{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "IqM-T1RTzY6C" }, "source": [ "To run this, press \"*Runtime*\" and press \"*Run all*\" on a **free** Tesla T4 Google Colab instance!\n", "
\n", " \n", " \n", " Join Discord if you need help + ⭐ Star us on Github ⭐\n", "
\n", "\n", "To install Unsloth on your own computer, follow the installation instructions on our Github page [here](https://github.com/unslothai/unsloth?tab=readme-ov-file#-installation-instructions).\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)." ] }, { "cell_type": "code", "execution_count": null, "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." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 301, "referenced_widgets": [ "7862ae87572d44fe989c0af0bc298988", "3bff0ec06ac74c7aab5fb9f2e1be1b75", "350c53a02d5246dda926f786bf7d6d8c", "338819c2b08347889688289d2f50edf6", "5d1c1ad2d244405b8d487b0f6ce9effb", "0032e040955048ff955ce9fd5592e736", "674a029ab575479595b4c6d45496f917", "d593be7c0749468eb576668e00a24068", "6310d7ab4b4a41c1bc54d7b3bae3a180", "161dd813c8304711a37246b887568c1e", "45e4686902e44075be1a5634db2314fc", "d9408052934741df94d9d663574356d5", "c013e7cd8afa4d2188747c6c377ff782", "2fc1f315bb14475eadde8fb46aee5254", "1e6072dd7a584265a3bd5e3ebc44ac86", "884cbaaf8a4a42e296b3a981161d434a", "70e6cd81477449989e0af5cbc548bf42", "d10ccb8b91d944929c4a67b8112b2fe4", "cca81dcac05a47e69042e3a3f41c2f73", "dcc98889af594041afa8dd7fe964b71c", "a1c95951ad4944deb1ac8b1fb045c526", "6865db1f812d4a5c970f053bee9d8bc3", "33c0e87302014b90bd0486a3637eb746", "7cbac02ae7c747c2bfa40acf5e3d490b", "929d0d881fa74f858a62c7ec06a792e6", "cb9d6c37279e46c886cd186f880c9c0c", "a910c6e3e1fc41718c864ec3e011c55a", "196d5655eda44112ae65798efc353f1f", "08217fa8c13e4cbd8e25ce899aeffee0", "c4933379c649415a9fe2948efcc2262f", "86a2525c57324601abdfe133bdf2f265", "7b2ae51299ac4e33b74e50d973502562", "aac48db4d98e44919244b790b95092d3", "e86fcc970c564443ad5c94eae54d541d", "955da52fa5684b8899e24f66ffb4acd7", "3f4e3a7b65d6457381aac3fae0dcb20f", "100cf5530daa4fab87574bba5e2f8db2", "72431820635e4d5586eab9246683b1a4", "61db8bfa77144b6ca3001680888f8212", "4db68379b9ab4a4aa67de1a60cb14326", "75feddc6ba2d4855b86011b5c248dda0", "31b6f58f12774d89819d801ea8e7b9a1", "4a030f99d94e4c0fabb59c24bfd147a8", "5a1501830dd94bc3a93710c2e34c6f4b", "a36e10a102b6451da556ca2f746da5ea", "ad697f5145154a3e9c331f081325f3a2", "df990d74204946a58aa5c85a5a4f56a5", "e4ba7135719a4410bc6975a1211f6a7c", "69d534af89c04acaa2c53d480bbee60f", "37306d5fe4fa4462ada270182ccb2b62", "6a3fc8fec4ca41fd81e4c34f73b749e8", "da946901d8de4c58acd95aab4e60669b", "ae76bec19e064408986ea336efbee017", "dd1fa26f880748f1b4399978f09c3cf3", "2a8ab192ffef4d2fb526f2e2d5b003a9" ] }, "id": "QmUBVEnvCDJv", "outputId": "5b923d74-7d6d-442c-c73e-33f0021b19b6" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "🦥 Unsloth: Will patch your computer to enable 2x faster free finetuning.\n", "==((====))== Unsloth 2024.8: Fast Llama patching. Transformers = 4.44.2.\n", " \\\\ /| GPU: Tesla T4. Max memory: 14.748 GB. Platform = Linux.\n", "O^O/ \\_/ \\ Pytorch: 2.4.0+cu121. CUDA = 7.5. CUDA Toolkit = 12.1.\n", "\\ / Bfloat16 = FALSE. FA [Xformers = 0.0.27.post2. 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 [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": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 145, "referenced_widgets": [ "52df8cb2c21c4cd488c4c0d1eb7299dc", "49ab4a4ed96044d598c81d82168c1899", "f6827597fc144fbda172e5b365d59370", "65c881eec138491db8c2d7c05883de39", "f36cf6ee8eb94d95b54070b1e3623b90", "5ea626c327b84a279a16cab02c13c716", "68b929a10b4c4ed289db3a60744b340f", "32d2d3b3dcdc49e2bec9107d18c5aab1", "d025d109e37747b9889e836c5d3fc367", "f5e74db4aeab435e9f0e3f730058474f", "349b6b2537f14e7e813dc617ea1b3d23", "f9f3183e4ebf4f69b2e06dcb664b6088", "50edc5ebe52e4cd0bb089bdc68bcbc1e", "cc351a8cc34644d094d070bb5272bdc9", "39209a742cb64408bd0b3bb93ef72f63", "58af8049059f442290d7c6e5973d83a0", "bbd6919cefe6471c82eeb53551f5c7ce", "03dc58fe841b4b09b46246a49862dec7", "33054d76a8664acc8b13eaaeb488b9ce", "fc955444876f4ead891753ff8c39af30", "ab7b7fd111d84f7486c741f8a0dff37e", "7d2b6a8a6956454e9ab71ad8f015d876", "928911af56384c4888fe28ac78e41485", "9cf23b1c834f4758b7d8525838bf31b6", "7cc25a473eb9468ea3049497e71acd3a", "a96a4daad9d4400fbdbdbec660f568ee", "385885d66ffd49c7ad0f5eb121909867", "b59cc363c41d4a058821e15a53c29217", "86a28f48f7364273b30ac627d57a2cc7", "fbfa895167b447139bf0676d2e1628dd", "03a637788d504de98bff8d398772d692", "1b11b3dce17a4bb592124a120b586161", "e0ad47ab4e0e4680b8818c4a53b10578", "02b19171e211489493ce3f2bbcbaa384", "05d1c6a1f6f24a0c8d08a84468d791e3", "d4a370fdc9824ebd9600b2f1475cae8d", "dae998bea106433aa3a46163bd4a76da", "5730bcde3c9d42b385402fcc75e1c54a", "7c1f887bdd9e4639bd644c64e40fad99", "c1de02827a8e4d5a946dfb16550a1388", "82aad18c9a6c46feb5c8a1bbb8ea9162", "3d1afa8b51fe471cb9d884a0d8b18265", "1c53493bf34841c98817f5d2c6dc2f11", "5c122e2cda794f0f8abd85f9f074d7ce" ] }, "id": "LjY75GoYUCB8", "outputId": "7a7eab79-7d3b-4453-a6c4-761085f048ee" }, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "Downloading readme: 0%| | 0.00/11.6k [00:00\n", "### Train the model\n", "Now let's use Huggingface TRL's `SFTTrainer`! More docs here: [TRL SFT docs](https://huggingface.co/docs/trl/sft_trainer). We do 60 steps to speed things up, but you can set `num_train_epochs=1` for a full run, and turn off `max_steps=None`. We also support TRL's `DPOTrainer`!" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 67, "referenced_widgets": [ "cb9c107f897f4cc6b0d3435d7ec6ef2c", "18ba44e5da9546e19b8960ba6f4454d4", "ac7ded552cde490ea12cd7e5f25bcd2a", "c682b7c312294af6a84544e3967904ee", "ffcfc1a1bdd14c83b360fefdf6296d71", "cdd56091ddf94285b17f2c245fc7ba2f", "04679d317723427e80f612aa06bd2be2", "1ed581366a364c42bce8eb3b86a7df00", "8a90191a49d3459eb97a04435c4702d2", "dc88454b006d465ba6d6258045edd71a", "95cec420703b4567943b209ddcd99e33" ] }, "id": "95_Nn-89DhsL", "outputId": "8097a019-a80b-4087-cb1a-bbcf0334c233" }, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "Map (num_proc=2): 0%| | 0/51760 [00:00" ], "text/html": [ "\n", "
\n", " \n", " \n", " [60/60 07:22, Epoch 0/1]\n", "
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StepTraining Loss
11.817600
22.304200
31.689300
41.938200
51.656900
61.621900
71.187100
81.264200
91.101200
101.189500
110.930800
120.959400
130.929400
141.048700
150.892800
160.901400
171.009100
181.256100
191.016500
200.882600
210.940500
221.018500
230.897200
240.991900
251.072000
261.022900
271.044900
280.877800
290.843800
300.887500
310.853400
320.866000
330.983200
340.852200
350.961200
360.856700
370.872300
380.751100
391.081400
401.174400
410.893400
420.977500
430.957100
440.908100
450.915000
460.973400
470.870900
481.196500
490.907500
501.031300
511.015900
520.907900
530.977000
541.154300
550.778000
561.013300
570.886800
580.827500
590.852300
600.896600

" ] }, "metadata": {} } ], "source": [ "trainer_stats = trainer.train()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "cellView": "form", "colab": { "base_uri": "https://localhost:8080/" }, "id": "pCqnaKmlO1U9", "outputId": "9730caa0-2498-4a9c-fb6a-0bd1e3ee78a2" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "456.3689 seconds used for training.\n", "7.61 minutes used for training.\n", "Peak reserved memory = 7.922 GB.\n", "Peak reserved memory for training = 1.938 GB.\n", "Peak reserved memory % of max memory = 53.716 %.\n", "Peak reserved memory for training % of max memory = 13.141 %.\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!\n" ] }, { "cell_type": "markdown", "metadata": { "id": "CrSvZObor0lY" }, "source": [ " 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": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "e2pEuRb1r2Vg", "outputId": "4352b6c1-5c66-42b6-d778-551138fc12d3" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "<|begin_of_text|>Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n", "\n", "### Instruction:\n", "Continue the fibonnaci sequence.\n", "\n", "### Input:\n", "1, 1, 2, 3, 5, 8\n", "\n", "### Response:\n", "13, 21, 34, 55, 89, 144<|end_of_text|>\n" ] } ], "source": [ "# alpaca_prompt = Copied from above\n", "FastLanguageModel.for_inference(model) # Enable native 2x faster inference\n", "inputs = tokenizer(\n", "[\n", " alpaca_prompt.format(\n", " \"Determine if the input is a question related to chemical science. If it is, return a JSON object with the 'valid' field set to true. If it is not, return a JSON object with the 'valid' field set to false, and the 'reason' field set to the type of question or statement it is.\", # instruction\n", " \"Was Donald Trump a good president?\", # 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": "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.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "FqfebeAdT073" }, "outputs": [], "source": [ "# Save to multiple GGUF options - much faster if you want multiple!\n", "if True:\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": "bDp0zNpwe6U_" }, "source": [ "Now, use the `model-unsloth.gguf` file or `model-unsloth-Q4_K_M.gguf` file in [LlamaEdge](https://github.com/LlamaEdge/LlamaEdge) or [Gaia network](https://github.com/GaiaNet-AI/gaianet-node).\n", "\n", "**Note:** If you are [running a Gaia node](https://docs.gaianet.ai/node-guide/quick-start), you simply need to change the `chat` field in the `~/gaianet/config.json` file to point to the GGUF file for the new model. [Learn more](https://docs.gaianet.ai/node-guide/customize#select-an-llm) on how to select a new LLM for your Gaia node." ] }, { "cell_type": "markdown", "metadata": { "id": "Zt9CHJqO6p30" }, "source": [ "And we're done! If you have any questions on Unsloth, we have a [Discord](https://discord.gg/u54VK8m8tk) channel! If you find any bugs or want to keep updated with the latest LLM stuff, or need help, join projects etc, feel free to join our Discord!\n", "\n", "

\n", " \n", " \n", " Support our work if you can! Thanks!\n", "
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