diff --git "a/Alpaca_+_Mistral_7b_full_example.ipynb" "b/Alpaca_+_Mistral_7b_full_example.ipynb" new file mode 100644--- /dev/null +++ "b/Alpaca_+_Mistral_7b_full_example.ipynb" @@ -0,0 +1,5240 @@ +{ + "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, # 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": [ + "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", + "
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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": [ + "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": "TCv4vXHd61i7" + } + }, + { + "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": [ + "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": [ + "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", + "

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