060fe4786a473f9e92dbd2123", "e731171392144a44b9ad1c00e053d7c2", "d63af63319bf477db65cf82798e3e94a", "7a51811cd62f417188fc9def136f75c2", "a1f5f349e4914449baac681adf8d9ffa", "e1d7d4a1ad3943cfa1c6c393b3ec8fa9", "07c1dac7860449959f98875095428c8e", "9c16f94a447044fd812a5e07b455c1e1", "ee33b6b5166d4ed5af4ac37fffe98960", "9b816ccaf4244a56837ece478512305e", "e9dccbaa8edd4f498ecac527690387e3", "a7876f1a32e747a6b9b27832869c6465", "b88962551870425bb2d9ecb447aa3ccb", "28478a5b79c744d8a59a1fefc8d13ddd", "085783b0fbed4614bddb27bae28872ba", "6a5da30b96f540f5a269d4a65ce62ac2", "b8e81706d00b4c909a5ec0af9c268781", "0553ab1878d54ae29cf7053949b25be8", "d66e9ae45ef54030b88e2346b229ceba", "1782ff6a8d3a409d9c1ff8c17aebea0e", "095e79dbaa0e4850bd5a87c5850c8469", "94071f8fcd7243e1848896e6187424c2", "efa7c56d7faa4fd5b693ab307f9e05a4", "b94592b0393a43cd8ccf27347ec0aed4", "62d976d654804d03880e4d6bcb81ba23", "c838747d204840e6891aa9d4169a016e", "e89c1d0d9dba4d18be3d220ab88d50be", "5b71e3a911764babbc6442da73157f7d", "60c1a75bb74341a9af04c3b33d29ceb6", "192deb5485594bf7a2357fa3e66d3ba8" ] }, "id": "QmUBVEnvCDJv", "outputId": "de7c7f66-beb7-4088-bbf5-e2afa8bb2784" }, "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%| | 0.00/1.10k [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, # Currently only supports dropout = 0\n", " bias = \"none\", # Currently only supports bias = \"none\"\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": [ "1ad8100179344b659456d1519992a20c", "47a30e51056a466d9ff38c18ae7c92b2", "0e9c4d435701492d996625132ff8e89f", "babd391a882c457fbf5ccac408d0d0e0", "53feae2f6bce4f5e9fe46ed75562b74b", "844287e9ecd346b7a5e38a92abfd00d8", "f9c1c27d6b9c41abb511c0225d3a98a4", "10d56c6612c74a4680b6e64f9cf28057", "a63dc78b5c334f2882319ded4d1a31f1", "5989a3edb28a43f9aa7d23d6d5698394", "15e02c7908d74d8caf707be000199dce", "d90cb12c521e460d995f86a37dbd7cbd", "7e86d8407a27483fb3cfc6189884b143", "ea40a9a523394410be01e84e09335d43", "461b3cbd65a54201a6614732730c5052", "4eb32b35bff14fb2a3cd583758952136", "2d275f36c9484f4987cbbbf3817c6e60", "c67b841b536141258f943dbb8e533448", "4d9d28b5489d4919abd0e1efaac3c2cc", "1240fcb23e8f4f67be6a17f7f92a4119", "51f0c1b88ca44b2c894d87b67db91a17", "86ddac5d48644e7db99f93c3d1bdf706", "b056f9720512470eb013d3f9670dcff1", "9a16aae1c2444ad297a30cefbd52bd33", "7839c5a4623b4860822e29d5e1c228ca", "8fe1c9ceaaa649cba7bd8818d6b10ada", "321ce162f5354a2c8da91420305defc3", "44754c1c65164f2390b189301e8fcea7", "d845da7c591e4f158bf27a6e7a3d6052", "84d6a93ac4b04fe888544ddc7b48ab0c", "89766eb5651e40e0baf71e1d34cf236b", "b7b6dd26c0204349bb94f7bf05d863b9", "e83d0e8438cb423d9533de385d48e660", "7314cd3f8b444f33bbe8e8f315585c55", "06aa73c3ee5b43ffaa3b4e52f7d42a93", "7c6b1c53823d4cde8d293cd47d65d09f", "d1dec186275d44b9a69876e6a09a2663", "63342701c31c45548c010b54269ba904", "d7eb017ccb054711bc63c55e72766e1f", "614e5b9476c24f2aa3c14d766c260594", "473bba705dbf43eb8ea225563d575799", "c8e41ed860554289b6f96e73f6514026", "98670c4c6b4242db9cbb26282eeb0a69", "e8774961e8314a268a9d977f40226e99" ] }, "cellView": "form", "outputId": "58b732a7-4fdf-4131-f8ad-653f5e6845d5" }, "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": [ "62bf1a9297c44ab1984201d97200b60a", "228660bc715a43da8eac4c7585e16bac", "861f5cab34fa47718b8da2e5c5426ce5", "b17bc865041b43999440ab80271f0610", "6b5cab72791b481d80c73ea95969962e", "692a42425ecf4da9a562803c22d522e0", "87fcc05152694fed9f5cac6c35a7d8bd", "25087a506e6b4ee5827c87804f4dfdc2", "28eae49a2ca2422cb0666a8dd2ddf57b", "c40aa48eed1741b6997f6bf23fee7057", "c7b9b43135e342d2b659839c31db90d1" ] }, "outputId": "0f8efda2-8dbd-4b6b-e56e-0132183591c5" }, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "Map: 0%| | 0/51760 [00:00" ], "text/html": [ "\n", "
\n", " \n", " \n", " [60/60 06:57, 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
11.553400
22.030900
31.562900
41.765400
51.518000
61.589000
71.251600
81.235800
91.129500
101.202800
110.922900
120.896000
130.906600
140.990200
150.816700
160.856300
170.929600
181.094100
190.890500
200.771500
210.867300
220.895800
230.800700
240.858700
250.923800
260.980100
270.920300
280.800100
290.754100
300.805100
310.757400
320.752700
330.803100
340.713000
350.839500
360.772600
370.775400
380.692200
390.933300
400.991700
410.779300
420.847800
430.765000
440.820400
450.773500
460.813200
470.772400
480.998300
490.805000
500.867900
510.864400
520.795100
530.876900
541.019400
550.762300
560.904300
570.768700
580.716400
590.712200
600.768200

" ] }, "metadata": {} } ], "source": [ "trainer_stats = trainer.train()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "pCqnaKmlO1U9", "cellView": "form", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "8a7a7eee-c329-4337-bcc7-ee5946a4b985" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "429.4844 seconds used for training.\n", "7.16 minutes used for training.\n", "Peak reserved memory = 5.453 GB.\n", "Peak reserved memory for training = 1.332 GB.\n", "Peak reserved memory % of max memory = 36.975 %.\n", "Peak reserved memory for training % of max memory = 9.032 %.\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": "ad759299-9d63-465c-a4ee-28d60feef175" }, "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:\\nContinue the fibonnaci sequence.\\n\\n### Input:\\n1, 1, 2, 3, 5, 8\\n\\n### Response:\\n2, 3, 5, 8, 13, 21, 34, 55, 89, 144, 233, 377, 610, 987, 1597, 2584, 4181, 6765, 10946, 17714, 28657, 46368, 75025, 121429, 1968']" ] }, "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": [ "To save to `GGUF` / `llama.cpp`, or for model merging, use `model.merge_and_unload` first, then save the model. See this [issue](https://github.com/ggerganov/llama.cpp/issues/3097) on llama.cpp for more info." ], "metadata": { "id": "1bSoOaDunRPL" } }, { "cell_type": "code", "source": [ "model = model.merge_and_unload()" ], "metadata": { "id": "xcRjsZe0RK1b", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "cd1025e7-9d24-4262-ff02-e4332f532ad3" }, "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": [ "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": "89236a12-69ff-42e3-8178-b7d039d19655" }, "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 was built as the entrance arch to the 1889 World\\'s Fair. It is one of the most recognizable symbols of France and is the most-visited paid monument in the world. The tower stands at 324 meters tall and is the tallest structure in Paris. It is a cultural icon and has been featured in many movies, including the famous scene in the movie \"An American in Paris\" where the character played by Gene Kelly dances on']" ] }, "metadata": {}, "execution_count": 13 } ] }, { "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", " \n", " \n", " \n", "
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