{ "cells": [ { "cell_type": "markdown", "source": [ "To run this, press \"*Runtime*\" and press \"*Run all*\" on a **free** Tesla T4 Google Colab instance!\n", "
\n", " \n", " \n", " Join Discord if you need help + support us if you can!\n", "
\n", "\n", "To install Unsloth on your own computer, follow the installation instructions on our Github page [here](https://github.com/unslothai/unsloth#installation-instructions---conda).\n", "\n", "You will learn how to do [data prep](#Data), how to [train](#Train), how to [run the model](#Inference), & [how to save it](#Save) (eg for Llama.cpp).\n", "\n", "See on our [blog post](https://unsloth.ai/blog/gemma) on how we made **Gemma 7b 2.5x faster** and **Gemma 2b 2x faster**!" ], "metadata": { "id": "IqM-T1RTzY6C" } }, { "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\n", "# Get latest Unsloth\n", "!pip uninstall unsloth -y && pip install --upgrade --no-cache-dir \"unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git\"" ] }, { "cell_type": "markdown", "source": [ "* We support Llama, Mistral, CodeLlama, TinyLlama, Vicuna, Open Hermes etc\n", "* And Yi, Qwen ([llamafied](https://huggingface.co/models?sort=trending&search=qwen+llama)), Deepseek, all Llama, Mistral derived archs.\n", "* We support 16bit LoRA or 4bit QLoRA. Both 2x faster.\n", "* `max_seq_length` can be set to anything, since we do automatic RoPE Scaling via [kaiokendev's](https://kaiokendev.github.io/til) method.\n", "* [**NEW**] With [PR 26037](https://github.com/huggingface/transformers/pull/26037), we support downloading 4bit models **4x faster**! [Our repo](https://huggingface.co/unsloth) has Llama, Mistral 4bit models." ], "metadata": { "id": "r2v_X2fA0Df5" } }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 385, "referenced_widgets": [ "1f9b5284d7ac40f0beae41acfd6c7f62", "78be9340df944dbb8fe857d641e096ea", "63861061417c453da4873d76bf6a4e55", "6e2cb14dd0cf4ff381915084d1fe92e1", "d86db4fd81c94a8c9c48d46e3820488c", "f1ec0cc6439845f3be6824505f14fc70", "cbd1a312a9264b7ba0d4b6b9e1ebb10b", "6aabab17366044eaa980bbadc48f4f2f", "fd739012822541c9b96bb46cb95b23ac", "e6601fbc3bc743d487a184c2ab375fa9", "37145878d083460f8fff02127f34cf41", "5242a17835764e9285a183e0fdd182ac", "00291b5bdbde4f6698f00e3a69297943", "59a25cb526d44a5a940e202152bed008", "bda5e969cb9e425aaf12192d002cb504", "d0a11e0b5c4c469ebac3796b8394d693", "6954e969d6ba42309cc3062edb097024", "76a4ce00e0f243a9ab98b593a9ebc243", "2f2e08e690e04b44a9e96597a63915f6", "ec91b7a9907d41159aac8aaf29079e28", "09bbe6b538eb4c8eb120d8757c685e80", "7263f7eef98d454faa6bc7adf3ca0aea", "84cddcbfd3b7483287a9e450d0128114", "e39907e22e7e4507b567501c14c7a436", "2b12695f13a7418d863f413d7c735c0e", "ee1050424f8845bab3723f118504e8c6", "fb72b76a165843a289aa56f9dcc5ceb0", "0ec6d0df052944209e596ecb1bba0598", "c90a5c749c3645ad8100c405a347a512", "52ca1ed1b4b34d67ae68d4a5b1776aab", "27892941bbb54ad9b9d620a5cebc5939", "96eba3df6fc34338b8ac0d2d7deb4393", "76741f9f11064a16a3ea4882af0cf25d", "d0776e74927b4819bc87eca981b3a7b8", "e02f182cda3d4d68b0daa5c4ae7674ac", "be61b42787704da1911367fe368d99c1", "00b21c791faf4e5d9370ccfc5838147f", "958e7b7857d64b5cab5923e4440fd094", "bcdf494a4d52411092d26287fddc5446", "02c83fef850749c283a43b98bc2d499a", "5678f0c46753468ab21ba43641316df3", "555ca3185a084cae9baa996b45e848c7", "c27165aa15204124b6d22e4fa1a683b5", "c834a08b82b942d0a5e45315852b3301", "3ed82a0a904c4626be80ab5e4de94f98", "4bab62c35d33434ebf0d711f65f9c1be", "ac6a39ddea724aa3854e9565ce59adf9", "91e8c483b92a4197887c9a30bfd60501", "f0c6d5209b0d424bb24527060526ce19", "8efd2af34e5c4f439012718545ff756f", "65ea2f319a304a148bc8db50dd0ff99d", "b0358483e49342818785aa40ed0280e0", "8f52986a2a714b2a93d1ef52d7687689", "095ba563cbf040acb1ca84e951c20c86", "bf29a0e5f7b3435692099559528e4a54", "b580f55bfcb44079befd68c85e25cb0b", "083d919afa8f473490b42debf842e201", "a548789e485446a9a41aed8d7cd89779", "a3f3a55c309d4f9db7d5107e4e29da26", "ed3475930ea746dcb5d57f2e5d6864d7", "b80e89c062cb42d187473222dcca3062", "f26cfb3119774c1aa18b4f1957dd721f", "8ac34ce28c064b66b84898fb8817a0e9", "615850f111fa4fbea0dadfeedbe0a3c8", "f2c27d6099234b36a684dbed6585d5be", "13b9aaf1122d45fdacda3a538931aad5", "b21a5d8b0d0940a38eab0a0d466cb64e", "6742d77b371d4bfdbe7bc34e751740c0", "570dfcd6d1c94d59b86fddb038ea4c7d", "aabfa8e68a31404883c66b0fd6d67883", "751f91346de64ac89ecf1e8af8f7bfd3", "b19bca82ff0c46378802ce8e6c1a3757", "a2d03fca2bb5496cb3d4b24d950313b2", "61e745eb141148f0879f9468450fde60", "790dd907de0a41378ef5dfa3142ac0d4", "b312441a69fe466f8089b4d1d27e3247", "6b014e76b05c4e74a60193a7663d0282" ] }, "id": "QmUBVEnvCDJv", "outputId": "66693dbb-5f95-4394-d30d-1a99aea504f2" }, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "config.json: 0%| | 0.00/1.11k [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", " use_gradient_checkpointing = True,\n", " random_state = 3407,\n", " use_rslora = False, # We support rank stabilized LoRA\n", " loftq_config = None, # And LoftQ\n", ")" ] }, { "cell_type": "markdown", "source": [ "\n", "### Data Prep\n", "We now use the Alpaca dataset from [yahma](https://huggingface.co/datasets/yahma/alpaca-cleaned), which is a filtered version of 52K of the original [Alpaca dataset](https://crfm.stanford.edu/2023/03/13/alpaca.html). You can replace this code section with your own data prep.\n", "\n", "**[NOTE]** To train only on completions (ignoring the user's input) read TRL's docs [here](https://huggingface.co/docs/trl/sft_trainer#train-on-completions-only).\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 `ChatML` template for ShareGPT datasets, try our conversational [notebook](https://colab.research.google.com/drive/1Aau3lgPzeZKQ-98h69CCu1UJcvIBLmy2?usp=sharing).\n", "\n", "For text completions like novel writing, try this [notebook](https://colab.research.google.com/drive/1ef-tab5bhkvWmBOObepl1WgJvfvSzn5Q?usp=sharing)." ], "metadata": { "id": "vITh0KVJ10qX" } }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "LjY75GoYUCB8", "colab": { "base_uri": "https://localhost:8080/", "height": 145, "referenced_widgets": [ "fc06e86eb977492e843157bcbe5e4884", "b0677fbb5aeb4b3a9c16f03c6caa15ee", "077e7ddaf98b468aa5110b43295ca455", "339122ace10f440c95990a6ead8ea74b", "0a829eb7d22c4a99a8b1645e045aafe0", "6c3941826efb43f3a2c664b9dc7124a9", "22e28a93b43a494fa113aeb1b276260d", "2af0967a93d84c9c9390aae966880542", "c96a3ba973a24ed2a6ec703255e1b869", "4c679a1a259840ccb540893ade215723", "3e8ca7105d2d4ff8afdad1c75f4f00c8", "9e0d496bcf954d5690b360c241b9f699", "08d3986daea8460481990642635c762d", "c638224e495b4d02bcf93bb993a04608", "13b982e310d0440c96dd2f59fa5eafaa", "a1d519dde1464f028fecaf0080951ef4", "48297bc87b604c68a9e7281f23cdbfee", "dd057d017b7f49da9dbdbf76639a0618", "879142eadb3a45c6903f81fd9c3fd9c5", "8d41716e6ed048139565b0bd3c25a1ad", "518f6dd5817b4d36965d2cf84c8b3cbd", "4f55c2f5da9e42baa1ce56eedeee09be", "d89b5a6e73c9453f8627b7fff996797e", "6d196e71f6d048a897cffa12040fa9fb", "2840d220bc3a4ee98f2d5e1a02e01058", "788c7488c4784d85abd173897f5c6414", "dfed5c25c29145528dd482d4fbd9e29c", "ad55068b22dc42f5b5e5b58077769ef0", "7119cddd8d504e1db92d4b639806f00a", "a08ebc53a07b43f3873d89802b667018", "e26fc9e95d0d4972a3f987dd348e56a7", "692e4a81b76b4b22a9d5300a672bbd3f", "380cf17b22004120822515ab9d44beb6", "84b42451daf7432fb17fa670f78c22fe", "d9ef20e1474646c1922534b6be20daef", "acdeefe570aa4a259ab5e659438513a4", "7ddd0b85cc624ed6a0bde81c09953365", "ecc10dba505047ecba4641bf92cebc70", "8790948fb64c42d280638d69f4e886dc", "2567958ef90b4bf49cf76059a48111d5", "547e0a8c7129457cae3ebe759fb0595f", "b3005b4bdfb84c549ad98de047dcb21d", "f9eabd2903424e72b9493f62733a412b", "1172a2311ec54ed7aebaec740e711d57" ] }, "outputId": "cd4d6fb5-4e9f-4844-f6fd-d40cf3ec9f17" }, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "Downloading readme: 0%| | 0.00/11.6k [00:00\n", "### Train the model\n", "Now let's use Huggingface TRL's `SFTTrainer`! More docs here: [TRL SFT docs](https://huggingface.co/docs/trl/sft_trainer). We do 60 steps to speed things up, but you can set `num_train_epochs=1` for a full run, and turn off `max_steps=None`. We also support TRL's `DPOTrainer`!" ], "metadata": { "id": "idAEIeSQ3xdS" } }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "95_Nn-89DhsL", "colab": { "base_uri": "https://localhost:8080/", "height": 122, "referenced_widgets": [ "388a2aaff9cd4725b78eb70136d12284", "a18a1a5abd814bfe876cc029ce75638b", "cf9f06653dd84a508b25a7d7ab412ae6", "9ba830d34c6d444c9d2c0c8d289a982c", "dc158fbc11ee405e861dcdce7a08639e", "f5ba9127d9e74833953d9e67844a84d8", "04e9f7bea00e40cca234faf98e23daa5", "ba5ce0d461074b2ba9323c01f764304b", "53f0301919644ea49cfcc35bf527f746", "d2766f23e0c2446996a0d7ed42cf430d", "43b89f289be544d5bb9bd8ee96f10f8c" ] }, "outputId": "1e1192a5-06b4-4f73-cabe-3fe73d835695" }, "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 08:54, Epoch 0/1]\n", "
\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
StepTraining Loss
11.751900
22.306900
31.609100
41.755200
51.426000
61.370200
70.986500
81.162100
91.005800
101.075900
110.902600
120.938500
130.865400
141.012700
150.847900
160.853400
170.969700
181.194500
190.954200
200.843800
210.842700
220.877500
230.829100
240.937000
251.034500
261.017000
271.001900
280.847000
290.808900
300.830700
310.815100
320.846000
330.935700
340.799200
350.878700
360.814500
370.821100
380.726300
391.035300
401.112300
410.881100
420.901100
430.895500
440.845300
450.885600
460.885400
470.813100
481.125500
490.858400
501.005600
510.960500
520.895900
530.944900
541.107000
550.746100
561.003100
570.863500
580.796100
590.788700
600.867200

" ] }, "metadata": {} } ], "source": [ "trainer_stats = trainer.train()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "pCqnaKmlO1U9", "cellView": "form", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "7b6f3381-fc91-4081-828d-a807ffaefaef" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "549.6228 seconds used for training.\n", "9.16 minutes used for training.\n", "Peak reserved memory = 11.002 GB.\n", "Peak reserved memory for training = 5.064 GB.\n", "Peak reserved memory % of max memory = 74.6 %.\n", "Peak reserved memory for training % of max memory = 34.337 %.\n" ] } ], "source": [ "#@title Show final memory and time stats\n", "used_memory = round(torch.cuda.max_memory_reserved() / 1024 / 1024 / 1024, 3)\n", "used_memory_for_lora = round(used_memory - start_gpu_memory, 3)\n", "used_percentage = round(used_memory /max_memory*100, 3)\n", "lora_percentage = round(used_memory_for_lora/max_memory*100, 3)\n", "print(f\"{trainer_stats.metrics['train_runtime']} seconds used for training.\")\n", "print(f\"{round(trainer_stats.metrics['train_runtime']/60, 2)} minutes used for training.\")\n", "print(f\"Peak reserved memory = {used_memory} GB.\")\n", "print(f\"Peak reserved memory for training = {used_memory_for_lora} GB.\")\n", "print(f\"Peak reserved memory % of max memory = {used_percentage} %.\")\n", "print(f\"Peak reserved memory for training % of max memory = {lora_percentage} %.\")" ] }, { "cell_type": "markdown", "source": [ "\n", "### Inference\n", "Let's run the model! You can change the instruction and input - leave the output blank!" ], "metadata": { "id": "ekOmTR1hSNcr" } }, { "cell_type": "code", "source": [ "# alpaca_prompt = Copied from above\n", "FastLanguageModel.for_inference(model) # Enable native 2x faster inference\n", "inputs = tokenizer(\n", "[\n", " alpaca_prompt.format(\n", " \"Continue the fibonnaci sequence.\", # instruction\n", " \"1, 1, 2, 3, 5, 8\", # input\n", " \"\", # output - leave this blank for generation!\n", " )\n", "], return_tensors = \"pt\").to(\"cuda\")\n", "\n", "outputs = model.generate(**inputs, max_new_tokens = 64, use_cache = True)\n", "tokenizer.batch_decode(outputs)" ], "metadata": { "id": "kR3gIAX-SM2q", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "d77da63c-2fd8-4eb8-9617-21e9af2a1b71" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "['Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\\n\\n### Instruction:\\nContinue the fibonnaci sequence.\\n\\n### Input:\\n1, 1, 2, 3, 5, 8\\n\\n### Response:\\n13, 21, 34, 55, 89, 144']" ] }, "metadata": {}, "execution_count": 9 } ] }, { "cell_type": "markdown", "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!" ], "metadata": { "id": "CrSvZObor0lY" } }, { "cell_type": "code", "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", " \"Continue the fibonnaci sequence.\", # instruction\n", " \"1, 1, 2, 3, 5, 8\", # 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)" ], "metadata": { "id": "e2pEuRb1r2Vg", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "43e0a26e-4f84-417e-dca5-f456720ae290" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "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\n" ] } ] }, { "cell_type": "markdown", "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!" ], "metadata": { "id": "uMuVrWbjAzhc" } }, { "cell_type": "code", "source": [ "model.save_pretrained(\"lora_model\") # Local saving\n", "# model.push_to_hub(\"your_name/lora_model\", token = \"...\") # Online saving" ], "metadata": { "id": "upcOlWe7A1vc" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "Now if you want to load the LoRA adapters we just saved for inference, set `False` to `True`:" ], "metadata": { "id": "AEEcJ4qfC7Lp" } }, { "cell_type": "code", "source": [ "if False:\n", " from unsloth import FastLanguageModel\n", " model, tokenizer = FastLanguageModel.from_pretrained(\n", " model_name = \"lora_model\", # YOUR MODEL YOU USED FOR TRAINING\n", " max_seq_length = max_seq_length,\n", " dtype = dtype,\n", " load_in_4bit = load_in_4bit,\n", " )\n", " FastLanguageModel.for_inference(model) # Enable native 2x faster inference\n", "\n", "# alpaca_prompt = You MUST copy from above!\n", "\n", "inputs = tokenizer(\n", "[\n", " alpaca_prompt.format(\n", " \"What is a famous tall tower in Paris?\", # instruction\n", " \"\", # 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)" ], "metadata": { "id": "MKX_XKs_BNZR", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "7e5db868-dc5d-4bfb-bf18-59034b87a900" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "['Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\\n\\n### Instruction:\\nWhat is a famous tall tower in Paris?\\n\\n### Input:\\n\\n\\n### Response:\\nOne of the most famous tall towers in Paris is the Eiffel Tower. It is a wrought-iron lattice tower on the Champ de Mars in Paris, France. It is named after the engineer Gustave Eiffel, whose company designed and built the tower. The tower is 324 meters (1,063 feet']" ] }, "metadata": {}, "execution_count": 12 } ] }, { "cell_type": "markdown", "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**." ], "metadata": { "id": "QQMjaNrjsU5_" } }, { "cell_type": "code", "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\")" ], "metadata": { "id": "yFfaXG0WsQuE" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "### Saving to float16 for VLLM\n", "\n", "We also support saving to `float16` directly. Select `merged_16bit` for float16 or `merged_4bit` for int4. We also allow `lora` adapters as a fallback. Use `push_to_hub_merged` to upload to your Hugging Face account! You can go to https://huggingface.co/settings/tokens for your personal tokens." ], "metadata": { "id": "f422JgM9sdVT" } }, { "cell_type": "code", "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 = \"\")" ], "metadata": { "id": "iHjt_SMYsd3P" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "### GGUF / llama.cpp Conversion\n", "To save to `GGUF` / `llama.cpp`, we support it natively now! We clone `llama.cpp` and we default save it to `q8_0`. We allow all methods like `q4_k_m`. Use `save_pretrained_gguf` for local saving and `push_to_hub_gguf` for uploading to HF.\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." ], "metadata": { "id": "TCv4vXHd61i7" } }, { "cell_type": "code", "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 = \"\")" ], "metadata": { "id": "FqfebeAdT073" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "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)." ], "metadata": { "id": "bDp0zNpwe6U_" } }, { "cell_type": "markdown", "source": [ "And we're done! If you have any questions on Unsloth, we have a [Discord](https://discord.gg/u54VK8m8tk) channel! If you find any bugs or want to keep updated with the latest LLM stuff, or need help, join projects etc, feel free to join our Discord!\n", "\n", "Some other links:\n", "1. Zephyr DPO 2x faster [free Colab](https://colab.research.google.com/drive/15vttTpzzVXv_tJwEk-hIcQ0S9FcEWvwP?usp=sharing)\n", "2. Llama 7b 2x faster [free Colab](https://colab.research.google.com/drive/1lBzz5KeZJKXjvivbYvmGarix9Ao6Wxe5?usp=sharing)\n", "3. TinyLlama 4x faster full Alpaca 52K in 1 hour [free Colab](https://colab.research.google.com/drive/1AZghoNBQaMDgWJpi4RbffGM1h6raLUj9?usp=sharing)\n", "4. CodeLlama 34b 2x faster [A100 on Colab](https://colab.research.google.com/drive/1y7A0AxE3y8gdj4AVkl2aZX47Xu3P1wJT?usp=sharing)\n", "5. Mistral 7b [free Kaggle version](https://www.kaggle.com/code/danielhanchen/kaggle-mistral-7b-unsloth-notebook)\n", "6. We also did a [blog](https://huggingface.co/blog/unsloth-trl) with 🤗 HuggingFace, and we're in the TRL [docs](https://huggingface.co/docs/trl/main/en/sft_trainer#accelerate-fine-tuning-2x-using-unsloth)!\n", "7. `ChatML` for ShareGPT datasets, [conversational notebook](https://colab.research.google.com/drive/1Aau3lgPzeZKQ-98h69CCu1UJcvIBLmy2?usp=sharing)\n", "8. Text completions like novel writing [notebook](https://colab.research.google.com/drive/1ef-tab5bhkvWmBOObepl1WgJvfvSzn5Q?usp=sharing)\n", "\n", "

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