{ "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 + support us if you can!\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.**" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "id": "2eSvM9zX_2d3" }, "outputs": [], "source": [ "%%capture\n", "import torch\n", "major_version, minor_version = torch.cuda.get_device_capability()\n", "# Must install separately since Colab has torch 2.2.1, which breaks packages\n", "!pip install \"unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git\"\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 --no-deps packaging ninja einops flash-attn xformers trl peft accelerate bitsandbytes\n", "else:\n", " # Use this for older GPUs (V100, Tesla T4, RTX 20xx)\n", " !pip install --no-deps xformers trl peft accelerate bitsandbytes\n", "pass\n", "# Llama 3 Video Tutorial https://www.youtube.com/watch?v=aQmoog_s8HE" ] }, { "cell_type": "markdown", "metadata": { "id": "r2v_X2fA0Df5" }, "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." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 529, "referenced_widgets": [ "e1e7f41af2f74c28ae88cffb9ba1a583", "449705b846684e11b07bd612eccd63bb", "72e02e75de1f409295171e199f344614", "0e6ff54da808434883fddbeefe77404c", "4a21b1f14f8e4e9e8c0b5cb7680582b5", "ab3c4de877594b438392faf6cbcca9a2", "6bdde0dab7f4441e9b26fac50eaf28a4", "13c84ee10ed14e9b9b087dc7fad6f010", "645f49948627408eb46e030085b6e8e5", "537efc18ed2a4cf3b67031b5f183fb14", "6b9bffae822640aeb8ff1489f51aca1d", "c204ebc5fe924816b6a2fadb25c62ecd", "7f381c92cf5b4f5780912d1d521371e8", "b257d8cd83c14323a517255ef081116a", "e27f1e28b8734238878b280c61a34f7d", "ddcfbccfedd24c39a17c7e2649ac3db0", "0a063239057944f6a5d76f0bbad75608", "a943e676d50a4d05b73b935da5f66547", "88cecf58442647ec8035eb9dddd6297b", "cc0d751d98fd4cc3abba742cd07dd14c", "435f02bef6d34df2995b7f870f8b2ac0", 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"a21cd4da51ab4ca298ba9b9f96a6ed5b", "ebffafafb3b74d0ca5c4812030c91e73", "7abcad2cabc04ac987abd23350250909" ] }, "id": "QmUBVEnvCDJv", "outputId": "ecec9211-6d9c-4215-dcae-92e5695e6a1f" }, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "config.json: 0%| | 0.00/701 [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 = 5,\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 `ChatML` template for ShareGPT datasets, try our conversational [notebook](https://colab.research.google.com/drive/1Aau3lgPzeZKQ-98h69CCu1UJcvIBLmy2?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": 6, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 430, "referenced_widgets": [ "b55fc171c5184b0cbc087a93e659bdc9", "f1e8b4ce8da34c38833e09cfd0f0ebc8", "ae1ff8cfe38e4be8b9b0929c277d71c8", "707af573667e4c97bc1b95fc2af36ea5", "da037ffe1f9440b086ef0f7033580b09", "2a21172e12b045478ea64e8f8100cf22", "33a90a481a624aa8903494f892944f91", "baf29b71ed8a4e229dab7ad9c24394ec", "841453b3338644949855cae66b9c628e", "999150067a0a444088720972ba68d48c", "2c1f0a0286c34947812468236b9ec8a6", "e5b6e9f8d0d84660ac5a1aade543e91c", "99b1823fa84f451eb93794fb8f5ff440", "c56546494c2f47bfbb69c2aae773d77b", "62ccd5619caa478a95f8bfeefd6bac3e", "ae67e273a55f4c62837229a532a28c2a", "91ba93a41e174596817b130c3ae8d393", "104f6b4230ab4365a54ac61217159be7", "272966748cb64758bf0907551bc81ed6", "ea1e4a4ec8694ca09edaf0f9a9b3e5fc", "1cacc5ba08f94a069ea19833e728f998", "72f7ea3866954327b0b19416c4f1d7e3", "74368e1d4f1643758e47cd090c5e1a3b", "48dec769280a4c88834a22fc7883b74d", "c38548400261423a80e27365ec52fdc1", "c3e25561fba9471aba576cbd850412af", "8ee1c67dcc1f4938ac12200768c576dc", "747aa21eb09c49ae8ea89d74caa755a6", "0f5c425e0f324bc5b371e51e1228075c", "3fa1f5cac3c245feb20a7a460f1d0176", "d7ede42b1e9841c3b4e7a3152ab09aac", "60a2eb023e08436f843ea9c357e6025d", "24fb2ace1e7148d5847bec768c20cf46", "12d3b69ffb7445d289ab6408253eef99", "1940759ed6ea4b47a8cdeb078b77a405", "69667a674663489aab06fa1548218d28", "e0797fbbf1614f48bc8c1f03fad38dd9", "2a09e7b995294adabb695fc8d51acae0", "768e61a71f134577915654fbd24f0668", "807833f9e9ac462ab5bbc6021526478c", "cba98298b2544cd6a2dd12d44ced821c", "97385885a6ad4eecae393141ddef3111", "a776cea0a5dd4ebfb892fd12ed5632ab", "5be9f261309546aeabfcf8ed104079e9", "8b1d0d880e864bbbaac08eda070b6409", "b5a8eae56d604d3b9fee35c8e27ef2c2", "05ae736b4d8e417eba931523b2bc0cb4", "1c52f2d6b4b64f26a3c9265fcff967c0", "8a4c3df3ef9542488f87c1fe1793efac", "fccfda73b6fa4a6fbcd035ffee4616a2", "5511034816a04147b18f917483ff7767", "0901faf08dcf4c0a9d2773e96774f559", "f2300364bd26472ab432aaeaa1b16a70", "fda24332c71f43c9bfa03c059c2e2840", "328176ebd91941ca857218bc81191682", "0866820dd77b44fe9e30113fc122c286", "2a52212e18b34926979e579025b272dd", "9c40e10d3dcc43f28f89b5d92ef6779a", "26be9c7062fe4a698afb4b1b67627306", "84a43aa505a64ad2aefa874295d74e00", "66906a9a61074655aa4289473a9840d7", "6fb01090cee0416f8c420b1b7890a280", "66125d0eb146461db16f8fa9a047c235", "08e570629bae4fbfae4bc6fd479c76ee", "a8f018b7247a4fcca1939a12870fbe31", "64f3afc819414288be76cec498ee57b6", "bdc143433db845a2ad958fbf28010536", "050cc99793274726976ac4a6aea70f3b", "18fe1b6742804376a8f27fe097af5148", "cabed00de5654e9cb2acc0cca003a2ef", "d58c3690e9b04d62af72cd774e72424f", "95fa67de9c88413c8fe45c6b418ee120", "f886afa4878c4114b6bf0b68856b8a79", "639cf131b2c7489c9c30af66b3d5ae87", "2e103205ec194cb2926ddca3cdb2b616", "1aa70f38b8e440ffbaf4e0dc9640784c", "adc85249435f4c6eb01a24f7fc79bcf1", "0cf79b97991342aeb905bd4f2a1667b9", "49058a96d8314bba9bcd64acc4e8d6e4", "e0b2f6cd35714b5b9723708f51eaa0d7", "919d6b5402db4c68b5498f78c222d975", "a0ea6ba0c95a4e7580aa4ca94ad97eb4", "c819a5f64853409ebcc0b6b82887b5e7", "1c1e3a42906e4796bf84e0cae446b658", "000951074e0a41e69ac35a0ec8b49f34", "4c28e0a0617a4477815bce4905e88fdc", "4a51999e91124f939e18e779e5acde69", "4a06cace621b4157b72dc2d447e79175", "7bd780d23f704afaa359d303ff44aaba", "01b68a5b7180438daae262cb0c9c034f", "d0956d45548941d5b74aed9d265ae15e", "9564a353fb5b46c8862a639802677d78", "0638c475c58747019589e45e2e283028", "2fb87450ef004a3a846eb7a9f40c61d9", "e7c49d6815e541599d0ed5128abecf7a", "f61cdd5f241c4fe19fa535519987148b", "39b2a1b01f094b71b2252c91876b3312", "9c0402bad67c49c7b6501e48218e1147", "922fd63539b64f39bc56c3b441ddd55a", "de9beb72398f42d089e2d5e2b46aa88f", "fe7aef6d72f9421195c82c8112542568", "26229b60a4ff4f7f90121aef7b42a96a", "6d583a36f0a64d5980ee8ad1675524db", "b3602253938d46f4890b107cc92af5d5", "2bb3c2041c5b4bc594fba4010ac93211", "e01ec40bf4274e3cb4f2310d7a57b3b5", "bd4c498ca1d747f8a1be37b02b1eaee7", "23c5f34454834186b549f18a71a00bc0", "54a5f48739144deab4fb8ccb9086ebee", "618d4ecbd2804343b2d6c952490c64aa" ] }, "id": "LjY75GoYUCB8", "outputId": "bc006038-ec7c-4e4b-9047-b9c80f250edf" }, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.10/dist-packages/datasets/load.py:1486: FutureWarning: The repository for zjunlp/Mol-Instructions contains custom code which must be executed to correctly load the dataset. You can inspect the repository content at https://hf.co/datasets/zjunlp/Mol-Instructions\n", "You can avoid this message in future by passing the argument `trust_remote_code=True`.\n", "Passing `trust_remote_code=True` will be mandatory to load this dataset from the next major release of `datasets`.\n", " warnings.warn(\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "Downloading builder script: 0%| | 0.00/7.34k [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": 7, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 124, "referenced_widgets": [ "c1620a1dea1141709375c0cdea3fa0f2", "b7ca7133f7a740a6b415bcc7ba293c08", "eb392a288ff8428a9741aae1868c73f8", "27b530fea36344aaaf6f302af6f2aba3", "2207de6faa984eb293faa1a115495af6", "b20af4776fff47e8be73c7a7338977e5", "a6b9abbfd5f84d4e87641ae7d9b17a56", "de4f9607c43843f1bd4915aa82672aec", "9ff69d4962294e7485955b7ccfcd9a0f", "635c4062af1c4e7bbc491ebef00f35d3", "254c62bd1c0643b49690081d9e19230a" ] }, "id": "95_Nn-89DhsL", "outputId": "d199dc52-6630-46ff-e845-d23507e92faf" }, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.10/dist-packages/multiprocess/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n", " self.pid = os.fork()\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "Map (num_proc=2): 0%| | 0/298319 [00:00" ], "text/html": [ "\n", "
\n", " \n", " \n", " [400/400 09:51, Epoch 0/1]\n", "
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StepTraining Loss
12.047800
22.256400
32.332900
41.902500
51.992400
62.205100
71.723600
81.569600
91.508000
101.248100
111.226400
121.376900
131.105200
141.108600
150.987600
160.919500
170.998700
180.857700
190.907000
200.883900
210.814400
220.823100
230.823800
240.832900
250.679400
260.838000
270.672300
280.698500
290.844700
300.715100
310.705900
320.722100
330.704200
340.684900
350.638000
360.684600
370.630800
380.590000
390.721000
400.593000
410.582600
420.660000
430.562400
440.655300
450.746100
460.601200
470.590600
480.643700
490.580500
500.602900
510.594800
520.566400
530.615300
540.574800
550.569800
560.584100
570.489600
580.506300
590.572400
600.689300
610.555500
620.607400
630.555600
640.595700
650.530400
660.502000
670.744700
680.547100
690.530200
700.521000
710.686500
720.769000
730.496900
740.501000
750.456200
760.643600
770.516600
780.546600
790.475000
800.453300
810.512000
820.531300
830.499100
840.521800
850.545100
860.522800
870.533000
880.463700
890.557100
900.489900
910.448300
920.461300
930.504600
940.586300
950.528500
960.510300
970.449600
980.512300
990.520300
1000.521200
1010.564200
1020.517200
1030.465300
1040.540600
1050.607300
1060.455000
1070.529700
1080.457800
1090.429700
1100.525400
1110.620800
1120.430800
1130.607800
1140.508200
1150.485400
1160.493400
1170.479500
1180.478600
1190.450500
1200.525800
1210.594400
1220.468700
1230.465000
1240.450900
1250.586800
1260.553200
1270.564800
1280.473200
1290.473200
1300.490100
1310.553600
1320.549300
1330.701700
1340.703200
1350.437700
1360.520100
1370.512100
1380.449600
1390.505000
1400.436900
1410.416500
1420.433600
1430.565600
1440.438700
1450.572800
1460.401600
1470.446000
1480.453700
1490.446300
1500.459500
1510.440000
1520.474800
1530.396700
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1550.514200
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1570.498500
1580.460400
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1600.499200
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" ] }, "metadata": {} } ], "source": [ "trainer_stats = trainer.train()" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 0 }, "id": "pCqnaKmlO1U9", "outputId": "671757bd-57d1-4ba6-ebd3-97ee0a5fbab4" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "599.135 seconds used for training.\n", "9.99 minutes used for training.\n", "Peak reserved memory = 20.236 GB.\n", "Peak reserved memory for training = 5.138 GB.\n", "Peak reserved memory % of max memory = 51.148 %.\n", "Peak reserved memory for training % of max memory = 12.987 %.\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/", "height": 0 }, "id": "kR3gIAX-SM2q", "outputId": "09b824b0-51d7-49b0-9263-2beceeed1470" }, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "Setting `pad_token_id` to `eos_token_id`:128003 for open-end generation.\n" ] }, { "output_type": "execute_result", "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, 21, 34, 55, 89, 144, 233, 377, 610, 987, 1597, 2584, 4181, 6765, 10946, 17711, 28657, 46368, 75025']" ] }, "metadata": {}, "execution_count": 11 } ], "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/", "height": 0 }, "id": "e2pEuRb1r2Vg", "outputId": "23730b73-878e-41b7-d4b0-17e427142eb0" }, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "Setting `pad_token_id` to `eos_token_id`:128003 for open-end generation.\n" ] }, { "output_type": "stream", "name": "stdout", "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, 21, 34, 55, 89, 144, 233, 377, 610, 987, 1597, 2584, 4181, 6765, 10946, 17711, 28657, 46368, 75025, 121393,...\n", "The first two numbers in the Fibonacci sequence are 1, and the sequence thereafter is obtained by summing those preceding. So, adding 1 and 2 (the first two numbers) gives 8 as the third number. The next sum results in 21 (1+8),\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": "code", "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", " \"What is a famous bridge in San Francisco bay area?\", # instruction\n", " \"\", # 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)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 0 }, "id": "Z5tWI7ivVjMg", "outputId": "772d51ba-218f-4fed-9169-c2bf0c23e5f7" }, "execution_count": 13, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "Setting `pad_token_id` to `eos_token_id`:128003 for open-end generation.\n" ] }, { "output_type": "stream", "name": "stdout", "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", "What is a famous bridge in San Francisco bay area?\n", "\n", "### Input:\n", "\n", "\n", "### Response:\n", "The Golden Gate Bridge is a world-famous suspension bridge that spans the Golden Gate strait, connecting the San Francisco, California with Marin County. Its construction started in 1933 and it was completed in 1937. Built at a time when the demand for large bridges was increasing for transportation and engineering purposes, this suspension bridge was designed by a team of engineers who drew upon the best ideas from previous bridge designs.\n", "\n", "It is known for its unique and distinct color which is International Orange, it is an orange-red color that has been specially blended. The reason for selecting this color was to make it more visible from the surrounding area because the\n" ] } ] }, { "cell_type": "code", "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", " \"What is a famous university in San Francisco bay area?\", # instruction\n", " \"\", # 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)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 0 }, "id": "9fJZQcwlVlBq", "outputId": "6da0486d-f4cf-41d1-824a-7718898793d9" }, "execution_count": 14, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "Setting `pad_token_id` to `eos_token_id`:128003 for open-end generation.\n" ] }, { "output_type": "stream", "name": "stdout", "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", "What is a famous university in San Francisco bay area?\n", "\n", "### Input:\n", "\n", "\n", "### Response:\n", "The University of California, Berkeley (also referred to as UC Berkeley, Berkeley, and Cal) is a major public research and graduate university located in Berkeley, California, United States. It is the flagship institution of the ten research universities associated with the University of California system, as well as its most prestigious one. Founded in 1868 as the University of California, it was the first university in the city and state, and one of the first, along with the original 1866 private UC Berkeley branch, to admit women. Berkeley is a member of the Association of American Universities and is one of a select group of worldwide institutions with a\n" ] } ] }, { "cell_type": "code", "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", " \"What are the DNA bases?\", # instruction\n", " \"\", # 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)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 0 }, "id": "1TPeJxviVls8", "outputId": "fa275ce9-71aa-42dc-d970-dc7f5eee37b3" }, "execution_count": 15, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "Setting `pad_token_id` to `eos_token_id`:128003 for open-end generation.\n" ] }, { "output_type": "stream", "name": "stdout", "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", "What are the DNA bases?\n", "\n", "### Input:\n", "\n", "\n", "### Response:\n", "The DNA letters are adenine (A), cytosine (C), guanine (G), and thymine (T). These bases pair with one another in a sequence that determines the order of proteins in each organism. The sequence of pairs is read from the base that is a part of deoxyribose sugar moving into the terminal phosphate. This leads to the creation of the sequence of the protein strands that is produced. Typically adenine bonds with thymine and cytosine bonds with guanine. This arrangement was first determined by Erwin Chargoff and his team in 1951 through his studies of the structure and\n" ] } ] }, { "cell_type": "code", "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", " \"What are all of the types of bonds found in DNA?\", # instruction\n", " \"\", # 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)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 0 }, "id": "GlPUCSprVnRK", "outputId": "c619fe24-f4a1-41b0-ec3f-da1a953cf09e" }, "execution_count": 16, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "Setting `pad_token_id` to `eos_token_id`:128003 for open-end generation.\n" ] }, { "output_type": "stream", "name": "stdout", "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", "What are all of the types of bonds found in DNA?\n", "\n", "### Input:\n", "\n", "\n", "### Response:\n", "[Deoxyribosyl (6-2)- and (6-1)-H-methyltransferase and (6-2) and (6-1) cytosine deaminase catalylzing DNA replication and repair pathway I activity]<|im_end|>\n" ] } ] }, { "cell_type": "code", "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", " \"What are the reaction names to create DNA?\", # instruction\n", " \"\", # 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)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 0 }, "id": "NelWwdJjVpqR", "outputId": "0413c016-61f7-43f8-e176-dc8a59a28db6" }, "execution_count": 17, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "Setting `pad_token_id` to `eos_token_id`:128003 for open-end generation.\n" ] }, { "output_type": "stream", "name": "stdout", "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", "What are the reaction names to create DNA?\n", "\n", "### Input:\n", "\n", "\n", "### Response:\n", "[C][C][Branch2][Ring2][O][C][C][Branch2][Ring1][#Branch1][N][O][P][=Branch1][C][=O][Branch1][C][O][O][O][C][C][Branch1][P][C][Branch1][=N][C][Branch1][=Branch2][C][Branch1][Ring2][O][Ring1][=Branch1][C][O][O][O][O]<|im_end|>\n" ] } ] }, { "cell_type": "code", "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", " \"What is the structure for adenine?\", # instruction\n", " \"\", # 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)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 0 }, "id": "FQw11BVmrV-f", "outputId": "ed4ab5c8-3d49-4318-f95d-9f3aa4e655cd" }, "execution_count": 18, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "Setting `pad_token_id` to `eos_token_id`:128003 for open-end generation.\n" ] }, { "output_type": "stream", "name": "stdout", "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", "What is the structure for adenine?\n", "\n", "### Input:\n", "\n", "\n", "### Response:\n", "[C][C][N][N][C][=C][Branch1][=N][O][C][Ring1][=Branch1][Cl]<|im_end|>\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": 19, "metadata": { "id": "upcOlWe7A1vc", "colab": { "base_uri": "https://localhost:8080/", "height": 0 }, "outputId": "448e9d59-e736-4084-e4ac-97cacdbc4429" }, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n", " warnings.warn(\n" ] } ], "source": [ "model.save_pretrained(\"lora_model\") # Local saving\n", "# model.push_to_hub(\"Your-Model-Name\", organization=\"kevinkawchak\", token = \"Your HF writable token\", private=True) # ONLY saves the LoRA adapters" ] }, { "cell_type": "code", "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", "# alpaca_prompt = You MUST copy from above!\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", "outputs = model.generate(**inputs, max_new_tokens = 128, use_cache = True)\n", "tokenizer.batch_decode(outputs)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 0 }, "id": "RYh575LYOG5J", "outputId": "91506973-6b34-4a03-b08f-7b8e5cd00058" }, "execution_count": 20, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "Setting `pad_token_id` to `eos_token_id`:128003 for open-end generation.\n" ] }, { "output_type": "execute_result", "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:\\nWhat is a famous tall tower in Paris?\\n\\n### Input:\\n\\n\\n### Response:\\nThe Eiffel Tower in Paris, France, is a most famous tall tower. It has an overall height of 330 meters which includes a radio antenna on top of the tower that can reach 345 meters. It was completed in 1889 and is named after Gustave Eiffel, who was the engineer responsible for the construction of the tower. It is one of the most recognized and most visited places in the city and around the world, serving both a national and international public. \\n\\nThe Eiffel Tower features three levels for visitors, with restaurants and an observation deck with a panoramic view of the city at the top level']" ] }, "metadata": {}, "execution_count": 20 } ] }, { "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": "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": 21, "metadata": { "id": "yFfaXG0WsQuE" }, "outputs": [], "source": [ "## If using model.save_pretrained(\"lora_model\") # Local saving\n", "if False:\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", " \"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": 22, "metadata": { "id": "iHjt_SMYsd3P", "colab": { "base_uri": "https://localhost:8080/", "height": 1014, "referenced_widgets": [ "cb447c5d69cf48a6bd1990077a73edfe", "2a4bebaa531d4b2c8e3d0b0349107891", "9444dc34f8324193889cc46a18e816a1", "28ce49e7b45b4e2d9a19f1d9a8eb5f41", "8657dd941d124511a7ce646f9e5fc41a", "630b965fa5be469e90c235a6dd35349a", "7921a751b90b4eaabd7200010a116416", "ef8664e369854d88b6f955a77afed49e", "bf0f3dcb22c447b4ad4021542561c6be", "ad844b3c437941d186e8909d01e32f95", "6d0b62e87e1140938d770d4d64c24281", "3f4148d1b97d4a62b61186a4c22eda76", "ccb17b49f53141bda7e20eb4b239416e", "ec17bb3c7acb4372af1cf034f299f409", "b67ca3a96e264efa898e131f6fee2685", "cfc4f7156a7e49dfa1b4942102f67e20", "554cef970c1b47c4b5d4ca44c416cbb5", "e0920897fb5248f2a7b525b4c44d43d2", "269bfae4198c42a2b03f577b33e46e2c", "1894f7348aa64bf2915e1ab8b665676f", 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We need to delete the downloaded\n", "model which will save 4-16GB of disk space, allowing you to save on Kaggle/Colab.\n", "Unsloth: Will remove a cached repo with size 16.1G\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "Unsloth: Merging 4bit and LoRA weights to 16bit...\n", "Unsloth: Will use up to 61.75 out of 83.48 RAM for saving.\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "100%|██████████| 32/32 [00:00<00:00, 65.45it/s]\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "Unsloth: Saving to organization with address kevinkawchak/NousResearch-Hermes-2-Pro-Llama-3-8B-Molecule16\n", "Unsloth: Saving tokenizer... Done.\n", "Unsloth: Saving model... This might take 5 minutes for Llama-7b...\n", "Unsloth: Saving to organization with address kevinkawchak/NousResearch-Hermes-2-Pro-Llama-3-8B-Molecule16\n", "Unsloth: Uploading all files... Please wait...\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "model-00001-of-00004.safetensors: 0%| | 0.00/4.98G [00:00\n", " \n", " \n", " Support our work if you can! Thanks!\n", "" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "id": "zmdJjv6-GarM" }, "outputs": [], "source": [ "# from google.colab import runtime\n", "# runtime.unassign()" ] } ], "metadata": { "accelerator": "GPU", "colab": { "gpuType": "A100", "machine_shape": "hm", "provenance": [] }, "kernelspec": { "display_name": "Python 3", "name": "python3" }, "language_info": { "name": "python" }, "widgets": { "application/vnd.jupyter.widget-state+json": { "e1e7f41af2f74c28ae88cffb9ba1a583": { "model_module": "@jupyter-widgets/controls", "model_name": "HBoxModel", "model_module_version": "1.5.0", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HBoxView", "box_style": "", "children": [ "IPY_MODEL_449705b846684e11b07bd612eccd63bb", 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