diff --git "a/Alpaca_+_Llama_7b_full_example.ipynb" "b/Alpaca_+_Llama_7b_full_example.ipynb" --- "a/Alpaca_+_Llama_7b_full_example.ipynb" +++ "b/Alpaca_+_Llama_7b_full_example.ipynb" @@ -3,11 +3,11 @@ { "cell_type": "markdown", "source": [ - "To run this, press \"Runtime\" and press \"Run all\" on a **free** Tesla T4 Google Colab instance!\n", + "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", + " 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", @@ -59,97 +59,102 @@ "metadata": { "colab": { "base_uri": "https://localhost:8080/", - "height": 438, + "height": 527, "referenced_widgets": [ - "1ddd1a7f731a42ea86f8f38907200c0d", - "b06f3ae2d51748649473e5fd50c7d965", - "df8d35e0ac574c9e86feb01568070798", - "acd1e9b8d7804b00903d131cf0375df4", - "ffa07e2231044b77acd56ebc2d41de21", - "6a93358b89fd48abae84e563f1c32a36", - "9c1c57caebfb4974abf75895be7e8a3d", - "ef7532bbe1cd49dd96e432c597ec6dfa", - "17784e5eadeb416fbb000dcfc66f4699", - "1f97459b01b340038dd02120127472c3", - "6cd5a1dd2da3469bbf2d16d12147fa50", - "21a946400f2d4b3b9abc6074f9ac9dbd", - "1522d662921b4e4485b8e5da3680ba24", - "3aba6932c94645be9956bc2bcc1cf6f7", - "c1775a590f0e4ea0abb982beb2000505", - "fea68882982841b2a01ee4e435ffca87", - "a0eb963673924ae3924c0ad37c40b32b", - "dcafeb2b8a544ee7afc7570d38d1d9b0", - 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"outputId": "de7c7f66-beb7-4088-bbf5-e2afa8bb2784" + "outputId": "39744db3-80c8-44d0-b082-66069f0be4d0" }, "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", + "/usr/local/lib/python3.10/dist-packages/unsloth/__init__.py:67: UserWarning: Running `ldconfig /usr/lib64-nvidia` to link CUDA.\n", + " warnings.warn(\n", + "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: UserWarning: \n", + "The secret `HF_TOKEN` does not exist in your Colab secrets.\n", + "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n", + "You will be able to reuse this secret in all of your notebooks.\n", + "Please note that authentication is recommended but still optional to access public models or datasets.\n", " warnings.warn(\n" ] }, @@ -162,7 +167,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "1ddd1a7f731a42ea86f8f38907200c0d" + "model_id": "8d89897b8d6043a987a22557fbb0dafe" } }, "metadata": {} @@ -171,7 +176,7 @@ "output_type": "stream", "name": "stderr", "text": [ - "==((====))== Unsloth: Fast Llama patching release 2023.12\n", + "==((====))== Unsloth: Fast Llama patching release 2024.1\n", " \\\\ /| GPU: Tesla T4. Max memory: 14.748 GB\n", "O^O/ \\_/ \\ CUDA capability = 7.5. Xformers = 0.0.22.post7. FA = False.\n", "\\ / Pytorch version: 2.1.0+cu121. CUDA Toolkit = 12.1\n", @@ -189,7 +194,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "21a946400f2d4b3b9abc6074f9ac9dbd" + "model_id": "471e931d67c84e54aedf20c9a877d2f1" } }, "metadata": {} @@ -203,7 +208,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "fba46c7d2ad54a429ee903d865effc9f" + "model_id": "1d5578090f284a5ba79ad57afdd64dbe" } }, "metadata": {} @@ -217,7 +222,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "adca77c0f10941f68b210db4f70c223b" + "model_id": "930ff2003e8a47ceb49fca7868354173" } }, "metadata": {} @@ -231,7 +236,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "fde24c4c5fdb4f3eb9a9e9ded49e0514" + "model_id": "3b0e72a93a764f16965b10c0fdae262e" } }, "metadata": {} @@ -245,7 +250,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "ee33b6b5166d4ed5af4ac37fffe98960" + "model_id": "13c9bef53636491eab093352d4caa735" } }, "metadata": {} @@ -259,7 +264,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "1782ff6a8d3a409d9c1ff8c17aebea0e" + "model_id": "007c960fb11341189b9ca216c2387912" } }, "metadata": {} @@ -272,8 +277,17 @@ "dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+\n", "load_in_4bit = True # Use 4bit quantization to reduce memory usage. Can be False.\n", "\n", + "# 4bit pre quantized models we support for 4x faster downloading + no OOMs.\n", + "fourbit_models = [\n", + " \"unsloth/mistral-7b-bnb-4bit\",\n", + " \"unsloth/llama-2-7b-bnb-4bit\",\n", + " \"unsloth/llama-2-13b-bnb-4bit\",\n", + " \"unsloth/codellama-34b-bnb-4bit\",\n", + " \"unsloth/tinyllama-bnb-4bit\",\n", + "]\n", + "\n", "model, tokenizer = FastLanguageModel.from_pretrained(\n", - " model_name = \"unsloth/llama-2-7b-bnb-4bit\", # \"unsloth/llama-2-7b\" for 16bit loading\n", + " model_name = \"unsloth/llama-2-7b-bnb-4bit\", # Choose ANY! eg mistralai/Mistral-7B-Instruct-v0.2\n", " max_seq_length = max_seq_length,\n", " dtype = dtype,\n", " load_in_4bit = load_in_4bit,\n", @@ -298,14 +312,14 @@ "colab": { "base_uri": "https://localhost:8080/" }, - "outputId": "9ec45db2-2bde-4f35-b8ab-521ee19100e1" + "outputId": "aff41a1e-2a0c-4092-d0d8-9438e7057212" }, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ - "Unsloth 2023.12 patched 32 layers with 32 QKV layers, 32 O layers and 32 MLP layers.\n" + "Unsloth 2024.1 patched 32 layers with 32 QKV layers, 32 O layers and 32 MLP layers.\n" ] } ], @@ -316,11 +330,12 @@ " 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", + " 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", + " use_rslora = False, # We support rank stabilized LoRA\n", + " loftq_config = None, # And LoftQ\n", ")" ] }, @@ -331,7 +346,9 @@ "### 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)." + "**[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!" ], "metadata": { "id": "vITh0KVJ10qX" @@ -346,54 +363,53 @@ "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" + "74558dba954141e28f546384c5ce45df", + "c05bf4fa33e243318634e06a43c5e349", + "1f5d32a9671a4e09bf5d354ad78900cf", + "b02088d195c34da7972ab353c5f8c375", + "023a79c0d8fd40eb8830066dc08f92d4", + "8fdd1a662c56454da71ed5d321f34251", + "c24ff71905b94183b6b11caaf25c0679", + "f4a668de26214a1080746b8e9d718fe8", + "45b0279b01c64ce28d261a82023bf878", + "b970c106abfa413c99443000cfea30f3", + "2d40e05ff63546ee887f38da6ef4b3b4", + "174584afca224f86a9dd15e2edbcc428", + "6f37160eee3d420f8208547646bbd6d1", + "c81f8fadd92a427283c2aaad10b39683", + "099696f8bcfc4ed7bc5e0eb68e6526a0", + "2ffe07a48b4c4a33bdfa5be58d05fe86", + "387c937c33a64274a26eebc1a4e3dbe7", + "e851aebec1ef4a38b063d199066bcf9f", + "345b903d0f19437cbd6048fb13c66126", + "9032582c45a947dcbc19796da17d626f", + "f9fce8fd15d84d538494da57d4c9d84c", + "1055a35a671b422a82e6dd3bd3b62c1b", + "6ceae480b63c44848d7f7a210b2b88d5", + "895aaaba6b064f6ab85cc0b1a3e15ad5", + "16080e31e7fc447ebdcef8bcd3a68af5", + "b8fbceddaea24ee6a540b1a9b1706d54", + "b7e42686cedb4cd0b415790927988ed3", + "ed648c9a917447c79bf262638ad29a4d", + "406de0abbff2494194ff1f767bee84b4", + "e5449a6478584500a95cdbf571bc5410", + "bbff18dc8f3f4172a316065b43a7754e", + "46e14a149d2c4eeaaaca6cb507e23c87", + "ae9779aa721b4dc6b36b522c8f2183ae", + "617fd1a72995407aa171832a3f85da60", + "1f600fbaaed94f028970a52731e66a58", + "6c83ca6081fe4cf19d4dbb28eaa44148", + "bd337c697a49441ea0b0a3ed22a524fc", + "6187678a8c7646818bbb1d7ee6c8e57e", + "0855a4acfa4c44ad8b6de5cc22748f58", + "35685010bc8e452e9618be89bf6e121d", + "874aaa7bd499479d9b004f89c9ac566a", + "ce1a4213b6b149c6a8480e6b9883f1e4", + "78526ffb80284e1cb6b67bb29e8a58e8", + "2488b84919634be980df351d436f2c9b" ] }, - "cellView": "form", - "outputId": "58b732a7-4fdf-4131-f8ad-653f5e6845d5" + "outputId": "c6475cdc-f9d7-4923-8b54-bbc7f24f157b" }, "outputs": [ { @@ -405,7 +421,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "1ad8100179344b659456d1519992a20c" + "model_id": "74558dba954141e28f546384c5ce45df" } }, "metadata": {} @@ -419,7 +435,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "d90cb12c521e460d995f86a37dbd7cbd" + "model_id": "174584afca224f86a9dd15e2edbcc428" } }, "metadata": {} @@ -433,7 +449,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "b056f9720512470eb013d3f9670dcff1" + "model_id": "6ceae480b63c44848d7f7a210b2b88d5" } }, "metadata": {} @@ -447,14 +463,13 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "7314cd3f8b444f33bbe8e8f315585c55" + "model_id": "617fd1a72995407aa171832a3f85da60" } }, "metadata": {} } ], "source": [ - "#@title Alpaca dataset preparation code\n", "alpaca_prompt = \"\"\"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", @@ -466,13 +481,15 @@ "### Response:\n", "{}\"\"\"\n", "\n", + "EOS_TOKEN = tokenizer.eos_token\n", "def formatting_prompts_func(examples):\n", " instructions = examples[\"instruction\"]\n", " inputs = examples[\"input\"]\n", " outputs = examples[\"output\"]\n", " texts = []\n", " for instruction, input, output in zip(instructions, inputs, outputs):\n", - " text = alpaca_prompt.format(instruction, input, output)\n", + " # Must add EOS_TOKEN, otherwise your generation will go on forever!\n", + " text = alpaca_prompt.format(instruction, input, output) + EOS_TOKEN\n", " texts.append(text)\n", " return { \"text\" : texts, }\n", "pass\n", @@ -500,34 +517,134 @@ "id": "95_Nn-89DhsL", "colab": { "base_uri": "https://localhost:8080/", - "height": 49, + "height": 177, "referenced_widgets": [ - "62bf1a9297c44ab1984201d97200b60a", - "228660bc715a43da8eac4c7585e16bac", - "861f5cab34fa47718b8da2e5c5426ce5", - "b17bc865041b43999440ab80271f0610", - "6b5cab72791b481d80c73ea95969962e", - "692a42425ecf4da9a562803c22d522e0", - "87fcc05152694fed9f5cac6c35a7d8bd", - "25087a506e6b4ee5827c87804f4dfdc2", - "28eae49a2ca2422cb0666a8dd2ddf57b", - "c40aa48eed1741b6997f6bf23fee7057", - "c7b9b43135e342d2b659839c31db90d1" + "b45961f634ab4c77b0299b90a844ceb4", + "e198db9222944139ae54378c8ec37faa", + "c836372d9d8f4e47ae3f0ac30c3c3819", + "897344eb4c334ff3a6daaaad35785547", + "a7cefb96d91d4da2ba22c505884ccb27", + "509bb157e8164ba3b12e191a979dcbe7", + "6dbdcba8515d4fb6be4656727c26848c", + "a81e5681e8e64940ae70bd0499ebc542", + "655a84ad15274d5e952eb77c778417bc", + "9544de014f9a410ea9a85fe030463921", + "0b611036fa664a309ab78800967f80b3", + "b5f83d82ca924895b49c67425ede0f1f", + "580bec4f24d7440f9366a744d739c6f3", + "973c72b8232e49a1bf0ae438a35268cd", + "b431d2460e724d69b57a4cd2a6f4a0b9", + "831a8178a0f043b09be9b9bbcc077d0a", + "725b302fc78940e880ae281b1808126e", + "1946e9c53e6645b0ae8a9bcc5c53d7ae", + "334e83adc688445a9593abe1787fcd53", + "a9fb267b56954fd696d273119602461f", + "8176392dd9a04671b3c48df9955e39b4", + "0c05f5826824460da5702f54bf4512ea", + "28d40a2c73fe4e5ab132019f9e877d04", + "136e1e8dbbc84ed78c9c33fb33b07c6a", + "82c58e0f1fd547d082edaf0e1247e016", + "0204195337d6419b8bc11f87648922a2", + "519dcba35ab34667b9c1d7e956292bad", + "627d0b0edec94287a24982d4691be832", + "940e704814514a1d83a844e69998ae53", + "552cf4fecfc548e3b0cbe7b077fe9419", + "9f5f46652e784b6799a973a3800fdc99", + "4ce998bc595143afb1e6660b32aad390", + "911f9c43749242a3977b3665926eb86a", + "e7420547ef9d4cf3bff2c5f3bbe57c1b", + "25c500c92a5243948e2964be54c7d489", + "fbf6d1dc0a24412fb067b1dac2fda172", + "66b0660356cd491ba80289fbb7d77002", + "9de8c460109347fbb4fbe9c9defbd3fa", + "c0704e7bc6d34a7cad29a30694a4d2e1", + "fe3f918fcfb34bc6a63e590acd0d5814", + "639ed7ec582b4083ad58379ba035a3f2", + "0210a35193a84e0ebab03f6b0805525d", + "9125296314f1428195b31c8bef2099a0", + "a6ddb773091c446783f7882f8245e55b", + "f9c467b27a574fe8a949c87859e56da4", + "b5c6123394b44d6daaa26974a63618b7", + "654a37cc0ed24270b88d9046763ab797", + "983324d114f9466bb24d428a935bf419", + "d0fe47bce69e4f80ac1c548827d5c3be", + "8b3b6069d2e24d00a160ef04d0423fcd", + "3fc53be851324935830e16efbf13a9cd", + "3f56efc5bebd41b28b977b35f1257135", + "07113f67b2434918948be81683628df2", + "d4d2b43807d24b609dc27bfd9b02ed2d", + "beeb810d96ff4b7a8ae2285bc39c9869" ] }, - "outputId": "0f8efda2-8dbd-4b6b-e56e-0132183591c5" + "outputId": "aa0306a5-78f3-4360-b144-3e2478a5ad99" }, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ - "Map: 0%| | 0/51760 [00:00\n", " \n", " \n", - " [60/60 06:57, Epoch 0/1]\n", + " [60/60 07:44, Epoch 0/1]\n", " \n", " \n", " \n", @@ -633,243 +751,243 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - 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" @@ -891,19 +1009,19 @@ "colab": { "base_uri": "https://localhost:8080/" }, - "outputId": "8a7a7eee-c329-4337-bcc7-ee5946a4b985" + "outputId": "3cecbbbd-a23b-45f6-948f-424edb37bdd4" }, "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" + "476.7837 seconds used for training.\n", + "7.95 minutes used for training.\n", + "Peak reserved memory = 5.77 GB.\n", + "Peak reserved memory for training = 1.649 GB.\n", + "Peak reserved memory % of max memory = 39.124 %.\n", + "Peak reserved memory for training % of max memory = 11.181 %.\n" ] } ], @@ -935,6 +1053,8 @@ { "cell_type": "code", "source": [ + "# alpaca_prompt = Copied from above\n", + "\n", "inputs = tokenizer(\n", "[\n", " alpaca_prompt.format(\n", @@ -944,7 +1064,7 @@ " )\n", "]*1, return_tensors = \"pt\").to(\"cuda\")\n", "\n", - "outputs = model.generate(**inputs, max_new_tokens = 128, use_cache = True)\n", + "outputs = model.generate(**inputs, max_new_tokens = 64, use_cache = True)\n", "tokenizer.batch_decode(outputs)" ], "metadata": { @@ -952,7 +1072,7 @@ "colab": { "base_uri": "https://localhost:8080/" }, - "outputId": "ad759299-9d63-465c-a4ee-28d60feef175" + "outputId": "684a01f7-3545-4078-a361-64800718ba37" }, "execution_count": null, "outputs": [ @@ -960,7 +1080,7 @@ "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']" + "[' 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, 2']" ] }, "metadata": {}, @@ -971,55 +1091,53 @@ { "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." + " You can also use a `TextStreamer` for continuous inference - so you can see the generation token by token, instead of waiting the whole time!" ], "metadata": { - "id": "1bSoOaDunRPL" + "id": "_zsWEKC7t6Tt" } }, { "cell_type": "code", "source": [ - "model = model.merge_and_unload()" + "# alpaca_prompt = Copied from above\n", + "\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", + "]*1, 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": { - "id": "xcRjsZe0RK1b", "colab": { "base_uri": "https://localhost:8080/" }, - "outputId": "cd1025e7-9d24-4262-ff02-e4332f532ad3" + "id": "-C5dS6ZZt6ra", + "outputId": "d5fb7b7a-e2ba-4174-be00-62c86026c116" }, "execution_count": null, "outputs": [ { "output_type": "stream", - "name": "stderr", + "name": "stdout", "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" + " 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", + "1, 2, 3, 5, 8, 13, 21, 34, 55, 89, 144, 233, 377, 610, 987, 1597, 2584, 4181, 6765, 10946, 17711, 28657, 46368, 75025, 121429, 1\n" ] } ] @@ -1027,20 +1145,24 @@ { "cell_type": "markdown", "source": [ - "Now if you want to load the LoRA adapters we just saved, we can!" + "\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!" ], "metadata": { - "id": "AEEcJ4qfC7Lp" + "id": "uMuVrWbjAzhc" } }, { "cell_type": "code", "source": [ - "from peft import PeftModel\n", - "model = PeftModel.from_pretrained(model, \"lora_model\")" + "model.save_pretrained(\"lora_model\") # Local saving\n", + "# model.push_to_hub(\"your_name/lora_model\", token = \"...\") # Online saving" ], "metadata": { - "id": "MKX_XKs_BNZR" + "id": "upcOlWe7A1vc" }, "execution_count": null, "outputs": [] @@ -1048,33 +1170,45 @@ { "cell_type": "markdown", "source": [ - "Finally, we can now do some inference on the loaded model." + "Now if you want to load the LoRA adapters we just saved for inference, set `False` to `True`:" ], "metadata": { - "id": "f8pvYYN9DvbN" + "id": "L7z5EkMXuC8c" } }, { "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", + "\n", + "# alpaca_prompt = You MUST copy from above!\n", + "\n", "inputs = tokenizer(\n", "[\n", " alpaca_prompt.format(\n", - " \"What is the famous tower in France called?\", # instruction\n", + " \"What is a famous tall tower in Paris?\", # instruction\n", " \"\", # input\n", - " \"\", # output\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)" + "from transformers import TextStreamer\n", + "text_streamer = TextStreamer(tokenizer)\n", + "_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 64)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, - "id": "qWyyUoDq71jt", - "outputId": "89236a12-69ff-42e3-8178-b7d039d19655" + "id": "_tbHr99fuAuL", + "outputId": "5c8468f6-7c2c-4733-d477-305d0df35c57" }, "execution_count": null, "outputs": [ @@ -1082,7 +1216,7 @@ "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']" + "[\" 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 is a famous tall tower in Paris. It is located on the Champ de Mars in the 7th arrondissement of Paris. It was built in 1889 as the main entrance to the 1889 World's Fair.\"]" ] }, "metadata": {}, @@ -1090,14 +1224,122 @@ } ] }, + { + "cell_type": "markdown", + "source": [ + "You can also use Hugging Face's `AutoModelForPeftCausalLM`" + ], + "metadata": { + "id": "TGKU509CuMmq" + } + }, + { + "cell_type": "code", + "source": [ + "if False:\n", + " from peft import AutoModelForPeftCausalLM\n", + " from transformers import AutoTokenizer\n", + " model = AutoModelForPeftCausalLM.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\")" + ], + "metadata": { + "id": "I1blTAruuM7X" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "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!" + ], + "metadata": { + "id": "-xp0YDnKuN98" + } + }, + { + "cell_type": "code", + "source": [ + "# Merge to 16bit\n", + "if False: model.save_pretrained_merged(\"x\", tokenizer, save_method = \"merged_16bit\",)\n", + "if False: model.push_to_hub_merged(\"hf_user/x\", tokenizer, save_method = \"merged_16bit\", token = \"\")\n", + "\n", + "# Merge to 4bit\n", + "if False: model.save_pretrained_merged(\"x\", tokenizer, save_method = \"merged_4bit\",)\n", + "if False: model.push_to_hub_merged(\"hf_user/x\", tokenizer, save_method = \"merged_4bit\", token = \"\")\n", + "\n", + "# Just LoRA adapters\n", + "if False: model.save_pretrained_merged(\"x\", tokenizer, save_method = \"lora\",)\n", + "if False: model.push_to_hub_merged(\"hf_user/x\", tokenizer, save_method = \"lora\", token = \"\")" + ], + "metadata": { + "id": "vnFt-4ymuPM1" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "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." + ], + "metadata": { + "id": "8xg8B-N7uQcE" + } + }, + { + "cell_type": "code", + "source": [ + "# Save to 8bit Q8_0\n", + "if False: model.save_pretrained_gguf(\"x\", tokenizer,)\n", + "if False: model.push_to_hub_gguf(\"hf_user/x\", tokenizer, token = \"\")\n", + "\n", + "# Save to 16bit GGUF\n", + "if False: model.save_pretrained_gguf(\"x\", tokenizer, quantization_method = \"f16\")\n", + "if False: model.push_to_hub_gguf(\"hf_user/x\", tokenizer, quantization_method = \"f16\", token = \"\")\n", + "\n", + "# Save to q4_k_m GGUF\n", + "if False: model.save_pretrained_gguf(\"x\", tokenizer, quantization_method = \"q4_k_m\")\n", + "if False: model.push_to_hub_gguf(\"hf_user/x\", tokenizer, quantization_method = \"q4_k_m\", token = \"\")" + ], + "metadata": { + "id": "8T822D9fuR0g" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "Now, use the `x.gguf` file or `x-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)." + ], + "metadata": { + "id": "RiRcv_rquUq0" + } + }, { "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. Mistral 7b 2x faster [free Colab](https://colab.research.google.com/drive/1Dyauq4kTZoLewQ1cApceUQVNcnnNTzg_?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", - " \n", + " \n", + " \n", + " Support our work if you can! Thanks!\n", "
" ], "metadata": { @@ -1120,7 +1362,7 @@ }, "widgets": { "application/vnd.jupyter.widget-state+json": { - "1ddd1a7f731a42ea86f8f38907200c0d": { + "8d89897b8d6043a987a22557fbb0dafe": { "model_module": "@jupyter-widgets/controls", "model_name": "HBoxModel", "model_module_version": "1.5.0", @@ -1135,14 +1377,14 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_b06f3ae2d51748649473e5fd50c7d965", - "IPY_MODEL_df8d35e0ac574c9e86feb01568070798", - "IPY_MODEL_acd1e9b8d7804b00903d131cf0375df4" + "IPY_MODEL_05b3626ffd6440928672d6404ec9557d", + "IPY_MODEL_c9071ce1dd5c42f8b6f80809b0a7b74c", + "IPY_MODEL_0a8c217f44334a02a4abb3a948873077" ], - "layout": "IPY_MODEL_ffa07e2231044b77acd56ebc2d41de21" + "layout": "IPY_MODEL_341a9367f92c489db8a15ad1266719bd" } }, - "b06f3ae2d51748649473e5fd50c7d965": { + "05b3626ffd6440928672d6404ec9557d": { "model_module": "@jupyter-widgets/controls", "model_name": "HTMLModel", "model_module_version": "1.5.0", @@ -1157,13 +1399,13 @@ "_view_name": "HTMLView", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_6a93358b89fd48abae84e563f1c32a36", + "layout": "IPY_MODEL_9124f5ad5e1c4c67bbfc03b40e3e93a3", "placeholder": "​", - "style": "IPY_MODEL_9c1c57caebfb4974abf75895be7e8a3d", + "style": "IPY_MODEL_36b804f7ebf34ed0afb58c5ff3c2e932", "value": "config.json: 100%" } }, - "df8d35e0ac574c9e86feb01568070798": { + "c9071ce1dd5c42f8b6f80809b0a7b74c": { "model_module": "@jupyter-widgets/controls", "model_name": "FloatProgressModel", "model_module_version": "1.5.0", @@ -1179,15 +1421,15 @@ "bar_style": "success", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_ef7532bbe1cd49dd96e432c597ec6dfa", + "layout": "IPY_MODEL_79bf0b38acd34d8aae552bc0314f81cd", "max": 1096, "min": 0, "orientation": "horizontal", - "style": "IPY_MODEL_17784e5eadeb416fbb000dcfc66f4699", + "style": "IPY_MODEL_4295dda456ed44f5959c79e31f837d04", "value": 1096 } }, - "acd1e9b8d7804b00903d131cf0375df4": { + "0a8c217f44334a02a4abb3a948873077": { "model_module": "@jupyter-widgets/controls", "model_name": "HTMLModel", "model_module_version": "1.5.0", @@ -1202,13 +1444,13 @@ "_view_name": "HTMLView", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_1f97459b01b340038dd02120127472c3", + "layout": "IPY_MODEL_09b8323944d64e07bb6e7d5f743868fb", "placeholder": "​", - "style": "IPY_MODEL_6cd5a1dd2da3469bbf2d16d12147fa50", - "value": " 1.10k/1.10k [00:00<00:00, 18.3kB/s]" + "style": "IPY_MODEL_c92c8b9ee4964620bea877268d7be45c", + "value": " 1.10k/1.10k [00:00<00:00, 35.7kB/s]" } }, - "ffa07e2231044b77acd56ebc2d41de21": { + "341a9367f92c489db8a15ad1266719bd": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", @@ -1260,7 +1502,7 @@ "width": null } }, - "6a93358b89fd48abae84e563f1c32a36": { + "9124f5ad5e1c4c67bbfc03b40e3e93a3": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", @@ -1312,7 +1554,7 @@ "width": null } }, - "9c1c57caebfb4974abf75895be7e8a3d": { + "36b804f7ebf34ed0afb58c5ff3c2e932": { "model_module": "@jupyter-widgets/controls", "model_name": "DescriptionStyleModel", "model_module_version": "1.5.0", @@ -1327,7 +1569,7 @@ "description_width": "" } }, - "ef7532bbe1cd49dd96e432c597ec6dfa": { + "79bf0b38acd34d8aae552bc0314f81cd": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", @@ -1379,7 +1621,7 @@ "width": null } }, - "17784e5eadeb416fbb000dcfc66f4699": { + "4295dda456ed44f5959c79e31f837d04": { "model_module": "@jupyter-widgets/controls", "model_name": "ProgressStyleModel", "model_module_version": "1.5.0", @@ -1395,7 +1637,7 @@ "description_width": "" } }, - "1f97459b01b340038dd02120127472c3": { + "09b8323944d64e07bb6e7d5f743868fb": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", @@ -1447,7 +1689,7 @@ "width": null } }, - 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