{ "cells": [ { "cell_type": "code", "execution_count": 3, "metadata": { "id": "2eSvM9zX_2d3" }, "outputs": [], "source": [ "%%capture\n", "# Installs Unsloth, Xformers (Flash Attention) and all other packages!\n", "!pip install \"unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git\"\n", "\n", "# We have to check which Torch version for Xformers (2.3 -> 0.0.27)\n", "from torch import __version__; from packaging.version import Version as V\n", "xformers = \"xformers==0.0.27\" if V(__version__) < V(\"2.4.0\") else \"xformers\"\n", "!pip install --no-deps {xformers} trl peft accelerate bitsandbytes triton" ] }, { "cell_type": "markdown", "metadata": { "id": "r2v_X2fA0Df5" }, "source": [ "* We support Llama, Mistral, Phi-3, Gemma, Yi, DeepSeek, Qwen, TinyLlama, Vicuna, Open Hermes etc\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", "* 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.\n", "* [**NEW**] We make Phi-3 Medium / Mini **2x faster**! See our [Phi-3 Medium notebook](https://colab.research.google.com/drive/1hhdhBa1j_hsymiW9m-WzxQtgqTH_NHqi?usp=sharing)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 531, "referenced_widgets": [ "d02e7fd6db8443aaba33f5c92409c008", "85f2c68a14074e778ce4314cc8af98f0", "ac4920c4534c421c971e515caf349b12", "eda939372e5c4c168386cef64bf40636", "c6e0f67f04b7449784b481309594e789", "e08fb1944c8a4b2f8f9017e875036bee", "7e4f1bd255f9468385fa23a694b93ece", "7236c426bfa54443a03452eb18101409", "52caacae4afd40ca9e8aa931746ec0c4", "3ed97edcb46740f283861db76ff9d8fd", "c83329fa445a4917b2d0748cd6dc84df", "0456b3d2bd084d0aacc7f9b13f87fab1", "abe11efdea8f491cb5808c4413e35724", "ffca90f4afdf464c8b93a78ca7be390e", "a0b0d7c078b74a02b4eae4be91593549", "27c376fd959a47b2ae5dee0af4257a38", "8c5ebc058cc946c2ab962ef69929bf54", "b2115fa9f27346c2a271f4614669e55d", "95bcb12cac7948a696abbde96e00a2ee", "e6ba37135ff8460a9d6f69f76dad7660", "fea7bf39b5f745f8a7511c70d6118670", "ae6efd8a31674b50a1383b59a161ef45", "154dd96089c346ec89f8b2af2fdf1013", "734d9d74eab44584964e2a3841dd9e40", "4c08f80700b8410688a9f4c38de72d20", "91bd8b1c56ea4451b6799b37a35fbd0a", "3c4a5863ddc34c489141908d79a4af57", "6b3741cf38ed4b79bc14f486bbd2cf65", "d27958462e5f4f07b6171d5768c02c7a", "45e96652eaf0497f93aa8fc17eb98434", "dd1802d1951543939297cdc29b16f3a0", "032f7643aca54b35bd3ac40a868eeb64", "a707331403664a4fa0eb096471a948a2", "7957d5afcd784ab39660867128dc5b33", "05eedcea87e54cebaeca0847421b3f39", "058a01509b6f4ae2bc3eaf155fe9a11d", "92baba9fc9e04e98bfc68b8cbc70ce17", "ddaaa03d303e42aead782ec10af49749", "053faf50ee4d4fbc83aeb6b3b456c6f7", "c660a0ad590e4a4aa187583b0bc0ac05", "f038959f25094f4a8415820b45ea61a6", "59572873b3c8421293623ca2a312a695", "e15f13b5b1784e49a89a64ea366b08f7", "e83297bad69845f989764b2e724ff037", "dd8a59a431934aa4a8542bef3e1b03cc", "7012f6da38444a88ab13fac24484f42d", "0e0158304e91455b841005e67215d1ef", "9d1483d3eb3842acbf8517526c86da54", "da14f6d9fa16493f8b565e0fcbbe9eb6", "c722650b4c6448d1a30641ee95d60c5d", "cd8947fd79f848c598dbe99357825f43", "84e783239c4448c5a4c52c410a037d7b", "6748263596d843f486e218137f0841f9", "6118dbdf5bdd4f14a68d521394a55c32", "8e305d156470464c9c6f66cd2624c008", "b8c8c167b1494ee1b9f612083556ee29", "ae86858a6f6649a189f45ac74ebd0f12", "7c2f83e31db3429e9542828feeaf0eb8", "7b547f9704954d1a8b0e621558322bbc", "e8d9c403f1f54e8b890cb43fa42627e5", "22c88a4dd2eb4be5aadf7e1cb8341416", "2449d931a2d84941883cae30ffef1c5e", "ff83829a055948cd8a46d1c30a100706", "7680773dcf0b4f6180240cbbbef7cfa2", "736fab037b4345288c317341652ebfcc", "0d0a06aeda4e451f8c5acd8ddd730610", "dfbaa3850dfa473cad45a21eedda2647", "cfa6206fa65f43368a67c8db4c7681be", "ad5593aa53154444a76fa255f3a8aded", "7630a16dafc145edbbd6967b97bf4b40", "a023f7d779444dfda14a9514440ce322", "ff4612ba3e084068b9baaf758e89e5fa", "dfe1a3d8d62146f1bf0bcc145023fd16", "abc52da02f574307911664b50fecceb9", "dd21902778cd4547b4ba2c01dfd9834d", "b194d9ca3d324638a7bf94ce89311851", "30819a0c189e4d9faf83bf8861bec8ab", "28895eedd87445edae9aeb951660ee60", "3dd4549228a440c0870d4772ce393a8a", "96759b0c24f04d6c9eb13bd65a9bd58a", "6101b1c53f3f4c7f82dcbe3492292702", "4533659224b04407a1cc46e1938837c5", "99b66efd718844d7a9c6ff08948d9ed6", "12f703cf17e54eb1a133cb46cf03e610", "4540ef70431a4db2ab9d43994d78f0ca", "5edbb685f0684a85b2b2fb7942604727", "e6236c639fe04273856cb3aa9f322a47", "64787fddab704048ae847b9e8ac61a78", "81dbc48041d04dff92da91970e4cba62", "b2a12aeec3814c1282fb501ff0615df2", "385d421be4ea4dd0a10b74a9fd2b5c71", "34959cfa4c5a4c239e225842b397245a", "429bde12d4084de6a2a1304c9f50dd48", "175df900a7734abcb20d66bb5d67ec70", "97cc4784a1cb4de2b1491f194976b128", "b658bdc0f70b4304873e4d65d1bc34c6", "254d93a15bcf4d7189d86211754ad227", "3b488153e813410fb7b73b5ae867b32c", "375c2d7650ed47fca2397c58471966d4" ] }, "id": "QmUBVEnvCDJv", "outputId": "16ff43ac-ca80-4270-d56b-87bdd052c184" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "==((====))== Unsloth 2024.10.2: Fast Gemma patching. Transformers = 4.44.2.\n", " \\\\ /| GPU: Tesla T4. Max memory: 14.748 GB. Platform = Linux.\n", "O^O/ \\_/ \\ Pytorch: 2.4.1+cu121. CUDA = 7.5. CUDA Toolkit = 12.1.\n", "\\ / Bfloat16 = FALSE. FA [Xformers = 0.0.28.post1. FA2 = False]\n", " \"-____-\" Free Apache license: http://github.com/unslothai/unsloth\n", "Unsloth: Fast downloading is enabled - ignore downloading bars which are red colored!\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "d02e7fd6db8443aaba33f5c92409c008", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Downloading shards: 0%| | 0/2 [00:00 0 ! Suggested 8, 16, 32, 64, 128\n", " target_modules = [\"q_proj\", \"k_proj\", \"v_proj\", \"o_proj\",\n", " \"gate_proj\", \"up_proj\", \"down_proj\",],\n", " lora_alpha = 16,\n", " lora_dropout = 0, # Supports any, but = 0 is optimized\n", " bias = \"none\", # Supports any, but = \"none\" is optimized\n", " # [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 `llama-3` template for ShareGPT datasets, try our conversational [notebook](https://colab.research.google.com/drive/1XamvWYinY6FOSX9GLvnqSjjsNflxdhNc?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": 177, "referenced_widgets": [ "f56eabceed6d43c09991a5d45ae91ca7", "278528a1f9be4f4489d5e9f079c140fc", "eefe0d2480b241d790f5af2dd0608c56", "cf0c9a30ecf2479e9eabcd6897cd2ae4", "bdc21cb998cc46c0b5bbb164e97caa3c", "4c6e92a233d846e891361505fcb662f6", "250e9ac8e09647048c4d3d547c7d308b", "7f58e340056d4093b16b78764cb9b250", "9644fcbdddd74d03a4ea5dd4d08ab503", "2607a6cb68324b2e914b2215ff21718c", "c51aeb5cc9134b2eb14be937f62f5324", "087b8ea60c154db78830e2f0951f1149", "864144823c3f412e9a9e633433335de4", "2a88ee04ec8a45b2b7501d818a24e590", "1fee9ee653b1410ba830f529c91a81cd", "dedd0dad381e4eada83049c050d928ef", "a001e114cb814ad1a365b8573db7e5d1", "25223801e9fe4259bfc2fb6806126925", "2bb29b0a85d24902921d9c775a043610", "5dbffddd42d74c2395a8780eb97d8621", "cddb97a4075d4f7c800a4adf2b3d51ad", "de3846b29fb74247bb54c162e9d269e1", "cd16fe5996ad436eaa71f061eb636080", "346ffd6c0a8d4294ae3b34039cd24d35", "0faa89bfff194caeb94c8aafbe01ef8d", "5ebb319b66fa4654938f632e4c835873", "298ecc6277b940d09a54dd3e40b9eea2", "d504a1c60ce34be6b69ca3f71350b441", "74c2344516804d3aa1fc5790ae0be7c8", "fb3312065b134aea8828e5ea365a034b", "5949f23ddee64ef58372bde70afe326e", "a1f7c20dcbfe4985901ccaa9e72d0e8f", "48ca9d738b594844a0629a32c6d46f00", "390ebf5b320044599a1f9745ac097920", "fbf09dd6e12b44f2af819103adf27bde", "f41f16ce15844fe2aaeab58ec2247169", "ad032e4f771d4e0badf64b954b890954", "82635117e1584dac84f12bb3f32aefe8", "f7ca3324dd5f4aa496c08c5cb68dec7d", "99300f8f51af4986b96d661ed4e3ab96", "b7dd1814ee244ab2aa713e69a8949757", "57d7e970f82b459caadee910456f0a3d", "eec90c452b094e4aac9a361d27ee6f1c", "6a9632cbe57642f094d213b338a2b9fb", "d120909e42be4e09a5a9a0c65dd1b8c9", "cb217356b0484cb9b05d10527101ba23", "c8ce6a5e365049a2b7a5f4233f96dc75", "01df545e3c734457b40343fd1ff92235", "83994791f7f648a299ea04d5a1892287", "acea1929d3684ed2bf879325196984ba", "31f4e7ccbb7b416c8d2d9c8f961ca21e", "0ea7da5f90894fb6bc81214c3d47e397", "76da0a2ba14a49e1b6042d3684dc2762", "c8581dca214c412caf7950ab81b66f8a", "14dd20bafc03480a9c6bb8553419a10c" ] }, "id": "LjY75GoYUCB8", "outputId": "af248563-7622-40cc-9b0f-f40a10f822e2" }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "f56eabceed6d43c09991a5d45ae91ca7", "version_major": 2, "version_minor": 0 }, "text/plain": [ "README.md: 0%| | 0.00/450 [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": 66, "referenced_widgets": [ "38b006b55c644ebdb6b010f1483d2dd5", "2f66d9ba81ff44e8b8a75186b06f1dab", "cfd7d85c85f44fbe87a72ff8174e5a47", "f4bc584c945546528904de08cee38f9c", "8e13342de0cb4657894ce5ea95c899dd", "627a338e41144f36a8a830a57281ee0b", "03b3e9dc42b145e485f628ff914ed9e5", "09758d6007f748b8897a3dfc1325efaf", "09a65291c7684e0eb41299243c6d1854", "eb83291e2c104a498dfc1e307edea512", "fda7d087bc0c4fa8a7d69ac87ad857b7" ] }, "id": "95_Nn-89DhsL", "outputId": "18daabf1-0894-4321-c34e-0907c3f7cfb3" }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "38b006b55c644ebdb6b010f1483d2dd5", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Map (num_proc=2): 0%| | 0/172026 [00:00\n", " \n", " \n", " [160/160 16:23, Epoch 0/1]\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " 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StepTraining Loss
11.675500
21.778500
32.056900
41.955700
51.856800
61.785000
71.896900
81.718600
91.768200
101.407700
111.649500
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" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/lib/python3.10/dist-packages/peft/utils/other.py:689: UserWarning: Unable to fetch remote file due to the following error 401 Client Error. (Request ID: Root=1-67128366-2df0abb2728dc7df7ee674a1;c0b18eb2-3ca3-4b91-b7b7-6805f471d326)\n", "\n", "Cannot access gated repo for url https://huggingface.co/google/gemma-2b/resolve/main/config.json.\n", "Access to model google/gemma-2b is restricted. You must have access to it and be authenticated to access it. Please log in. - silently ignoring the lookup for the file config.json in google/gemma-2b.\n", " warnings.warn(\n", "/usr/local/lib/python3.10/dist-packages/peft/utils/save_and_load.py:243: UserWarning: Could not find a config file in google/gemma-2b - will assume that the vocabulary was not modified.\n", " warnings.warn(\n" ] } ], "source": [ "trainer_stats = trainer.train()" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "cellView": "form", "colab": { "base_uri": "https://localhost:8080/" }, "id": "pCqnaKmlO1U9", "outputId": "933c492d-483a-41b6-a8d4-cbc4addf878f" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "998.3348 seconds used for training.\n", "16.64 minutes used for training.\n", "Peak reserved memory = 8.383 GB.\n", "Peak reserved memory for training = 6.043 GB.\n", "Peak reserved memory % of max memory = 56.842 %.\n", "Peak reserved memory for training % of max memory = 40.975 %.\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/" }, "id": "kR3gIAX-SM2q", "outputId": "6a9bae55-795e-46d1-cf56-e1958d664bea" }, "outputs": [ { "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:\\nআমি জানি না তুমি কি চাও, তবে নিচে থেকে একটি বিজোড় নম্বর বেছে নেও\\n\\n### Input:\\n১, ২, ৩, ৪, ৫\\n\\n### Response:\\nএই তালিকাটিতে 1, 2, 3, 4, 5 নম্বর রয়েছে।']" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# alpaca_prompt = Copied from above\n", "FastLanguageModel.for_inference(model) # Unsloth has 2x faster inference!\n", "inputs = tokenizer(\n", "[\n", " alpaca_prompt.format(\n", " \"আমি জানি না তুমি কি চাও, তবে নিচে থেকে একটি বিজোড় নম্বর বেছে নেও\", # instruction\n", " \"১, ২, ৩, ৪, ৫\", # 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/" }, "id": "e2pEuRb1r2Vg", "outputId": "e5b8b23b-5f97-47c4-8543-96929ad1e509" }, "outputs": [ { "name": "stdout", "output_type": "stream", "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", "আমি জানি না তুমি কি চাও, তবে নিচে থেকে একটি বিজোড় নম্বর বেছে নেও\n", "\n", "### Input:\n", "১, ২, ৩, ৪, ৫\n", "\n", "### Response:\n", "এই তালিকাটিতে 1, 2, 3, 4, 5 নম্বর রয়েছে।\n" ] } ], "source": [ "# alpaca_prompt = Copied from above\n", "FastLanguageModel.for_inference(model) # Unsloth has 2x faster inference!\n", "inputs = tokenizer(\n", "[\n", " alpaca_prompt.format(\n", " \"আমি জানি না তুমি কি চাও, তবে নিচে থেকে একটি বিজোড় নম্বর বেছে নেও\", # instruction\n", " \"১, ২, ৩, ৪, ৫\", # 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": "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": 14, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 420, "referenced_widgets": [ "755f646774be4f55878156816973d4c5", "bb25648ab2ca4ddf935b44850156a786", "cb93be1ce278426380a050b89e4e18c1", "cdf3fab9528947aaa4b1da16a3372d8e", "2c377d2c2cab427899e21cb46c4c8e69", "418b80fbe17f476baedb9d1bcc33eda8", "31e5b4175b5e4dc5a5cb14c1e374fce4", "4861bbdaf3fa4c53b9a5c01bbdcc9fbf", "8f82beb1b4eb43578388ab2d9e5b1818", "b6aeac4a5842405692a20e9ae05293dc", "d2fef79f7b07496c9f993894e3c27c8e", "8243e4db4a704ab48573c9b596c62887", "ad121fdac2ea4135b7f432a8a930bbe1", "aa0de24700c44a03975a90beefdb7fed", "5ed06407f384456887707ca2a50b43b4", "dc2573355d234bb5bdf2121e6e0372f5", "2238a294be114cf0885f58de8404f860", "87f56e4f906b4079bf320bb702cfa337", "a8259bc58b4d4acc98e53d28be433bf5", "f89a28ea02f34c89b837335a2b2d3b94", "3199e967dbb847148a76a4555cafba17", "4ad9408003554ea2a5eb6195304df0e3", "96a1612d94de4213ba725d1939a046d9", "7f97ab61aea54772a799174d62ca21a1", "57e08ec6d3bf424da0ebdb1d4a473ee9", "7140b9bb51fe459e928425d556196f10", "a8aac0aac54f48718d5204260c838adf", "d7082d420a004559b4e935caf07617dd", "82ef193e54b44278b485527e648b65a0", "75bb7557b8f240688800f1be16dfcc4f", "1686642a5c6f4787b62a1f329306eb82", "d0450d2f2ea94342bff364cb4cacbed3", "38f0155a58c34e208fa95994767d904d", "f8cd6d1989df470c85b3bbe76876870d", "0cf1e7d0275644959b65044850046dbe", "16d9259df86c41d18085dd61afebfd3b", "e3d506835f384260bbc9e87a640b377a", "5ddc77ee7dd24666b9183b70ade31f49", "001ec4624279477087750d0166e0a15a", "3be720e0c21944418d8da40af1a49942", "e145d331f68548259e51396554127241", "c350a4612ff343a2a468408db554e316", "333f3bfbf448451b9550af1ef9c963a3", "3740e58e6bee49a1a424f3cbc480700e", "9f03dd1f11264077afbcece71b84d686", "5937ec7d86bf423f9e8581a8a3732011", "a0080debd0674313b56824aa238db925", "b49e37eecb144a2bb71d6e77df9efae9", "220d7c238ec54312bcbd00b2946a502b", "e87fda940b70443695a07a5d7597ad96", "806dc42cd10545bf842189c8a3090b6c", "d845e6062b6a408991ce099ed30739ca", "e82e781fdfd44d82b5d5228c3e0c2a1f", "9d7e7be742804fbb8c3fd6b16d368741", "cd1c3e1f3a5e41c3bee26d593a784832", "f9d9e57f0c414d88be5b43c0c17fd59b", "4283d9114aef4d5f8f86da0c4cdffbc8", "352409d8e16b44e297408b7965d87502", "f08df714c10b4f8db857589078f8690b", "9a4521d490ad4b47a16aeab1c46fece6", "22860b820cb54fc492e3048fb434ef01", "da7e458516ec48a8bc2957b671e5646a", "bc1e2a397e7d4b349d5c7d9e94a1c0f4", "9107aa021360413baf10b4ac588b909f", "80c25f1229044ae783b3b0106a8e1f3d", "c6992a4ecaae43a6b8085b56fc6c3531" ] }, "id": "upcOlWe7A1vc", "outputId": "f26c5909-578f-4a11-88a5-31c5b8006727" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/lib/python3.10/dist-packages/peft/utils/other.py:689: UserWarning: Unable to fetch remote file due to the following error 401 Client Error. (Request ID: Root=1-671283b6-74d563702568500c47aafacb;d2edfe97-a041-4fca-a14f-d7ae29c2a89a)\n", "\n", "Cannot access gated repo for url https://huggingface.co/google/gemma-2b/resolve/main/config.json.\n", "Access to model google/gemma-2b is restricted. You must have access to it and be authenticated to access it. Please log in. - silently ignoring the lookup for the file config.json in google/gemma-2b.\n", " warnings.warn(\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "755f646774be4f55878156816973d4c5", "version_major": 2, "version_minor": 0 }, "text/plain": [ "README.md: 0%| | 0.00/550 [00:00Below 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:\\nআমি জানি না তুমি কি চাও, তবে নিচে থেকে একটি বিজোড় নম্বর বেছে নেও\\n\\n### Input:\\n১, ২, ৩, ৪, ৫\\n\\n### Response:\\nএই তালিকাটিতে 1, 2, 3, 4, 5 নম্বর রয়েছে।']" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "if True:\n", " from unsloth import FastLanguageModel\n", " model, tokenizer = FastLanguageModel.from_pretrained(\n", " model_name = \"BanglaGemma2B160\", # 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) # Unsloth has 2x faster inference!\n", "\n", "# alpaca_prompt = You MUST copy from above!\n", "FastLanguageModel.for_inference(model) # Unsloth has 2x faster inference!\n", "inputs = tokenizer(\n", "[\n", " alpaca_prompt.format(\n", " \"আমি জানি না তুমি কি চাও, তবে নিচে থেকে একটি বিজোড় নম্বর বেছে নেও\", # instruction\n", " \"১, ২, ৩, ৪, ৫\", # 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": "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": 16, "metadata": { "id": "yFfaXG0WsQuE" }, "outputs": [], "source": [ "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", " \"BanglaGemma2B160\", # YOUR MODEL YOU USED FOR TRAINING\n", " load_in_4bit = load_in_4bit,\n", " )\n", " tokenizer = AutoTokenizer.from_pretrained(\"BanglaGemma2B160\")" ] }, { "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": 17, "metadata": { "id": "iHjt_SMYsd3P" }, "outputs": [], "source": [ "# Merge to 16bit\n", "if False: model.save_pretrained_merged(\"model\", tokenizer, save_method = \"merged_16bit\",)\n", "if False: model.push_to_hub_merged(\"hf/model\", tokenizer, save_method = \"merged_16bit\", token = \"\")\n", "\n", "# Merge to 4bit\n", "if False: model.save_pretrained_merged(\"model\", tokenizer, save_method = \"merged_4bit\",)\n", "if False: model.push_to_hub_merged(\"hf/model\", tokenizer, save_method = \"merged_4bit\", token = \"\")\n", "\n", "# Just LoRA adapters\n", "if False: model.save_pretrained_merged(\"model\", tokenizer, save_method = \"lora\",)\n", "if False: model.push_to_hub_merged(\"hf/model\", tokenizer, save_method = \"lora\", token = \"\")" ] }, { "cell_type": "markdown", "metadata": { "id": "TCv4vXHd61i7" }, "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.\n", "\n", "Some supported quant methods (full list on our [Wiki page](https://github.com/unslothai/unsloth/wiki#gguf-quantization-options)):\n", "* `q8_0` - Fast conversion. High resource use, but generally acceptable.\n", "* `q4_k_m` - Recommended. Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q4_K.\n", "* `q5_k_m` - Recommended. Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q5_K." ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "id": "FqfebeAdT073" }, "outputs": [], "source": [ "# Save to 8bit Q8_0\n", "if False: model.save_pretrained_gguf(\"model\", tokenizer,)\n", "if False: model.push_to_hub_gguf(\"hf/model\", tokenizer, token = \"\")\n", "\n", "# Save to 16bit GGUF\n", "if False: model.save_pretrained_gguf(\"model\", tokenizer, quantization_method = \"f16\")\n", "if False: model.push_to_hub_gguf(\"hf/model\", tokenizer, quantization_method = \"f16\", token = \"\")\n", "\n", "# Save to q4_k_m GGUF\n", "if False: model.save_pretrained_gguf(\"model\", tokenizer, quantization_method = \"q4_k_m\")\n", "if False: model.push_to_hub_gguf(\"hf/model\", tokenizer, quantization_method = \"q4_k_m\", token = \"\")" ] }, { "cell_type": "markdown", "metadata": { "id": "bDp0zNpwe6U_" }, "source": [ "Now, use the `model-unsloth.gguf` file or `model-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)." ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "BUge_oKXybms", "outputId": "58ec0a97-b810-4671-dc60-5800def8fe12" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Collecting git+https://github.com/unslothai/unsloth.git\n", " Cloning https://github.com/unslothai/unsloth.git to /tmp/pip-req-build-t0py8j2k\n", " Running command git clone --filter=blob:none --quiet https://github.com/unslothai/unsloth.git /tmp/pip-req-build-t0py8j2k\n", " Resolved https://github.com/unslothai/unsloth.git to commit 3085f4c3daacc63939e78e3c87759d0d03c5a71f\n", " Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n", " Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n", " Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n", "Collecting xformers<0.0.27\n", " Downloading xformers-0.0.26.post1-cp310-cp310-manylinux2014_x86_64.whl.metadata (1.0 kB)\n", "Requirement already satisfied: trl in /usr/local/lib/python3.10/dist-packages (0.11.1)\n", "Requirement already satisfied: peft in /usr/local/lib/python3.10/dist-packages (0.13.2)\n", "Requirement already satisfied: accelerate in /usr/local/lib/python3.10/dist-packages (0.34.2)\n", "Requirement already satisfied: bitsandbytes in /usr/local/lib/python3.10/dist-packages (0.44.1)\n", "Requirement already satisfied: unsloth-zoo in /usr/local/lib/python3.10/dist-packages (from unsloth==2024.10.2) (2024.10.2)\n", "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from unsloth==2024.10.2) (24.1)\n", "Requirement already satisfied: tyro in /usr/local/lib/python3.10/dist-packages (from unsloth==2024.10.2) (0.8.12)\n", "Requirement already satisfied: transformers>=4.44.2 in /usr/local/lib/python3.10/dist-packages (from unsloth==2024.10.2) (4.44.2)\n", "Requirement already 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This could take a while.\n", "INFO: pip is still looking at multiple versions of peft to determine which version is compatible with other requirements. This could take a while.\n", "INFO: This is taking longer than usual. You might need to provide the dependency resolver with stricter constraints to reduce runtime. See https://pip.pypa.io/warnings/backtracking for guidance. If you want to abort this run, press Ctrl + C.\n", " Downloading trl-0.4.2-py3-none-any.whl.metadata (7.2 kB)\n", " Downloading trl-0.4.1-py3-none-any.whl.metadata (7.2 kB)\n", " Downloading trl-0.4.0-py3-none-any.whl.metadata (7.2 kB)\n", " Downloading trl-0.3.1-py3-none-any.whl.metadata (7.0 kB)\n", " Downloading trl-0.3.0-py3-none-any.whl.metadata (7.0 kB)\n", " Downloading trl-0.2.1-py3-none-any.whl.metadata (6.5 kB)\n", " Downloading trl-0.2.0-py3-none-any.whl.metadata (6.6 kB)\n", " Downloading trl-0.1.0-py3-none-any.whl.metadata (6.8 kB)\n", "INFO: pip is looking at multiple versions of trl to determine which version is compatible with other requirements. This could take a while.\n", " Downloading trl-0.0.3-py3-none-any.whl.metadata (6.7 kB)\n", " Downloading trl-0.0.2-py3-none-any.whl.metadata (6.7 kB)\n", " Downloading trl-0.0.1-py3-none-any.whl.metadata (6.5 kB)\n", "\u001b[31mERROR: Cannot install trl==0.0.2, trl==0.0.3, trl==0.1.0 and unsloth[colab-new]==2024.10.2 because these package versions have conflicting dependencies.\u001b[0m\u001b[31m\n", "\u001b[0m\n", "The conflict is caused by:\n", " unsloth[colab-new] 2024.10.2 depends on transformers>=4.44.2; extra == \"colab-new\"\n", " trl 0.1.0 depends on transformers==4.3.2\n", " unsloth[colab-new] 2024.10.2 depends on transformers>=4.44.2; extra == \"colab-new\"\n", " trl 0.0.3 depends on transformers==4.3.2\n", " unsloth[colab-new] 2024.10.2 depends on transformers>=4.44.2; extra == \"colab-new\"\n", " trl 0.0.2 depends on transformers==2.6.0\n", "\n", "To fix this you could try to:\n", "1. loosen the range of package versions you've specified\n", "2. remove package versions to allow pip to attempt to solve the dependency conflict\n", "\n", "\u001b[31mERROR: ResolutionImpossible: for help visit https://pip.pypa.io/en/latest/topics/dependency-resolution/#dealing-with-dependency-conflicts\u001b[0m\u001b[31m\n", "\u001b[0m" ] } ], "source": [ "!pip install git+https://github.com/unslothai/unsloth.git \"unsloth[colab-new]\" \"xformers<0.0.27\" trl peft accelerate bitsandbytes\n" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 223, "referenced_widgets": [ "169ce9e3445841559134f3280c07a1bf", "39236852b9ae498c956838e38c232e0e", "ac32092031c74d8794a6d082ad1b733f", "eeb73145769c49968b0721d2a0eda8f5", "1e68ac7213154e9e9c564acd5122677e", "a4356e02df764f73a814c09c1f4f7b77", "d4649faabe494fd48150cd0a92f5feab", "104ebdd2e47d45589327f645d06090dc", "2e83cb1b383645f88a97b04712a4a3e0", "a44f87a31e1a42dba5f1595c9043fb51", "45904816717b4916b0344b9277fec2ec", "46d72041c7994c27a9792b7cbee62574", "2e838031e69342e4815b2b7a2870b035", "b13458495e754184897947b7c96a0499", "1969677eb4fa4126b1ddac986b86efee", "ab0ac00b2c1044bab063963362670c67", "10c75324ffcb4be0ae18fd304f3c73b9", "f3ba660f3db64851ad96568610b9d110", "0113f65fa6164ad7b318e4074c21ede7", "a9c10a689c834aad82418ea118b272fc", "a3b0cb01242d436394063aa4db15a8ea", "fa0be7a13fb848fc8affdfb772039659" ] }, "collapsed": true, "id": "PDTgImXTygdd", "outputId": "bed8943a-8924-4929-b16c-ae4ddde31189" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "==((====))== Unsloth 2024.10.2: Fast Gemma patching. Transformers = 4.44.2.\n", " \\\\ /| GPU: Tesla T4. Max memory: 14.748 GB. Platform = Linux.\n", "O^O/ \\_/ \\ Pytorch: 2.4.1+cu121. CUDA = 7.5. CUDA Toolkit = 12.1.\n", "\\ / Bfloat16 = FALSE. FA [Xformers = 0.0.28.post1. FA2 = False]\n", " \"-____-\" Free Apache license: http://github.com/unslothai/unsloth\n", "Unsloth: Fast downloading is enabled - ignore downloading bars which are red colored!\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "169ce9e3445841559134f3280c07a1bf", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Loading checkpoint shards: 0%| | 0/2 [00:00Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\\n### Instruction:\\nStart a casual conversation.\\n### Input:\\nHello, how are you today?\\n### Response:\\n### Instruction:\\n### Input:\\n### Response:\\n### Instruction:\\n### Input:\\n### Response:\\n### Instruction:\\n### Input:\\n### Response:\\n### Instruction:\\n### Input:\\n### Response:\\n### Instruction:\\n### Input:\\n### Response:\\n### Instruction:\\n']\n" ] } ], "source": [ "from unsloth import FastLanguageModel\n", "from transformers import AutoTokenizer\n", "\n", "# Load the model and tokenizer\n", "model_name = \"vaugheu/BanglaGemma2B160\"\n", "model, tokenizer = FastLanguageModel.from_pretrained(model_name)\n", "\n", "# Set up for inference\n", "FastLanguageModel.for_inference(model)\n", "\n", "# Define the prompt and input\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", "### Instruction:\n", "{}\n", "### Input:\n", "{}\n", "### Response:\n", "{}\"\"\"\n", "\n", "# Specify the chat prompt and input\n", "chat_instruction = \"Start a casual conversation.\"\n", "user_input = \"Hello, how are you today?\"\n", "\n", "# Format the prompt and input\n", "chat_text = alpaca_prompt.format(chat_instruction, user_input, \"\")\n", "chat_tokens = tokenizer([chat_text], return_tensors=\"pt\").to(\"cuda\")\n", "\n", "# Generate the chat response\n", "chat_output = model.generate(**chat_tokens, max_new_tokens=64, use_cache=True)\n", "chat_response = tokenizer.batch_decode(chat_output)\n", "\n", "# Display the chat response\n", "print(chat_response)" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 587, "referenced_widgets": [ "d17ea74054e44150baeaeb0f2b639726", "6b5e67aa5b0747b88dbf225692baca27", "4a550ea967d9433eb63372ad49e7d48e", "0d68bf34df80484ebd87cc6d7494a403", "a3f92c2e134b4bf881ac7ec8cf2f4f76", "b490fee0afc44436b71d05158e6afd87", "24e1e3bb5a7d404fb09dd795a096dc5d", "0dbe9a95b66d4ee08b091cb422522a29", "12aacda700fd49e6afa3183447d5bf41", "21503159ebbe4071920a5efb1a95b4eb", "c2e9c38d8ca34278b248ace626e725de" ] }, "id": "YW_yXAYDypjW", "outputId": "a4a1cd07-be9c-47d2-b03b-fcb3e676d945" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "==((====))== Unsloth 2024.10.2: Fast Gemma patching. Transformers = 4.44.2.\n", " \\\\ /| GPU: Tesla T4. Max memory: 14.748 GB. Platform = Linux.\n", "O^O/ \\_/ \\ Pytorch: 2.4.1+cu121. CUDA = 7.5. CUDA Toolkit = 12.1.\n", "\\ / Bfloat16 = FALSE. FA [Xformers = 0.0.28.post1. FA2 = False]\n", " \"-____-\" Free Apache license: http://github.com/unslothai/unsloth\n", "Unsloth: Fast downloading is enabled - ignore downloading bars which are red colored!\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "d17ea74054e44150baeaeb0f2b639726", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Loading checkpoint shards: 0%| | 0/2 [00:00\n", "Input: hello\n", "### Instruction:\n", "### Input:\n", "### Response:\n", "### Instruction:\n", "### Input:\n", "### Response:\n", "### Instruction:\n", "### Input:\n", "### Response:\n", "### Instruction:\n", "### Input:\n", "### Response:\n", "### Instruction:\n", "### Input:\n", "### Response:\n", "### Instruction:\n", "Input: vat khaba?\n", "\"vat khaba?\" is a request that asks for clarification. It is unclear what \"vat\" refers to, so it is difficult to provide a response that appropriately completes the request.\n", "Input: অট্রেলিয়ার রাজধানী কি?\n", "অট্রেলিয়ার রাজধানী হল অট্রেলিয়ার।\n", "Input: bye\n", "\"Bye!\"\n" ] } ], "source": [ "from unsloth import FastLanguageModel\n", "from transformers import AutoTokenizer\n", "\n", "# Load the model and tokenizer\n", "model_name = \"vaugheu/BanglaGemma2B160\"\n", "model, tokenizer = FastLanguageModel.from_pretrained(model_name)\n", "\n", "# Set up for inference\n", "FastLanguageModel.for_inference(model)\n", "\n", "# Define the prompt and input\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", "### Instruction:\n", "{}\n", "### Input:\n", "{}\n", "### Response:\n", "{}\"\"\"\n", "\n", "# Start the chat loop\n", "bye = 0\n", "while bye == 0:\n", " # Get user input for instruction and input\n", " # chat_instruction = input(\"Instruction: \")\n", " chat_instruction = \"Start a casual conversation.\"\n", " user_input = input(\"Input: \")\n", "\n", " # Format the prompt and input\n", " chat_text = alpaca_prompt.format(chat_instruction, user_input, \"\")\n", " chat_tokens = tokenizer([chat_text], return_tensors=\"pt\").to(\"cuda\")\n", "\n", " # Generate the chat response\n", " chat_output = model.generate(**chat_tokens, max_new_tokens=64, use_cache=True)\n", " chat_response = tokenizer.batch_decode(chat_output)\n", "\n", " # Display the chat response\n", " # print(\"Response:\", chat_response)\n", "\n", " # Since chat_response is a list, extract the first item and then find the response part\n", " chat_response_text = chat_response[0]\n", "\n", " # Extract the response part\n", " start_index = chat_response_text.find(\"Response:\") + len(\"Response:\")\n", " response = chat_response_text[start_index:].strip()\n", "\n", " print(response)\n", "\n", "\n", " # Check if user wants to exit\n", " # (\"Type 'bye' to exit: \")\n", " if user_input.lower() == \"bye\":\n", " bye = 1" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "6ZGaLphg4-Be" }, "outputs": [], "source": [] } ], "metadata": { "accelerator": "GPU", "colab": { "gpuType": "T4", "provenance": [] }, "kernelspec": { "display_name": "Python 3", "name": "python3" }, "language_info": { "name": "python" }, "widgets": { "application/vnd.jupyter.widget-state+json": { "001ec4624279477087750d0166e0a15a": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", 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