From feeaffbe958002ae871b265ad08dd2b008d4d5ca Mon Sep 17 00:00:00 2001 From: Sumit <108853577+Sumitwarrior7@users.noreply.github.com> Date: Tue, 25 Feb 2025 00:32:55 +0530 Subject: [PATCH] Created using Colab --- nb/Llama3_(8B)-Ollama.ipynb | 8074 +++++++++++++++++++++++++++++++++++ 1 file changed, 8074 insertions(+) create mode 100644 nb/Llama3_(8B)-Ollama.ipynb diff --git a/nb/Llama3_(8B)-Ollama.ipynb b/nb/Llama3_(8B)-Ollama.ipynb new file mode 100644 index 0000000000..d45cc80e75 --- /dev/null +++ b/nb/Llama3_(8B)-Ollama.ipynb @@ -0,0 +1,8074 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "OuJ1cKNIK6Ot" + }, + "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 + ⭐ Star us on Github ⭐\n", + "
\n", + "\n", + "To install Unsloth on your own computer, follow the installation instructions on our Github page [here](https://docs.unsloth.ai/get-started/installing-+-updating).\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)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "0iz0iXvAK6O0" + }, + "source": [ + "### News" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "APX_nCyfK6O3" + }, + "source": [ + "**Read our [blog post](https://unsloth.ai/blog/r1-reasoning) for guidance on how to train reasoning models.**\n", + "\n", + "Visit our docs for all our [model uploads](https://docs.unsloth.ai/get-started/all-our-models) and [notebooks](https://docs.unsloth.ai/get-started/unsloth-notebooks).\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "2JCupT95K6O4" + }, + "source": [ + "### Installation" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "id": "lqVDl_m9K6O5" + }, + "outputs": [], + "source": [ + "%%capture\n", + "import os\n", + "if \"COLAB_\" not in \"\".join(os.environ.keys()):\n", + " !pip install unsloth\n", + "else:\n", + " # Do this only in Colab and Kaggle notebooks! Otherwise use pip install unsloth\n", + " !pip install --no-deps bitsandbytes accelerate xformers==0.0.29 peft trl triton\n", + " !pip install --no-deps cut_cross_entropy unsloth_zoo\n", + " !pip install sentencepiece protobuf datasets huggingface_hub hf_transfer\n", + " !pip install --no-deps unsloth" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "gbtDLHHJK6O7" + }, + "source": [ + "### Unsloth" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 316, + "referenced_widgets": [ + "6f98440a4d6a4424b3ea32292bf8d990", + "d837757e66e84e118e08f851c208c37f", + "f4cc0012278c428da179462312d061f3", + "e241510e026641d79b423315774beab5", + "0447b59db66f45e58f394686a25b510a", + "d2f92706c1b54e30b36f8752ded1b9c6", + "ca8bf49666ff4447bf4ae5fc0594b01c", + "31662062bce940008129e00e8da2d49b", + "558562ef1ebf44239ae3db2f469a0792", + "77611cf9b5d845028916d10387aef71d", + "52651a38c7774f8994890a0ccfd4ccd3", + "fcf41db70c3c4d539e145bddc087b7b9", + "a71877e7441d4beaa0d0ed7d15af80f8", + "d37d438f1ceb430b8e5d2dbfde42f235", + "a5b069a432834023a89a2baf8252f554", + "da642d5177684c81ad706ece7e6aa217", + "3ff91c9376e44aafa247a2bc0770b8aa", + "9b04e55c7bec4cfda9a6470ae9d7ee0a", + "274f66c10563453b886c86c8f48d5dea", + "6c44385187d34f06a27a49493900d809", + "7912cfefbdbe479cacfefdfb4fb78ef3", + "6e0dd7dc45224dc3ab4e17d293c213a7", + "fe5482b4ab1b48ecb947b5792fa6e311", + "0446c21a11eb4cc7a296b617eda2c7d1", + "8f8cbf9090284543a99021d31c659428", + "fd3739b743414aff97dc3e7066f0b1c0", + "53e4b64e04e747ef84156256b31aa786", + "a69127433d4a456fbfc09b8554fa28ef", + "567a401cc3a047b8a4e8d1923e5d12a1", + "d86409d21d8a40c59dc32f8985a562a6", + "5ff597f6ca1c42719602332c69011c41", + "9b8144d9f73f4b18ab372d2d7ba24ffd", + "1e90d60c866c488aaf4ab5f616b7bafd", + "9ac540e6ac644a3092a27094903493f8", + "b98abfbb1376414faa7dfc7f18d44265", + "3a14258263644a1a877cb762c47d1ae2", + "3c0c66125b3a465bb5e8e62ce2482e1d", + "78c6216583384ed4b2bd286da417eafc", + "43e8963b001e4f86a1126188a7c3d4ce", + "802f1dd78702482398b7c24e8cfa03d3", + "a02abf39f6c241068a27d92ad4e48219", + "0d9fa07199d94fcda0be108a74c67851", + "a115b8a8df9048c99d52766d2c4cd05c", + "4fdcde623ac3471190114862ff5bb0c5", + "ae957f2bb99e45c387d9ba801c9dbab9", + "37ab9ac2702b4694bdd4d992781da31d", + "14aa6dd060bb4a6885d7588d2a35dc7b", + "f50a9084a39e4b86888f7c41b58679dd", + "f1f0fc2787b14dd6b618200cd169d258", + "c070420f1cd5409186677c8a93da7bbc", + "dd38d7179b1b449aad77be4c22de45ce", + "ae87aae78385438ea8be9ac9a2c48a90", + "cb0cdf301eae409a9db2c8d12d85454a", + "47dc406aaca54bff9c0c528bba2cc20f", + "3be4e9f1e4074bbaa870ecabcb49efbb" + ] + }, + "id": "QmUBVEnvCDJv", + "outputId": "ad487157-b7b7-41b8-e160-26140a4cd714" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "🦥 Unsloth: Will patch your computer to enable 2x faster free finetuning.\n", + "🦥 Unsloth Zoo will now patch everything to make training faster!\n", + "==((====))== Unsloth 2025.2.15: Fast Llama patching. Transformers: 4.48.3.\n", + " \\\\ /| GPU: Tesla T4. Max memory: 14.741 GB. Platform: Linux.\n", + "O^O/ \\_/ \\ Torch: 2.5.1+cu124. CUDA: 7.5. CUDA Toolkit: 12.4. Triton: 3.1.0\n", + "\\ / Bfloat16 = FALSE. FA [Xformers = 0.0.29. 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" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "model.safetensors: 0%| | 0.00/5.70G [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 [vicgalle](https://huggingface.co/datasets/vicgalle/alpaca-gpt4), which is a version of 52K of the original [Alpaca dataset](https://crfm.stanford.edu/2023/03/13/alpaca.html) generated from GPT4. You can replace this code section with your own data prep." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "HvOPfPnet76H" + }, + "outputs": [], + "source": [ + "from datasets import load_dataset\n", + "\n", + "dataset = load_dataset(\"vicgalle/alpaca-gpt4\", split=\"train\")\n", + "print(dataset.column_names)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "xg4_dG-m0Cz4" + }, + "source": [ + "One issue is this dataset has multiple columns. For `Ollama` and `llama.cpp` to function like a custom `ChatGPT` Chatbot, we must only have 2 columns - an `instruction` and an `output` column." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "DTQR4jrDMcJf", + "outputId": "fa235916-7b27-42d2-94fd-e8dbfcda3e75" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "['instruction', 'input', 'output', 'text']\n" + ] + } + ], + "source": [ + "print(dataset.column_names)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "MwEbRFl0Mf3E" + }, + "source": [ + "To solve this, we shall do the following:\n", + "* Merge all columns into 1 instruction prompt.\n", + "* Remember LLMs are text predictors, so we can customize the instruction to anything we like!\n", + "* Use the `to_sharegpt` function to do this column merging process!\n", + "\n", + "For example below in our [Titanic CSV finetuning notebook](https://colab.research.google.com/drive/1VYkncZMfGFkeCEgN2IzbZIKEDkyQuJAS?usp=sharing), we merged multiple columns in 1 prompt:\n", + "\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "w61VJ7rQM8jT" + }, + "source": [ + "To merge multiple columns into 1, use `merged_prompt`.\n", + "* Enclose all columns in curly braces `{}`.\n", + "* Optional text must be enclused in `[[]]`. For example if the column \"Pclass\" is empty, the merging function will not show the text and skp this. This is useful for datasets with missing values.\n", + "* You can select every column, or a few!\n", + "* Select the output or target / prediction column in `output_column_name`. For the Alpaca dataset, this will be `output`.\n", + "\n", + "To make the finetune handle multiple turns (like in ChatGPT), we have to create a \"fake\" dataset with multiple turns - we use `conversation_extension` to randomnly select some conversations from the dataset, and pack them together into 1 conversation." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 209, + "referenced_widgets": [ + "56319ed648e941e9a4ba62a28398ef0d", + "f98799f40dc141b584376c1a4cfbefe3", + "b732394173274baa9e2b36cc505d61b2", + "258b8376b420444c8b184945ee95cdb3", + "6d5e43bd16674705b531c1f3a0e3e24b", + "c956b8f3a50743f7b3c064a438ebf798", + "14f6c1ae7d5641e69c1b4b983b891469", + "fa4055d55e6e474aa85f6a5b39f3e3fb", + "f90eab6b3a5a4486bda1d4bbfca48de2", + "75e1e21ecce14c2fb9e47f5ee7989154", + "ca202657030344eeabbb07aa44f0f1d6", + "902a9535caf943588f56007c40ad8380", + "4bf4a9bf2a6c411786a2deb839602ae9", + "5d67fae99b6a4a82b7865cd17d3ed52d", + "b5aa373613f54132a07c905cf549efab", + "fa8f5ffa31f3429590b463e0e04d01ed", + "6d9869ccafeb4318bd4e90e5eb957bc5", + "1918fb1b15004fc9bc60020977f377b4", + "09febb189b6d4e5289a6dc1c3d934f53", + "4de8d851daba412088894fc5c74344a1", + "923d6c62f33049ec8a36a9a13ec56b04", + "4708eef08ec744f5898a1ffd413ad3b2", + "b043bcdc7f2841ca910edf0ecebacebf", + "807246e8379b41018087f2d940055fc8", + "5841f839432d4c8789a884a6bd00566e", + "ac670254cf724c7ba91485cfa4ba5ce0", + "cf0f4a39d5514f01a11831415c9c0e14", + "cfadbeaed5fc4dbdbe27e45cf5f1fd6b", + "e58a85d1df75462ca6a7cfb9033a9e45", + "503a6ff2d5ee4d58ac1b247490245b01", + "e12121093f834c6da5e2856ce6ed9f20", + "7caa7401442a49769985d1e579f13ec5", + "16551345646f4d02ab9a1fe21de93a43", + "5fc342312eb04e29a2d870dab5014082", + "858ff08f5b04407784b631e4cb95cd32", + "787b3002c94e4e82a54687f3a597fae7", + "eadb1fcce96c4bf895584d6395479aff", + "d8f5b9490c04465cbbc1b819bb764fa8", + "7f48f8398f3d439c84bc3ed46f5b7463", + "9026e3708195456d97f27ea3af6236ab", + "4ee00570fcb34a33b28fa78bb3bfd02f", + "493e15b2a65f4665ac2a52404266718c", + "d4d7989dddae4ca2b279e046e499aaba", + "d76ffd676b50486eaf55953f4c891e28", + "ec0adf49e31e412485152df02fe95248", + "dc084954aa2d496482e63ef566449f84", + "2285e8cee4a84fffb4ef021ccb36b192", + "958073fcb9e6423b917de7f98b7920a1", + "bd9dd3d136cc4f9c8fbc2ec6d7020291", + "8bc426cbe95b462c9578ce58d838f92d", + "cd772145a365455e8ec3ada2ae3ec823", + "9a348b84fcdb46dcb1365509ca077ea6", + "a0d2fb8456874f099226520c670a509f", + "f838e90eca1c48c2b1ad2f947c62783e", + "c1f45b53a8ea4d629719876cfd939938", + "1b300840ace14113833f86e453034eb5", + "7f4f93c27302413399e796f267a799d8", + "2f72f8ac7ee843e08864e42552fe061b", + "7bca316ce87e45bb897b0bddb60ec8b1", + "22cf49d0b9494464818bfb9410246990", + "82f268db3bc84c1c80c9853556c37431", + "796a300cb93e49c78b74df7a11495c78", + "d87246ad9b134a3fbfd900d5c5ac9c6b", + "367bb814b67b4d71a54901fec643b2e8", + "03cd3c9d82d546b49513838e0683824c", + "3d36420020af4b95a318bf424caf2a93" + ] + }, + "id": "jZxeGSeX0CR8", + "outputId": "71cb9f8a-91a9-4de4-a1b9-a1dcefa49f52" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Merging columns: 0%| | 0/52002 [00:00<|start_header_id|>system<|end_header_id|>\n", + "\n", + "{SYSTEM}<|eot_id|><|start_header_id|>user<|end_header_id|>\n", + "\n", + "{INPUT}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n", + "\n", + "{OUTPUT}<|eot_id|>\"\"\"\n", + "```\n", + "\n", + "For the ChatML format:\n", + "```python\n", + "chat_template = \"\"\"<|im_start|>system\n", + "{SYSTEM}<|im_end|>\n", + "<|im_start|>user\n", + "{INPUT}<|im_end|>\n", + "<|im_start|>assistant\n", + "{OUTPUT}<|im_end|>\"\"\"\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "EK-_ncj-RCNy" + }, + "source": [ + "The issue is the Alpaca format has 3 fields, whilst OpenAI style chatbots must only use 2 fields (instruction and response). That's why we used the `to_sharegpt` function to merge these columns into 1." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 66, + "referenced_widgets": [ + "c11aa5f795e644058a464062f2788e55", + "6222eb9d47e64f8b9573a0a8feccd463", + "130510ad80c84cf6940d42d59e36e40b", + "593540b3c27044b38bbcc9198667cebc", + "2f406e86560c4463b9cd6f644f6a1396", + "2161607647114f5a96a5c3c89b4835aa", + "3e492ee1308a4528b3f364b0fdef8a1a", + "891e038c882c422e8d8b3783f069b853", + "4482e4665f0e48ce834ed8bd55521c1c", + "f548b42e45df415d8ee3775d00dc6a5a", + "a569ba2aeac547c9aa2ce656c869cd52" + ] + }, + "id": "JOGaZf1sdLlr", + "outputId": "a57d0bcb-1249-4e8c-9648-62fe3a29224f" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "Unsloth: We automatically added an EOS token to stop endless generations.\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Map: 0%| | 0/52002 [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": 10, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 145, + "referenced_widgets": [ + "9037e610557243cab56d07decbb6fddd", + "d392536e2d33408eb16fc5b396ecea51", + "07dd0bf3ce0f48358a1a85eac30c804a", + "b347af01ef9944f581935134b408f1b8", + "307e4182dddf4064aed321ed8a1c3ed6", + "8cfc05fa050d4e268c2bcf6a8698d340", + "5cedcd01fece466aacbc6d365236517a", + "b75d43cd6c514c7cb79209166cfcaee9", + "60e51f0fc1fd41bc92d41235504cc1af", + "6947924acd104960b76d81907a7153e6", + "a5e74b1df3a24f499d687c60710a8a0d", + "618d6466db824a65a4f3ca26b6836c5a", + "64a5951e183f4622baf81c6a982c259e", + "b9807157afd84e8b897635a1b4c0799d", + "1dd3a14575c04c10bc05a515e237bb62", + "fd2e64255fe642fdb9b78791abc92fce", + "89056d09ce474645806eda732754c287", + "d6c462cfa17f4daeaf602e3fd0b1803a", + "d237e588e2ac425cbb4921fd29733c6a", + "14310830d8cb4d9f906bd407be600f7d", + "ca2f4a11f49a4081aabeae4d06400bc3", + "e6f3e2f96bcd4a659249f9881d5fa77a", + "889b9053dd6b4adf88f6d68d171003d8", + "ffa90564f27448e4b8ad99defa829a96", + "57988c9325e341e6ae5edce0251c7be8", + "2fe1c3c854c8478faa56b2dab193b417", + "2b92599124fd4a639c1f6fe137f59d75", + "0afc0a911c0f4c3a92be6ea0b82b48c2", + "f946f9fbe15041e89b3f134a0c8baacb", + "78a9a7ccbd5c4a409832de4974b7cd3e", + "6a927a28ec9b40e4bb7390942ff51e59", + "e26285b978c742ee9933335778d7eaed", + "761a9598bd4449f6a2f12c6f8862aba3", + "4c88bb06509f48d0ab368a7e900529be", + "e3a57d1400cd47f69bee13276b2407e6", + "b32c66e1b12a45bdbc4aacfb5769456b", + "21c02fee2d2b4cb2adb55625026f366d", + "bcf9f1c7b4ac4f0fa1aec58287448dcd", + "f27f9abb9bd54c789e6c9707d3d0ac78", + "26bbe7e7f2e24822b340f0db42aff4ae", + "a15ae38989304691b071ce2b4001a881", + "01a6fdd8d9c24bf18a047b6e5e0d2f92", + "f7aaa62229514854af7c3f8a9e5ff6a2", + "1a68f3a009754031bf88cf46017bc379" + ] + }, + "id": "95_Nn-89DhsL", + "outputId": "25fd3cf6-b8e2-4cc1-87c7-88fa9fd0dd6f" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Converting train dataset to ChatML (num_proc=2): 0%| | 0/52002 [00:00" + ], + "text/html": [ + "\n", + "
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StepTraining Loss
11.497500
21.466800
31.434800
41.530100
51.624800
61.300100
71.308200
81.307700
91.339500
101.197300
111.228700
121.258100
131.061600
141.130000
151.152900
161.098600
171.068200
181.207000
191.124300
201.104200
211.049100
221.123900
230.969600
241.071200
251.044900
261.065300
271.113600
281.104300
291.092400
301.161200
311.114100
321.107000
331.004800
341.105100
351.161800
361.165900
371.079000
381.034500
390.996300
401.036500
411.049100
421.068600
431.181000
441.121000
451.005500
461.061200
471.095500
481.143000
491.059500
501.233200
511.098000
521.107700
531.079500
540.998400
550.999300
561.071900
571.014600
581.054300
591.003700
601.031300

" + ] + }, + "metadata": {} + } + ], + "source": [ + "# This starts the training process.\n", + "trainer_stats = trainer.train()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "cellView": "form", + "id": "pCqnaKmlO1U9" + }, + "outputs": [], + "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(\n", + " f\"{round(trainer_stats.metrics['train_runtime']/60, 2)} minutes used for training.\"\n", + ")\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! Unsloth makes inference natively 2x faster as well! You should use prompts which are similar to the ones you had finetuned on, otherwise you might get bad results!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "kR3gIAX-SM2q", + "outputId": "40b5e499-1ad5-4907-a284-9416a36d010c" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "The attention mask is not set and cannot be inferred from input because pad token is same as eos token. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The next number in the Fibonacci sequence is 13.<|end_of_text|>\n" + ] + } + ], + "source": [ + "FastLanguageModel.for_inference(model) # Enable native 2x faster inference\n", + "messages = [ # Change below!\n", + " {\"role\": \"user\", \"content\": \"Follow bodmas strategy and then answer what is the value of :4-6+8?\"},\n", + "]\n", + "input_ids = tokenizer.apply_chat_template(\n", + " messages,\n", + " add_generation_prompt = True,\n", + " return_tensors = \"pt\",\n", + ").to(\"cuda\")\n", + "\n", + "from transformers import TextStreamer\n", + "\n", + "text_streamer = TextStreamer(tokenizer, skip_prompt = True)\n", + "_ = model.generate(\n", + " input_ids,\n", + " streamer = text_streamer,\n", + " max_new_tokens = 128,\n", + " pad_token_id = tokenizer.eos_token_id\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "CrSvZObor0lY" + }, + "source": [ + "Since we created an actual chatbot, you can also do longer conversations by manually adding alternating conversations between the user and assistant!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "JcbFUWEyQVaE", + "outputId": "517ed3fe-009e-4ebf-d233-2883e943de82" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "France's tallest tower is called the Eiffel Tower.<|end_of_text|>\n" + ] + } + ], + "source": [ + "FastLanguageModel.for_inference(model) # Enable native 2x faster inference\n", + "messages = [ # Change below!\n", + " {\"role\": \"user\", \"content\": \"Continue the fibonacci sequence! Your input is 1, 1, 2, 3, 5, 8\"},\n", + " {\"role\": \"assistant\", \"content\": \"The fibonacci sequence continues as 13, 21, 34, 55 and 89.\"},\n", + " {\"role\": \"user\", \"content\": \"What is France's tallest tower called?\"},\n", + "]\n", + "input_ids = tokenizer.apply_chat_template(\n", + " messages,\n", + " add_generation_prompt = True,\n", + " return_tensors = \"pt\",\n", + ").to(\"cuda\")\n", + "\n", + "from transformers import TextStreamer\n", + "text_streamer = TextStreamer(tokenizer, skip_prompt = True)\n", + "_ = model.generate(input_ids, streamer = text_streamer, max_new_tokens = 128, pad_token_id = tokenizer.eos_token_id)" + ] + }, + { + "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": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "upcOlWe7A1vc", + "outputId": "587e0389-0044-47a2-f691-581d262ea95c" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "('lora_model/tokenizer_config.json',\n", + " 'lora_model/special_tokens_map.json',\n", + " 'lora_model/tokenizer.json')" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.save_pretrained(\"lora_model\") # Local saving\n", + "tokenizer.save_pretrained(\"lora_model\")\n", + "# model.push_to_hub(\"your_name/lora_model\", token = \"...\") # Online saving\n", + "# tokenizer.push_to_hub(\"your_name/lora_model\", token = \"...\") # Online saving" + ] + }, + { + "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": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "MKX_XKs_BNZR", + "outputId": "98ec2273-2e01-4062-c576-1ffb7b3afdb0" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The sequence 1, 1, 2, 3, 5, 8 is a special sequence known as the Fibonacci sequence. The Fibonacci sequence is a series of numbers where each number is the sum of the two previous numbers, starting with 0 and 1. In this case, the sequence is 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89, 144, and so on. The Fibonacci sequence has many interesting properties and is widely studied in mathematics and computer science.<|end_of_text|>\n" + ] + } + ], + "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", + "pass\n", + "\n", + "messages = [ # Change below!\n", + " {\"role\": \"user\", \"content\": \"Describe anything special about a sequence. Your input is 1, 1, 2, 3, 5, 8,\"},\n", + "]\n", + "input_ids = tokenizer.apply_chat_template(\n", + " messages,\n", + " add_generation_prompt = True,\n", + " return_tensors = \"pt\",\n", + ").to(\"cuda\")\n", + "\n", + "from transformers import TextStreamer\n", + "text_streamer = TextStreamer(tokenizer, skip_prompt = True)\n", + "_ = model.generate(input_ids, streamer = text_streamer, max_new_tokens = 128, pad_token_id = tokenizer.eos_token_id)" + ] + }, + { + "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": null, + "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", + " \"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": "XOFzC441vCtq" + }, + "source": [ + "\n", + "### Ollama Support\n", + "\n", + "[Unsloth](https://github.com/unslothai/unsloth) now allows you to automatically finetune and create a [Modelfile](https://github.com/ollama/ollama/blob/main/docs/modelfile.md), and export to [Ollama](https://ollama.com/)! This makes finetuning much easier and provides a seamless workflow from `Unsloth` to `Ollama`!\n", + "\n", + "Let's first install `Ollama`!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "NUxcyP_UfeLl", + "outputId": "69972ce0-9caf-41fd-b19a-fa058521990b" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + ">>> Installing ollama to /usr/local\n", + ">>> Downloading Linux amd64 bundle\n", + "############################################################################################# 100.0%\n", + ">>> Creating ollama user...\n", + ">>> Adding ollama user to video group...\n", + ">>> Adding current user to ollama group...\n", + ">>> Creating ollama systemd service...\n", + "WARNING: Unable to detect NVIDIA/AMD GPU. Install lspci or lshw to automatically detect and install GPU dependencies.\n", + ">>> The Ollama API is now available at 127.0.0.1:11434.\n", + ">>> Install complete. Run \"ollama\" from the command line.\n" + ] + } + ], + "source": [ + "!curl -fsSL https://ollama.com/install.sh | sh" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "TCv4vXHd61i7" + }, + "source": [ + "Next, we shall save the model to GGUF / llama.cpp\n", + "\n", + "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.\n", + "\n", + "We also support saving to multiple GGUF options in a list fashion! This can speed things up by 10 minutes or more if you want multiple export formats!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "FqfebeAdT073", + "outputId": "9d2292eb-1e31-4c88-9b44-5371e4104abf" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Unsloth: ##### The current model auto adds a BOS token.\n", + "Unsloth: ##### Your chat template has a BOS token. We shall remove it temporarily.\n", + "Unsloth: You have 1 CPUs. Using `safe_serialization` is 10x slower.\n", + "We shall switch to Pytorch saving, which will take 3 minutes and not 30 minutes.\n", + "To force `safe_serialization`, set it to `None` instead.\n", + "Unsloth: Kaggle/Colab has limited disk space. 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 5.7G\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Unsloth: Merging 4bit and LoRA weights to 16bit...\n", + "Unsloth: Will use up to 5.49 out of 12.67 RAM for saving.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 47%|████▋ | 15/32 [00:01<00:01, 9.79it/s]We will save to Disk and not RAM now.\n", + "100%|██████████| 32/32 [01:41<00:00, 3.18s/it]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Unsloth: Saving tokenizer... Done.\n", + "Unsloth: Saving model... This might take 5 minutes for Llama-7b...\n", + "Unsloth: Saving model/pytorch_model-00001-of-00004.bin...\n", + "Unsloth: Saving model/pytorch_model-00002-of-00004.bin...\n", + "Unsloth: Saving model/pytorch_model-00003-of-00004.bin...\n", + "Unsloth: Saving model/pytorch_model-00004-of-00004.bin...\n", + "Done.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Unsloth: Converting llama model. Can use fast conversion = False.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "==((====))== Unsloth: Conversion from QLoRA to GGUF information\n", + " \\\\ /| [0] Installing llama.cpp will take 3 minutes.\n", + "O^O/ \\_/ \\ [1] Converting HF to GGUF 16bits will take 3 minutes.\n", + "\\ / [2] Converting GGUF 16bits to ['q8_0'] will take 10 minutes each.\n", + " \"-____-\" In total, you will have to wait at least 16 minutes.\n", + "\n", + "Unsloth: [0] Installing llama.cpp. This will take 3 minutes...\n", + "Unsloth: [1] Converting model at model into q8_0 GGUF format.\n", + "The output location will be ./model/unsloth.Q8_0.gguf\n", + "This will take 3 minutes...\n", + "INFO:hf-to-gguf:Loading model: model\n", + "INFO:gguf.gguf_writer:gguf: This GGUF file is for Little Endian only\n", + "INFO:hf-to-gguf:Exporting model...\n", + "INFO:hf-to-gguf:gguf: loading model weight map from 'pytorch_model.bin.index.json'\n", + "INFO:hf-to-gguf:gguf: loading model part 'pytorch_model-00001-of-00004.bin'\n", + "INFO:hf-to-gguf:token_embd.weight, torch.float16 --> Q8_0, shape = {4096, 128256}\n", + "INFO:hf-to-gguf:blk.0.attn_q.weight, torch.float16 --> Q8_0, shape = {4096, 4096}\n", + "INFO:hf-to-gguf:blk.0.attn_k.weight, torch.float16 --> Q8_0, shape = {4096, 1024}\n", + "INFO:hf-to-gguf:blk.0.attn_v.weight, torch.float16 --> Q8_0, shape = {4096, 1024}\n", + "INFO:hf-to-gguf:blk.0.attn_output.weight, torch.float16 --> Q8_0, shape = {4096, 4096}\n", + "INFO:hf-to-gguf:blk.0.ffn_gate.weight, torch.float16 --> Q8_0, shape = {4096, 14336}\n", + "INFO:hf-to-gguf:blk.0.ffn_up.weight, torch.float16 --> Q8_0, shape = {4096, 14336}\n", + "INFO:hf-to-gguf:blk.0.ffn_down.weight, torch.float16 --> Q8_0, shape = {14336, 4096}\n", + "INFO:hf-to-gguf:blk.0.attn_norm.weight, torch.float16 --> F32, shape = {4096}\n", + "INFO:hf-to-gguf:blk.0.ffn_norm.weight, torch.float16 --> F32, shape = {4096}\n", + "INFO:hf-to-gguf:blk.1.attn_q.weight, torch.float16 --> Q8_0, shape = {4096, 4096}\n", + "INFO:hf-to-gguf:blk.1.attn_k.weight, torch.float16 --> Q8_0, shape = {4096, 1024}\n", + "INFO:hf-to-gguf:blk.1.attn_v.weight, torch.float16 --> Q8_0, shape = {4096, 1024}\n", + "INFO:hf-to-gguf:blk.1.attn_output.weight, torch.float16 --> Q8_0, shape = {4096, 4096}\n", + "INFO:hf-to-gguf:blk.1.ffn_gate.weight, torch.float16 --> Q8_0, shape = {4096, 14336}\n", + "INFO:hf-to-gguf:blk.1.ffn_up.weight, torch.float16 --> Q8_0, shape = {4096, 14336}\n", + "INFO:hf-to-gguf:blk.1.ffn_down.weight, torch.float16 --> Q8_0, shape = {14336, 4096}\n", + "INFO:hf-to-gguf:blk.1.attn_norm.weight, torch.float16 --> F32, shape = {4096}\n", + "INFO:hf-to-gguf:blk.1.ffn_norm.weight, torch.float16 --> F32, shape = {4096}\n", + "INFO:hf-to-gguf:blk.2.attn_q.weight, torch.float16 --> Q8_0, shape = {4096, 4096}\n", + "INFO:hf-to-gguf:blk.2.attn_k.weight, torch.float16 --> Q8_0, shape = {4096, 1024}\n", + "INFO:hf-to-gguf:blk.2.attn_v.weight, torch.float16 --> Q8_0, shape = {4096, 1024}\n", + "INFO:hf-to-gguf:blk.2.attn_output.weight, torch.float16 --> Q8_0, shape = {4096, 4096}\n", + "INFO:hf-to-gguf:blk.2.ffn_gate.weight, torch.float16 --> Q8_0, shape = {4096, 14336}\n", + "INFO:hf-to-gguf:blk.2.ffn_up.weight, torch.float16 --> Q8_0, shape = {4096, 14336}\n", + "INFO:hf-to-gguf:blk.2.ffn_down.weight, torch.float16 --> Q8_0, shape = {14336, 4096}\n", + "INFO:hf-to-gguf:blk.2.attn_norm.weight, torch.float16 --> F32, shape = {4096}\n", + "INFO:hf-to-gguf:blk.2.ffn_norm.weight, torch.float16 --> F32, shape = {4096}\n", + "INFO:hf-to-gguf:blk.3.attn_q.weight, torch.float16 --> Q8_0, shape = {4096, 4096}\n", + "INFO:hf-to-gguf:blk.3.attn_k.weight, torch.float16 --> Q8_0, shape = {4096, 1024}\n", + "INFO:hf-to-gguf:blk.3.attn_v.weight, torch.float16 --> Q8_0, shape = {4096, 1024}\n", + "INFO:hf-to-gguf:blk.3.attn_output.weight, torch.float16 --> Q8_0, shape = {4096, 4096}\n", + "INFO:hf-to-gguf:blk.3.ffn_gate.weight, torch.float16 --> Q8_0, shape = {4096, 14336}\n", + "INFO:hf-to-gguf:blk.3.ffn_up.weight, torch.float16 --> Q8_0, shape = {4096, 14336}\n", + "INFO:hf-to-gguf:blk.3.ffn_down.weight, torch.float16 --> Q8_0, shape = {14336, 4096}\n", + "INFO:hf-to-gguf:blk.3.attn_norm.weight, torch.float16 --> F32, shape = {4096}\n", + "INFO:hf-to-gguf:blk.3.ffn_norm.weight, torch.float16 --> F32, shape = {4096}\n", + "INFO:hf-to-gguf:blk.4.attn_q.weight, torch.float16 --> Q8_0, shape = {4096, 4096}\n", + "INFO:hf-to-gguf:blk.4.attn_k.weight, torch.float16 --> Q8_0, shape = {4096, 1024}\n", + "INFO:hf-to-gguf:blk.4.attn_v.weight, torch.float16 --> Q8_0, shape = {4096, 1024}\n", + "INFO:hf-to-gguf:blk.4.attn_output.weight, torch.float16 --> Q8_0, shape = {4096, 4096}\n", + "INFO:hf-to-gguf:blk.4.ffn_gate.weight, torch.float16 --> Q8_0, shape = {4096, 14336}\n", + "INFO:hf-to-gguf:blk.4.ffn_up.weight, torch.float16 --> Q8_0, shape = {4096, 14336}\n", + "INFO:hf-to-gguf:blk.4.ffn_down.weight, torch.float16 --> Q8_0, shape = {14336, 4096}\n", + "INFO:hf-to-gguf:blk.4.attn_norm.weight, torch.float16 --> F32, shape = {4096}\n", + "INFO:hf-to-gguf:blk.4.ffn_norm.weight, torch.float16 --> F32, shape = {4096}\n", + "INFO:hf-to-gguf:blk.5.attn_q.weight, torch.float16 --> Q8_0, shape = {4096, 4096}\n", + "INFO:hf-to-gguf:blk.5.attn_k.weight, torch.float16 --> Q8_0, shape = {4096, 1024}\n", + "INFO:hf-to-gguf:blk.5.attn_v.weight, torch.float16 --> Q8_0, shape = {4096, 1024}\n", + "INFO:hf-to-gguf:blk.5.attn_output.weight, torch.float16 --> Q8_0, shape = {4096, 4096}\n", + "INFO:hf-to-gguf:blk.5.ffn_gate.weight, torch.float16 --> Q8_0, shape = {4096, 14336}\n", + "INFO:hf-to-gguf:blk.5.ffn_up.weight, torch.float16 --> Q8_0, shape = {4096, 14336}\n", + "INFO:hf-to-gguf:blk.5.ffn_down.weight, torch.float16 --> Q8_0, shape = {14336, 4096}\n", + "INFO:hf-to-gguf:blk.5.attn_norm.weight, torch.float16 --> F32, shape = {4096}\n", + "INFO:hf-to-gguf:blk.5.ffn_norm.weight, torch.float16 --> F32, shape = {4096}\n", + 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"INFO:hf-to-gguf:blk.7.attn_v.weight, torch.float16 --> Q8_0, shape = {4096, 1024}\n", + "INFO:hf-to-gguf:blk.7.attn_output.weight, torch.float16 --> Q8_0, shape = {4096, 4096}\n", + "INFO:hf-to-gguf:blk.7.ffn_gate.weight, torch.float16 --> Q8_0, shape = {4096, 14336}\n", + "INFO:hf-to-gguf:blk.7.ffn_up.weight, torch.float16 --> Q8_0, shape = {4096, 14336}\n", + "INFO:hf-to-gguf:blk.7.ffn_down.weight, torch.float16 --> Q8_0, shape = {14336, 4096}\n", + "INFO:hf-to-gguf:blk.7.attn_norm.weight, torch.float16 --> F32, shape = {4096}\n", + "INFO:hf-to-gguf:blk.7.ffn_norm.weight, torch.float16 --> F32, shape = {4096}\n", + "INFO:hf-to-gguf:blk.8.attn_q.weight, torch.float16 --> Q8_0, shape = {4096, 4096}\n", + "INFO:hf-to-gguf:blk.8.attn_k.weight, torch.float16 --> Q8_0, shape = {4096, 1024}\n", + "INFO:hf-to-gguf:blk.8.attn_v.weight, torch.float16 --> Q8_0, shape = {4096, 1024}\n", + "INFO:hf-to-gguf:blk.8.attn_output.weight, torch.float16 --> Q8_0, shape = {4096, 4096}\n", + "INFO:hf-to-gguf:blk.8.ffn_gate.weight, torch.float16 --> Q8_0, shape = {4096, 14336}\n", + "INFO:hf-to-gguf:blk.8.ffn_up.weight, torch.float16 --> Q8_0, shape = {4096, 14336}\n", + "INFO:hf-to-gguf:blk.8.ffn_down.weight, torch.float16 --> Q8_0, shape = {14336, 4096}\n", + "INFO:hf-to-gguf:blk.8.attn_norm.weight, torch.float16 --> F32, shape = {4096}\n", + "INFO:hf-to-gguf:blk.8.ffn_norm.weight, torch.float16 --> F32, shape = {4096}\n", + "INFO:hf-to-gguf:gguf: loading model part 'pytorch_model-00002-of-00004.bin'\n", + "INFO:hf-to-gguf:blk.9.attn_q.weight, torch.float16 --> Q8_0, shape = {4096, 4096}\n", + "INFO:hf-to-gguf:blk.9.attn_k.weight, torch.float16 --> Q8_0, shape = {4096, 1024}\n", + "INFO:hf-to-gguf:blk.9.attn_v.weight, torch.float16 --> Q8_0, shape = {4096, 1024}\n", + "INFO:hf-to-gguf:blk.9.attn_output.weight, torch.float16 --> Q8_0, shape = {4096, 4096}\n", + "INFO:hf-to-gguf:blk.9.ffn_gate.weight, torch.float16 --> Q8_0, shape = {4096, 14336}\n", + 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"INFO:hf-to-gguf:blk.10.attn_norm.weight, torch.float16 --> F32, shape = {4096}\n", + "INFO:hf-to-gguf:blk.10.ffn_norm.weight, torch.float16 --> F32, shape = {4096}\n", + "INFO:hf-to-gguf:blk.11.attn_q.weight, torch.float16 --> Q8_0, shape = {4096, 4096}\n", + "INFO:hf-to-gguf:blk.11.attn_k.weight, torch.float16 --> Q8_0, shape = {4096, 1024}\n", + "INFO:hf-to-gguf:blk.11.attn_v.weight, torch.float16 --> Q8_0, shape = {4096, 1024}\n", + "INFO:hf-to-gguf:blk.11.attn_output.weight, torch.float16 --> Q8_0, shape = {4096, 4096}\n", + "INFO:hf-to-gguf:blk.11.ffn_gate.weight, torch.float16 --> Q8_0, shape = {4096, 14336}\n", + "INFO:hf-to-gguf:blk.11.ffn_up.weight, torch.float16 --> Q8_0, shape = {4096, 14336}\n", + "INFO:hf-to-gguf:blk.11.ffn_down.weight, torch.float16 --> Q8_0, shape = {14336, 4096}\n", + "INFO:hf-to-gguf:blk.11.attn_norm.weight, torch.float16 --> F32, shape = {4096}\n", + "INFO:hf-to-gguf:blk.11.ffn_norm.weight, torch.float16 --> F32, shape = {4096}\n", + 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"INFO:hf-to-gguf:blk.16.ffn_norm.weight, torch.float16 --> F32, shape = {4096}\n", + "INFO:hf-to-gguf:blk.17.attn_q.weight, torch.float16 --> Q8_0, shape = {4096, 4096}\n", + "INFO:hf-to-gguf:blk.17.attn_k.weight, torch.float16 --> Q8_0, shape = {4096, 1024}\n", + "INFO:hf-to-gguf:blk.17.attn_v.weight, torch.float16 --> Q8_0, shape = {4096, 1024}\n", + "INFO:hf-to-gguf:blk.17.attn_output.weight, torch.float16 --> Q8_0, shape = {4096, 4096}\n", + "INFO:hf-to-gguf:blk.17.ffn_gate.weight, torch.float16 --> Q8_0, shape = {4096, 14336}\n", + "INFO:hf-to-gguf:blk.17.ffn_up.weight, torch.float16 --> Q8_0, shape = {4096, 14336}\n", + "INFO:hf-to-gguf:blk.17.ffn_down.weight, torch.float16 --> Q8_0, shape = {14336, 4096}\n", + "INFO:hf-to-gguf:blk.17.attn_norm.weight, torch.float16 --> F32, shape = {4096}\n", + "INFO:hf-to-gguf:blk.17.ffn_norm.weight, torch.float16 --> F32, shape = {4096}\n", + "INFO:hf-to-gguf:blk.18.attn_q.weight, torch.float16 --> Q8_0, shape = {4096, 4096}\n", + "INFO:hf-to-gguf:blk.18.attn_k.weight, torch.float16 --> Q8_0, shape = {4096, 1024}\n", + "INFO:hf-to-gguf:blk.18.attn_v.weight, torch.float16 --> Q8_0, shape = {4096, 1024}\n", + "INFO:hf-to-gguf:blk.18.attn_output.weight, torch.float16 --> Q8_0, shape = {4096, 4096}\n", + "INFO:hf-to-gguf:blk.18.ffn_gate.weight, torch.float16 --> Q8_0, shape = {4096, 14336}\n", + "INFO:hf-to-gguf:blk.18.ffn_up.weight, torch.float16 --> Q8_0, shape = {4096, 14336}\n", + "INFO:hf-to-gguf:blk.18.ffn_down.weight, torch.float16 --> Q8_0, shape = {14336, 4096}\n", + "INFO:hf-to-gguf:blk.18.attn_norm.weight, torch.float16 --> F32, shape = {4096}\n", + "INFO:hf-to-gguf:blk.18.ffn_norm.weight, torch.float16 --> F32, shape = {4096}\n", + "INFO:hf-to-gguf:blk.19.attn_q.weight, torch.float16 --> Q8_0, shape = {4096, 4096}\n", + "INFO:hf-to-gguf:blk.19.attn_k.weight, torch.float16 --> Q8_0, shape = {4096, 1024}\n", + "INFO:hf-to-gguf:blk.19.attn_v.weight, torch.float16 --> Q8_0, shape = {4096, 1024}\n", + "INFO:hf-to-gguf:blk.19.attn_output.weight, torch.float16 --> Q8_0, shape = {4096, 4096}\n", + "INFO:hf-to-gguf:blk.19.ffn_gate.weight, torch.float16 --> Q8_0, shape = {4096, 14336}\n", + "INFO:hf-to-gguf:blk.19.ffn_up.weight, torch.float16 --> Q8_0, shape = {4096, 14336}\n", + "INFO:hf-to-gguf:blk.19.ffn_down.weight, torch.float16 --> Q8_0, shape = {14336, 4096}\n", + "INFO:hf-to-gguf:blk.19.attn_norm.weight, torch.float16 --> F32, shape = {4096}\n", + "INFO:hf-to-gguf:blk.19.ffn_norm.weight, torch.float16 --> F32, shape = {4096}\n", + "INFO:hf-to-gguf:blk.20.attn_q.weight, torch.float16 --> Q8_0, shape = {4096, 4096}\n", + "INFO:hf-to-gguf:blk.20.attn_k.weight, torch.float16 --> Q8_0, shape = {4096, 1024}\n", + "INFO:hf-to-gguf:blk.20.attn_v.weight, torch.float16 --> Q8_0, shape = {4096, 1024}\n", + "INFO:hf-to-gguf:blk.20.attn_output.weight, torch.float16 --> Q8_0, shape = {4096, 4096}\n", + "INFO:hf-to-gguf:blk.20.ffn_gate.weight, torch.float16 --> Q8_0, shape = {4096, 14336}\n", + "INFO:hf-to-gguf:gguf: loading model part 'pytorch_model-00003-of-00004.bin'\n", + "INFO:hf-to-gguf:blk.20.ffn_up.weight, torch.float16 --> Q8_0, shape = {4096, 14336}\n", + "INFO:hf-to-gguf:blk.20.ffn_down.weight, torch.float16 --> Q8_0, shape = {14336, 4096}\n", + "INFO:hf-to-gguf:blk.20.attn_norm.weight, torch.float16 --> F32, shape = {4096}\n", + "INFO:hf-to-gguf:blk.20.ffn_norm.weight, torch.float16 --> F32, shape = {4096}\n", + "INFO:hf-to-gguf:blk.21.attn_q.weight, torch.float16 --> Q8_0, shape = {4096, 4096}\n", + "INFO:hf-to-gguf:blk.21.attn_k.weight, torch.float16 --> Q8_0, shape = {4096, 1024}\n", + "INFO:hf-to-gguf:blk.21.attn_v.weight, torch.float16 --> Q8_0, shape = {4096, 1024}\n", + "INFO:hf-to-gguf:blk.21.attn_output.weight, torch.float16 --> Q8_0, shape = {4096, 4096}\n", + "INFO:hf-to-gguf:blk.21.ffn_gate.weight, torch.float16 --> Q8_0, shape = {4096, 14336}\n", + "INFO:hf-to-gguf:blk.21.ffn_up.weight, torch.float16 --> Q8_0, shape = {4096, 14336}\n", + "INFO:hf-to-gguf:blk.21.ffn_down.weight, torch.float16 --> Q8_0, shape = {14336, 4096}\n", + "INFO:hf-to-gguf:blk.21.attn_norm.weight, torch.float16 --> F32, shape = {4096}\n", + "INFO:hf-to-gguf:blk.21.ffn_norm.weight, torch.float16 --> F32, shape = {4096}\n", + "INFO:hf-to-gguf:blk.22.attn_q.weight, torch.float16 --> Q8_0, shape = {4096, 4096}\n", + "INFO:hf-to-gguf:blk.22.attn_k.weight, torch.float16 --> Q8_0, shape = {4096, 1024}\n", + "INFO:hf-to-gguf:blk.22.attn_v.weight, torch.float16 --> Q8_0, shape = {4096, 1024}\n", + "INFO:hf-to-gguf:blk.22.attn_output.weight, torch.float16 --> Q8_0, shape = {4096, 4096}\n", + "INFO:hf-to-gguf:blk.22.ffn_gate.weight, torch.float16 --> Q8_0, shape = {4096, 14336}\n", + "INFO:hf-to-gguf:blk.22.ffn_up.weight, torch.float16 --> Q8_0, shape = {4096, 14336}\n", + "INFO:hf-to-gguf:blk.22.ffn_down.weight, torch.float16 --> Q8_0, shape = {14336, 4096}\n", + "INFO:hf-to-gguf:blk.22.attn_norm.weight, torch.float16 --> F32, shape = {4096}\n", + "INFO:hf-to-gguf:blk.22.ffn_norm.weight, torch.float16 --> F32, shape = {4096}\n", + "INFO:hf-to-gguf:blk.23.attn_q.weight, torch.float16 --> Q8_0, shape = {4096, 4096}\n", + "INFO:hf-to-gguf:blk.23.attn_k.weight, torch.float16 --> Q8_0, shape = {4096, 1024}\n", + "INFO:hf-to-gguf:blk.23.attn_v.weight, torch.float16 --> Q8_0, shape = {4096, 1024}\n", + "INFO:hf-to-gguf:blk.23.attn_output.weight, torch.float16 --> Q8_0, shape = {4096, 4096}\n", + "INFO:hf-to-gguf:blk.23.ffn_gate.weight, torch.float16 --> Q8_0, shape = {4096, 14336}\n", + "INFO:hf-to-gguf:blk.23.ffn_up.weight, torch.float16 --> Q8_0, shape = {4096, 14336}\n", + "INFO:hf-to-gguf:blk.23.ffn_down.weight, torch.float16 --> Q8_0, shape = {14336, 4096}\n", + "INFO:hf-to-gguf:blk.23.attn_norm.weight, torch.float16 --> F32, shape = {4096}\n", + "INFO:hf-to-gguf:blk.23.ffn_norm.weight, torch.float16 --> F32, shape = {4096}\n", + "INFO:hf-to-gguf:blk.24.attn_q.weight, torch.float16 --> Q8_0, shape = {4096, 4096}\n", + "INFO:hf-to-gguf:blk.24.attn_k.weight, torch.float16 --> Q8_0, shape = {4096, 1024}\n", + "INFO:hf-to-gguf:blk.24.attn_v.weight, torch.float16 --> Q8_0, shape = {4096, 1024}\n", + "INFO:hf-to-gguf:blk.24.attn_output.weight, torch.float16 --> Q8_0, shape = {4096, 4096}\n", + "INFO:hf-to-gguf:blk.24.ffn_gate.weight, torch.float16 --> Q8_0, shape = {4096, 14336}\n", + "INFO:hf-to-gguf:blk.24.ffn_up.weight, torch.float16 --> Q8_0, shape = {4096, 14336}\n", + "INFO:hf-to-gguf:blk.24.ffn_down.weight, torch.float16 --> Q8_0, shape = {14336, 4096}\n", + "INFO:hf-to-gguf:blk.24.attn_norm.weight, torch.float16 --> F32, shape = {4096}\n", + "INFO:hf-to-gguf:blk.24.ffn_norm.weight, torch.float16 --> F32, shape = {4096}\n", + "INFO:hf-to-gguf:blk.25.attn_q.weight, torch.float16 --> Q8_0, shape = {4096, 4096}\n", + "INFO:hf-to-gguf:blk.25.attn_k.weight, torch.float16 --> Q8_0, shape = {4096, 1024}\n", + "INFO:hf-to-gguf:blk.25.attn_v.weight, torch.float16 --> Q8_0, shape = {4096, 1024}\n", + "INFO:hf-to-gguf:blk.25.attn_output.weight, torch.float16 --> Q8_0, shape = {4096, 4096}\n", + "INFO:hf-to-gguf:blk.25.ffn_gate.weight, torch.float16 --> Q8_0, shape = {4096, 14336}\n", + "INFO:hf-to-gguf:blk.25.ffn_up.weight, torch.float16 --> Q8_0, shape = {4096, 14336}\n", + "INFO:hf-to-gguf:blk.25.ffn_down.weight, torch.float16 --> Q8_0, shape = {14336, 4096}\n", + "INFO:hf-to-gguf:blk.25.attn_norm.weight, torch.float16 --> F32, shape = {4096}\n", + "INFO:hf-to-gguf:blk.25.ffn_norm.weight, torch.float16 --> F32, shape = {4096}\n", + "INFO:hf-to-gguf:blk.26.attn_q.weight, torch.float16 --> Q8_0, shape = {4096, 4096}\n", + "INFO:hf-to-gguf:blk.26.attn_k.weight, torch.float16 --> Q8_0, shape = {4096, 1024}\n", + "INFO:hf-to-gguf:blk.26.attn_v.weight, torch.float16 --> Q8_0, shape = {4096, 1024}\n", + "INFO:hf-to-gguf:blk.26.attn_output.weight, torch.float16 --> Q8_0, shape = {4096, 4096}\n", + "INFO:hf-to-gguf:blk.26.ffn_gate.weight, torch.float16 --> Q8_0, shape = {4096, 14336}\n", + "INFO:hf-to-gguf:blk.26.ffn_up.weight, torch.float16 --> Q8_0, shape = {4096, 14336}\n", + "INFO:hf-to-gguf:blk.26.ffn_down.weight, torch.float16 --> Q8_0, shape = {14336, 4096}\n", + "INFO:hf-to-gguf:blk.26.attn_norm.weight, torch.float16 --> F32, shape = {4096}\n", + "INFO:hf-to-gguf:blk.26.ffn_norm.weight, torch.float16 --> F32, shape = {4096}\n", + "INFO:hf-to-gguf:blk.27.attn_q.weight, torch.float16 --> Q8_0, shape = {4096, 4096}\n", + "INFO:hf-to-gguf:blk.27.attn_k.weight, torch.float16 --> Q8_0, shape = {4096, 1024}\n", + "INFO:hf-to-gguf:blk.27.attn_v.weight, torch.float16 --> Q8_0, shape = {4096, 1024}\n", + "INFO:hf-to-gguf:blk.27.attn_output.weight, torch.float16 --> Q8_0, shape = {4096, 4096}\n", + "INFO:hf-to-gguf:blk.27.ffn_gate.weight, torch.float16 --> Q8_0, shape = {4096, 14336}\n", + "INFO:hf-to-gguf:blk.27.ffn_up.weight, torch.float16 --> Q8_0, shape = {4096, 14336}\n", + "INFO:hf-to-gguf:blk.27.ffn_down.weight, torch.float16 --> Q8_0, shape = {14336, 4096}\n", + "INFO:hf-to-gguf:blk.27.attn_norm.weight, torch.float16 --> F32, shape = {4096}\n", + "INFO:hf-to-gguf:blk.27.ffn_norm.weight, torch.float16 --> F32, shape = {4096}\n", + "INFO:hf-to-gguf:blk.28.attn_q.weight, torch.float16 --> Q8_0, shape = {4096, 4096}\n", + "INFO:hf-to-gguf:blk.28.attn_k.weight, torch.float16 --> Q8_0, shape = {4096, 1024}\n", + "INFO:hf-to-gguf:blk.28.attn_v.weight, torch.float16 --> Q8_0, shape = {4096, 1024}\n", + "INFO:hf-to-gguf:blk.28.attn_output.weight, torch.float16 --> Q8_0, shape = {4096, 4096}\n", + "INFO:hf-to-gguf:blk.28.ffn_gate.weight, torch.float16 --> Q8_0, shape = {4096, 14336}\n", + "INFO:hf-to-gguf:blk.28.ffn_up.weight, torch.float16 --> Q8_0, shape = {4096, 14336}\n", + "INFO:hf-to-gguf:blk.28.ffn_down.weight, torch.float16 --> Q8_0, shape = {14336, 4096}\n", + "INFO:hf-to-gguf:blk.28.attn_norm.weight, torch.float16 --> F32, shape = {4096}\n", + "INFO:hf-to-gguf:blk.28.ffn_norm.weight, torch.float16 --> F32, shape = {4096}\n", + "INFO:hf-to-gguf:blk.29.attn_q.weight, torch.float16 --> Q8_0, shape = {4096, 4096}\n", + "INFO:hf-to-gguf:blk.29.attn_k.weight, torch.float16 --> Q8_0, shape = {4096, 1024}\n", + "INFO:hf-to-gguf:blk.29.attn_v.weight, torch.float16 --> Q8_0, shape = {4096, 1024}\n", + "INFO:hf-to-gguf:blk.29.attn_output.weight, torch.float16 --> Q8_0, shape = {4096, 4096}\n", + "INFO:hf-to-gguf:blk.29.ffn_gate.weight, torch.float16 --> Q8_0, shape = {4096, 14336}\n", + "INFO:hf-to-gguf:blk.29.ffn_up.weight, torch.float16 --> Q8_0, shape = {4096, 14336}\n", + "INFO:hf-to-gguf:blk.29.ffn_down.weight, torch.float16 --> Q8_0, shape = {14336, 4096}\n", + "INFO:hf-to-gguf:blk.29.attn_norm.weight, torch.float16 --> F32, shape = {4096}\n", + "INFO:hf-to-gguf:blk.29.ffn_norm.weight, torch.float16 --> F32, shape = {4096}\n", + "INFO:hf-to-gguf:blk.30.attn_q.weight, torch.float16 --> Q8_0, shape = {4096, 4096}\n", + "INFO:hf-to-gguf:blk.30.attn_k.weight, torch.float16 --> Q8_0, shape = {4096, 1024}\n", + "INFO:hf-to-gguf:blk.30.attn_v.weight, torch.float16 --> Q8_0, shape = {4096, 1024}\n", + "INFO:hf-to-gguf:blk.30.attn_output.weight, torch.float16 --> Q8_0, shape = {4096, 4096}\n", + "INFO:hf-to-gguf:blk.30.ffn_gate.weight, torch.float16 --> Q8_0, shape = {4096, 14336}\n", + "INFO:hf-to-gguf:blk.30.ffn_up.weight, torch.float16 --> Q8_0, shape = {4096, 14336}\n", + "INFO:hf-to-gguf:blk.30.ffn_down.weight, torch.float16 --> Q8_0, shape = {14336, 4096}\n", + "INFO:hf-to-gguf:blk.30.attn_norm.weight, torch.float16 --> F32, shape = {4096}\n", + "INFO:hf-to-gguf:blk.30.ffn_norm.weight, torch.float16 --> F32, shape = {4096}\n", + "INFO:hf-to-gguf:blk.31.attn_q.weight, torch.float16 --> Q8_0, shape = {4096, 4096}\n", + "INFO:hf-to-gguf:blk.31.attn_k.weight, torch.float16 --> Q8_0, shape = {4096, 1024}\n", + "INFO:hf-to-gguf:blk.31.attn_v.weight, torch.float16 --> Q8_0, shape = {4096, 1024}\n", + "INFO:hf-to-gguf:blk.31.attn_output.weight, torch.float16 --> Q8_0, shape = {4096, 4096}\n", + "INFO:hf-to-gguf:blk.31.ffn_gate.weight, torch.float16 --> Q8_0, shape = {4096, 14336}\n", + "INFO:hf-to-gguf:blk.31.ffn_up.weight, torch.float16 --> Q8_0, shape = {4096, 14336}\n", + "INFO:hf-to-gguf:gguf: loading model part 'pytorch_model-00004-of-00004.bin'\n", + "INFO:hf-to-gguf:blk.31.ffn_down.weight, torch.float16 --> Q8_0, shape = {14336, 4096}\n", + "INFO:hf-to-gguf:blk.31.attn_norm.weight, torch.float16 --> F32, shape = {4096}\n", + "INFO:hf-to-gguf:blk.31.ffn_norm.weight, torch.float16 --> F32, shape = {4096}\n", + "INFO:hf-to-gguf:output_norm.weight, torch.float16 --> F32, shape = {4096}\n", + "INFO:hf-to-gguf:output.weight, torch.float16 --> Q8_0, shape = {4096, 128256}\n", + "INFO:hf-to-gguf:Set meta model\n", + "INFO:hf-to-gguf:Set model parameters\n", + "INFO:hf-to-gguf:gguf: context length = 8192\n", + "INFO:hf-to-gguf:gguf: embedding length = 4096\n", + "INFO:hf-to-gguf:gguf: feed forward length = 14336\n", + "INFO:hf-to-gguf:gguf: head count = 32\n", + "INFO:hf-to-gguf:gguf: key-value head count = 8\n", + "INFO:hf-to-gguf:gguf: rope theta = 500000.0\n", + "INFO:hf-to-gguf:gguf: rms norm epsilon = 1e-05\n", + "INFO:hf-to-gguf:gguf: file type = 7\n", + "INFO:hf-to-gguf:Set model tokenizer\n", + "INFO:gguf.vocab:Adding 280147 merge(s).\n", + "INFO:gguf.vocab:Setting special token type bos to 128000\n", + "INFO:gguf.vocab:Setting special token type eos to 128001\n", + "INFO:gguf.vocab:Setting special token type pad to 128255\n", + "INFO:gguf.vocab:Setting chat_template to {{ 'Below are some instructions that describe some tasks. Write responses that appropriately complete each request.' }}{% for message in messages %}{% if message['role'] == 'user' %}{{ '\n", + "\n", + "### Instruction:\n", + "' + message['content'] }}{% elif message['role'] == 'assistant' %}{{ '\n", + "\n", + "### Response:\n", + "' + message['content'] + '<|end_of_text|>' }}{% else %}{{ raise_exception('Only user and assistant roles are supported!') }}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ '\n", + "\n", + "### Response:\n", + "' }}{% endif %}\n", + "INFO:hf-to-gguf:Set model quantization version\n", + "INFO:gguf.gguf_writer:Writing the following files:\n", + "INFO:gguf.gguf_writer:model/unsloth.Q8_0.gguf: n_tensors = 291, total_size = 8.5G\n", + "Writing: 100%|██████████| 8.53G/8.53G [03:05<00:00, 46.1Mbyte/s]\n", + "INFO:hf-to-gguf:Model successfully exported to model/unsloth.Q8_0.gguf\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Unsloth: ##### The current model auto adds a BOS token.\n", + "Unsloth: ##### We removed it in GGUF's chat template for you.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Unsloth: Conversion completed! Output location: ./model/unsloth.Q8_0.gguf\n", + "Unsloth: Saved Ollama Modelfile to model/Modelfile\n" + ] + } + ], + "source": [ + "# Save to 8bit Q8_0\n", + "if True: model.save_pretrained_gguf(\"model\", tokenizer,)\n", + "# Remember to go to https://huggingface.co/settings/tokens for a token!\n", + "# And change hf to your username!\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 = \"\")\n", + "\n", + "# Save to multiple GGUF options - much faster if you want multiple!\n", + "if False:\n", + " model.push_to_hub_gguf(\n", + " \"hf/model\", # Change hf to your username!\n", + " tokenizer,\n", + " quantization_method = [\"q4_k_m\", \"q8_0\", \"q5_k_m\",],\n", + " token = \"\", # Get a token at https://huggingface.co/settings/tokens\n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "J7lk6l0CuPXS" + }, + "source": [ + "We use `subprocess` to start `Ollama` up in a non blocking fashion! In your own desktop, you can simply open up a new `terminal` and type `ollama serve`, but in Colab, we have to use this hack!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "mcP9omF_tN7Q" + }, + "outputs": [], + "source": [ + "import subprocess\n", + "\n", + "subprocess.Popen([\"ollama\", \"serve\"])\n", + "import time\n", + "\n", + "time.sleep(3) # Wait for a few seconds for Ollama to load!" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "md3PExRLRhOc" + }, + "source": [ + "`Ollama` needs a `Modelfile`, which specifies the model's prompt format. Let's print Unsloth's auto generated one:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "h82vfNigRhiz", + "outputId": "bcd91437-d4cf-47de-8905-475e3fc4deec" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FROM {__FILE_LOCATION__}\n", + "\n", + "TEMPLATE \"\"\"Below are some instructions that describe some tasks. Write responses that appropriately complete each request.{{ if .Prompt }}\n", + "\n", + "### Instruction:\n", + "{{ .Prompt }}{{ end }}\n", + "\n", + "### Response:\n", + "{{ .Response }}<|end_of_text|>\"\"\"\n", + "\n", + "PARAMETER stop \"<|eot_id|>\"\n", + "PARAMETER stop \"<|start_header_id|>\"\n", + "PARAMETER stop \"<|end_header_id|>\"\n", + "PARAMETER stop \"<|end_of_text|>\"\n", + "PARAMETER stop \"<|reserved_special_token_\"\n", + "PARAMETER temperature 1.5\n", + "PARAMETER min_p 0.1\n" + ] + } + ], + "source": [ + "print(tokenizer._ollama_modelfile)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "j6cipBJBudxv" + }, + "source": [ + "We now will create an `Ollama` model called `unsloth_model` using the `Modelfile` which we auto generated!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "SDTUJv_QiaVh", + "outputId": "66fcae42-3792-4b52-eb42-d867d9f83d69" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[?25ltransferring model data ⠋ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1Gtransferring model data ⠹ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1Gtransferring model data ⠹ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1Gtransferring model data ⠸ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1Gtransferring model data ⠼ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1Gtransferring model data ⠴ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1Gtransferring model data ⠧ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1Gtransferring model data ⠇ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1Gtransferring model data ⠏ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1Gtransferring model data ⠋ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1Gtransferring model data ⠋ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1Gtransferring model data ⠙ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1Gtransferring model data ⠸ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1Gtransferring model data ⠼ 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\u001b[?25h\u001b[?25l\u001b[2K\u001b[1Gtransferring model data ⠋ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1Gtransferring model data ⠋ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1Gtransferring model data ⠙ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1Gtransferring model data ⠹ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1Gtransferring model data ⠸ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1Gtransferring model data \n", + "creating new layer sha256:94728011329d3d304c40e235f81f1b75580e163036c07d98382dc5548d555a34 \n", + "creating new layer sha256:95b5361453780fb5797ce5abfe9a330f5d33fdec13d2232ef1443ee0c3a86ecc \n", + "creating new layer sha256:57675488fe3dd2a75da06ae97984c4ce6f382208e9d989c584b22ee395bab0d8 \n", + "creating new layer sha256:e706dd26476841ded603017f70f5b99b5be356caa859878787bfc3898d547f08 \n", + "writing manifest \n", + "success \u001b[?25h\n" + ] + } + ], + "source": [ + "!ollama create unsloth_model -f ./model/Modelfile" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "-KSoKTKQukba" + }, + "source": [ + "And now we can do inference on it via `Ollama`!\n", + "\n", + "You can also upload to `Ollama` and try the `Ollama` Desktop app by heading to https://www.ollama.com/" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "rkp0uMrNpYaW", + "outputId": "38bb3bd7-4a29-4c81-e319-388dcd96a449" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{\"model\":\"unsloth_model\",\"created_at\":\"2024-10-01T06:47:04.241326628Z\",\"message\":{\"role\":\"assistant\",\"content\":\"The\"},\"done\":false}\n", + "{\"model\":\"unsloth_model\",\"created_at\":\"2024-10-01T06:47:04.465575479Z\",\"message\":{\"role\":\"assistant\",\"content\":\" next\"},\"done\":false}\n", + "{\"model\":\"unsloth_model\",\"created_at\":\"2024-10-01T06:47:04.760101468Z\",\"message\":{\"role\":\"assistant\",\"content\":\" number\"},\"done\":false}\n", + "{\"model\":\"unsloth_model\",\"created_at\":\"2024-10-01T06:47:05.051240606Z\",\"message\":{\"role\":\"assistant\",\"content\":\" in\"},\"done\":false}\n", + "{\"model\":\"unsloth_model\",\"created_at\":\"2024-10-01T06:47:05.376545126Z\",\"message\":{\"role\":\"assistant\",\"content\":\" the\"},\"done\":false}\n", + "{\"model\":\"unsloth_model\",\"created_at\":\"2024-10-01T06:47:05.515751946Z\",\"message\":{\"role\":\"assistant\",\"content\":\" Fibonacci\"},\"done\":false}\n", + "{\"model\":\"unsloth_model\",\"created_at\":\"2024-10-01T06:47:05.658721744Z\",\"message\":{\"role\":\"assistant\",\"content\":\" sequence\"},\"done\":false}\n", + "{\"model\":\"unsloth_model\",\"created_at\":\"2024-10-01T06:47:05.795226527Z\",\"message\":{\"role\":\"assistant\",\"content\":\" after\"},\"done\":false}\n", + "{\"model\":\"unsloth_model\",\"created_at\":\"2024-10-01T06:47:05.923676364Z\",\"message\":{\"role\":\"assistant\",\"content\":\" \"},\"done\":false}\n", + "{\"model\":\"unsloth_model\",\"created_at\":\"2024-10-01T06:47:06.053599585Z\",\"message\":{\"role\":\"assistant\",\"content\":\"8\"},\"done\":false}\n", + "{\"model\":\"unsloth_model\",\"created_at\":\"2024-10-01T06:47:06.187220374Z\",\"message\":{\"role\":\"assistant\",\"content\":\" is\"},\"done\":false}\n", + "{\"model\":\"unsloth_model\",\"created_at\":\"2024-10-01T06:47:06.316237671Z\",\"message\":{\"role\":\"assistant\",\"content\":\" \"},\"done\":false}\n", + "{\"model\":\"unsloth_model\",\"created_at\":\"2024-10-01T06:47:06.448901764Z\",\"message\":{\"role\":\"assistant\",\"content\":\"13\"},\"done\":false}\n", + "{\"model\":\"unsloth_model\",\"created_at\":\"2024-10-01T06:47:06.585864644Z\",\"message\":{\"role\":\"assistant\",\"content\":\" (\"},\"done\":false}\n", + "{\"model\":\"unsloth_model\",\"created_at\":\"2024-10-01T06:47:06.712030586Z\",\"message\":{\"role\":\"assistant\",\"content\":\"the\"},\"done\":false}\n", + "{\"model\":\"unsloth_model\",\"created_at\":\"2024-10-01T06:47:06.835728964Z\",\"message\":{\"role\":\"assistant\",\"content\":\" sum\"},\"done\":false}\n", + "{\"model\":\"unsloth_model\",\"created_at\":\"2024-10-01T06:47:06.962898827Z\",\"message\":{\"role\":\"assistant\",\"content\":\" of\"},\"done\":false}\n", + "{\"model\":\"unsloth_model\",\"created_at\":\"2024-10-01T06:47:07.088064406Z\",\"message\":{\"role\":\"assistant\",\"content\":\" the\"},\"done\":false}\n", + "{\"model\":\"unsloth_model\",\"created_at\":\"2024-10-01T06:47:07.212942126Z\",\"message\":{\"role\":\"assistant\",\"content\":\" previous\"},\"done\":false}\n", + "{\"model\":\"unsloth_model\",\"created_at\":\"2024-10-01T06:47:07.336569966Z\",\"message\":{\"role\":\"assistant\",\"content\":\" two\"},\"done\":false}\n", + "{\"model\":\"unsloth_model\",\"created_at\":\"2024-10-01T06:47:07.46094096Z\",\"message\":{\"role\":\"assistant\",\"content\":\" numbers\"},\"done\":false}\n", + "{\"model\":\"unsloth_model\",\"created_at\":\"2024-10-01T06:47:07.593857726Z\",\"message\":{\"role\":\"assistant\",\"content\":\").\"},\"done\":false}\n", + "{\"model\":\"unsloth_model\",\"created_at\":\"2024-10-01T06:47:07.741203726Z\",\"message\":{\"role\":\"assistant\",\"content\":\"\"},\"done_reason\":\"stop\",\"done\":true,\"total_duration\":3741960321,\"load_duration\":48967410,\"prompt_eval_count\":47,\"prompt_eval_duration\":150430000,\"eval_count\":23,\"eval_duration\":3499634000}\n" + ] + } + ], + "source": [ + "!curl http://localhost:11434/api/chat -d '{ \\\n", + " \"model\": \"unsloth_model\", \\\n", + " \"messages\": [ \\\n", + " { \"role\": \"user\", \"content\": \"Continue the Fibonacci sequence: 1, 1, 2, 3, 5, 8,\" } \\\n", + " ] \\\n", + " }'" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "XnMbhp7KsKhr" + }, + "source": [ + "# ChatGPT interactive mode\n", + "\n", + "### ⭐ To run the finetuned model like in a ChatGPT style interface, first click the **| >_ |** button.\n", + "![](https://raw.githubusercontent.com/unslothai/unsloth/nightly/images/Where_Terminal.png)\n", + "\n", + "---\n", + "---\n", + "---\n", + "\n", + "### ⭐ Then, type `ollama run unsloth_model`\n", + "\n", + "![](https://raw.githubusercontent.com/unslothai/unsloth/nightly/images/Terminal_Type.png)\n", + "\n", + "---\n", + "---\n", + "---\n", + "### ⭐ And you have a ChatGPT style assistant!\n", + "\n", + "### Type any question you like and press `ENTER`. If you want to exit, hit `CTRL + D`\n", + "![](https://raw.githubusercontent.com/unslothai/unsloth/nightly/images/Assistant.png)You can also use the `model-unsloth.gguf` file or `model-unsloth-Q4_K_M.gguf` file in llama.cpp or a UI based system like Jan or Open WebUI. You can install Jan [here](https://github.com/janhq/jan) and Open WebUI [here](https://github.com/open-webui/open-webui)\n", + "\n", + "And we're done! If you have any questions on Unsloth, we have a [Discord](https://discord.gg/unsloth) 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. Llama 3.2 Conversational notebook. [Free Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.2_(1B_and_3B)-Conversational.ipynb)\n", + "2. Saving finetunes to Ollama. [Free notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3_(8B)-Ollama.ipynb)\n", + "3. Llama 3.2 Vision finetuning - Radiography use case. [Free Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.2_(11B)-Vision.ipynb)\n", + "6. See notebooks for DPO, ORPO, Continued pretraining, conversational finetuning and more on our [documentation](https://docs.unsloth.ai/get-started/unsloth-notebooks)!\n", + "\n", + "

\n", + " \n", + " \n", + " \n", + "\n", + " Join Discord if you need help + ⭐️ Star us on Github ⭐️\n", + "
\n" + ] + } + ], + "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": { + "6f98440a4d6a4424b3ea32292bf8d990": { + "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_d837757e66e84e118e08f851c208c37f", + "IPY_MODEL_f4cc0012278c428da179462312d061f3", + "IPY_MODEL_e241510e026641d79b423315774beab5" + ], + "layout": "IPY_MODEL_0447b59db66f45e58f394686a25b510a" + } + }, + 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