{ "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)." ], "metadata": { "id": "IqM-T1RTzY6C" } }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "2eSvM9zX_2d3" }, "outputs": [], "source": [ "%%capture\n", "import torch\n", "major_version, minor_version = torch.cuda.get_device_capability()\n", "# Must install separately since Colab has torch 2.2.1, which breaks packages\n", "!pip install \"unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git\"\n", "if major_version >= 8:\n", " # Use this for new GPUs like Ampere, Hopper GPUs (RTX 30xx, RTX 40xx, A100, H100, L40)\n", " !pip install --no-deps packaging ninja einops flash-attn xformers trl peft accelerate bitsandbytes\n", "else:\n", " # Use this for older GPUs (V100, Tesla T4, RTX 20xx)\n", " !pip install --no-deps xformers trl peft accelerate bitsandbytes\n", "pass" ] }, { "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", "* With [PR 26037](https://github.com/huggingface/transformers/pull/26037), we support downloading 4bit models **4x faster**! [Our repo](https://huggingface.co/unsloth) has Llama, Mistral 4bit models.\n", "* [**NEW**] We make Gemma 6 trillion tokens **2.5x faster**! See our [Gemma notebook](https://colab.research.google.com/drive/10NbwlsRChbma1v55m8LAPYG15uQv6HLo?usp=sharing)" ], "metadata": { "id": "r2v_X2fA0Df5" } }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 347, "referenced_widgets": [ "75242c8ff4de48ccb52c20434cb35b4b", "e531c0c4eb144e3e9f3471abc8044b63", "78a5ccdf643246e58554c69be5f8fd1d", "cb036eb5495e4090be74cd45c8cc8623", "30e39b8145bb4771bfd8f3dcae7180e3", "c83a45a5c928496d8b98a5c29c9a0afc", "9ac47944637c42a99ab5efbbc11849c9", "a0eb357c52ee4749800c7f462ac6c040", "c0b8ea82ea084211a3da3ca7af6d34e4", "7c2bbdeec6c14127820ad7c18c23fd62", "ba9b9d9cf0d34a7887e102e6111060f1", "e474f9ac4b184af98bf14253af50d01d", "3748d7270b6c4ddf902559074564a231", "6362092216fb4ca791ddedfc546b533d", "afbfedf6e07c49919a4564eb304f2c82", "5d26fb674c4641efb1cbfc6053f81a63", "b9c79fda92db4f60927d28fc4e312b09", "71c15cc9985b49c0944589a075b64e42", "db2d3b40783549d5be2f7133fff866e2", "b2a58edcb859459f84ed3fdf935d8928", "5de73341ad9a46cc87978ce992332c60", "2595f442d9c4473dacebe288c6b99d8f", "72c1403b3e5f41dcb9a7b0d365975667", "58e148d223804b568c587bb0d7b803bf", "29cbcfe4a38e4eb9b14f688c289130dd", "cea83c31e0964ef2b1e243db7a95dcf8", "bca60398d1224542b7836ec4763e3805", "1c6acb5b35634effb9c6578fffa9fa48", "09871c45b8f3485d9b9e358e93c1d7d9", "2de90b9793054839b9789731e3148230", "c458d9c2882d471f82e648457a95adb3", "99fd29dcc8974bdda837d580bf6664c1", "b90e96f6816c46e7828eba3d64e1ea28", "87f5132ff1f24d1089616b0a85517416", "bb918a303a214e9e9d58121f65398a83", "7e21785b08c8465ab1e2b77afa9bece1", "389685c484cf4f29bb325c09fe25f657", "e71b4e1fb45049e7a97cfc7d8c8e12f5", "7df38a7bee904f5a9c7097a97edc934c", "d75eb5a7fb2d496598475173984889d5", "4e286b57f13743d094bb9f46be63b9b1", "45a3243a9e70427483899aa7978d542c", "58d779879bfa4d449f2130cde36c5f65", "fec00069556a480b90805c8fb82b7b1b", "37ccf3b009c748c28126df997606e124", "21d5cb3d464d4650b3ce29f1280dcf5e", "aace376ebfac40808a3c758f6350acad", "d9b3dd7433cd49f3820352179f9c55af", "929081f8d6544790807e2cfa510b8aa1", "d51d212faeaf404880661101afc0c3d4", "679519409ba84298a7777ad97d5e6909", "9e062fe136924fde828d9cdaffc9eb2d", "e5aae6ef2c7b4e1e95bc0cf4bed84080", "52d61128ca434da6be89dff5ccb0e432", "266bdc17fdb9485793a41204e84a688c", "7e53b5a232654711b9e935aba0d221e2", "6df7cce8ef7f4127a30ffe2ac6a3c0b9", "8408168867a74492bc4f5f127d2e55fe", "ea2e42fa8c2d4d529b36a5757f194f73", "cffbab61cb404036b2622b0aef2c83c6", "7b3634eadce242ab8eb09452d3d4f4d7", "e082b864c101469ab7c3e53f74c0d861", "7238e394d1e94f74b269ae950ac80a6b", "5159bd67a9c44775902a5d8ccd9974cd", "b494a99a10dc47249034388ab4953b64", "2ed85fd9b294405c9307ea2801f5c217", "5dcc4c3c63394e3fab4bbf772c85e50f", "05fe2e50370142fa9d1baae2e8554b95", "198ebd2bdbda4e67a984547ff27a3166", "3562caea1c854e2e907398d9a4d38e5a", "f423172624754c7ca4e5a6053ca23ff3", "f86209ecf89b416d89af1d3ac39eb84b", "dda6a00f7bfb4728b4a8a1222e06471e", "9b31bc6d7fbc48d7b953f1b16c655d0c", "39f69de79fb049619bb70685704b1856", "cd25dc0d5d824b5692f4244aa5dac9ad", "f26f91d73b834b7fae975a3a249da99c" ] }, "id": "QmUBVEnvCDJv", "outputId": "639b3704-e807-44a3-a047-58384faf60c8" }, "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": [ "c1b7fef5a97b4607945ad5bfc114cbd3", "01462c21701f4116b6bc2fe63f5b2cf4", "7eebe9c6002f420e9cce5226e8d667f8", "a52a93f4fc2b41429f809522e79ed4f8", "4fd730052612465796de38c8b421f9d7", "7f7cfa0aaf11484080c9e5508e1f3d4c", "87c1b83d3fa446c6a925357cb7dc61b9", "6b2069827ce4479080168a0390019870", "6b41cac1a4f94d3aa177176f30cb15f9", "53f8de0293be4044aa1b729bb5eb11b9", "8ab86ff950ef400ebf3b1800b7dd0af8", "70931237a95841769ae29c00ba7a9eba", "92532463a2c14c459113933b903837f3", "ebbef78187744f5f8d3ae789b5824cda", "faca70bfa4844304ba1b8cc150328d8e", "d998e6cdc3014a2d84cf1942b55047a2", "80073898216d454b8128bf2df91f5237", "fcbdb56f901e4a01b3ffdb75804f6b6f", "aabce41bd6bf47c7b09ab5fee3e2a304", "4bccb4ce450441cdbfb3f011f0c1c29c", "1bee13ef58bc43ddb8b9db70cda56299", "52fb456869d34622997763031ed51dcc", "fe9601d0a00d4ac2bb509f67dd1e10a5", "6a1a47252bb44eea91e532718d498e9f", "e04a34025cec46f29987b92e16c61c4b", "86c16c7c7aa14899af8f38c112c17533", "d45d859a33164006afbed6fdc5e33c8f", "3449c697b20f45a4b14413b33dd93514", "c8e6e9ae3fb24a21b8733f12dc65ea99", "eed92cd423f643ad96410d5aa14a244e", "61bb56769cfa4f08bb505b726dfb7ecc", "7379ce0651bc4c66b42bd63874f74c62", "f41432f4fcfc47dd94637faf4e24637d", "d096e6e11a4042d3a8fd19478b250827", "ddafe636b5d24f34a289f15043c3c0c4", "8565f220735842d790561863ab99c7eb", "1afb8c488b124d13a267b2fbe40791c0", "3c30512faeec43bebae7ff0d50686d92", "ece7443b74104370b791ccb7f3919122", "92b9004401fc4efc98663ef326f77b6c", "8db80bdc842e4b498832a5967344c81c", "f50c10052c5a4da791027130c0182cbb", "d305bc3068c34cad92fc29e34d0cad97", "662f392aa9b647b8ba671f0d266daa02" ] }, "outputId": "1b0f2ad6-e8c3-4348-fba1-dca426bd86e3" }, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "Downloading readme: 0%| | 0.00/11.6k [00:00\n", "### Train the model\n", "Now let's use Huggingface TRL's `SFTTrainer`! More docs here: [TRL SFT docs](https://huggingface.co/docs/trl/sft_trainer). We do 60 steps to speed things up, but you can set `num_train_epochs=1` for a full run, and turn off `max_steps=None`. We also support TRL's `DPOTrainer`!" ], "metadata": { "id": "idAEIeSQ3xdS" } }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "95_Nn-89DhsL", "colab": { "base_uri": "https://localhost:8080/", "height": 49, "referenced_widgets": [ "8283df539ac24e8bb2b13b99895d03a6", "f0e9901a5759478887e9cec18a26b74e", "2b803518664541b79c6a01fa66cbde4f", "017d38dcd200446c9695a74b8e92ad3b", "bcf6c05a480742209bd54f4678f95150", "dfe54d63f5fd4502b60d60cd3e0fb179", "8ae40a9c92c842408f353f0d44ee3cc1", "979c29f67c284008b4ccb0ad574bbcc3", "39760dc826d34d78a6c30367a3c472b2", "3f8b65cc774147b89e920bd56b503d00", "3746a4ae469345f8a7e6296e2b439265" ] }, "outputId": "06b69aa4-dc42-4e06-a231-633950f91633" }, "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 08:38, 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
11.401100
21.831100
31.329700
41.316300
51.077100
61.028700
70.800900
80.951900
90.847500
100.931400
110.751200
120.801800
130.793300
140.900100
150.723400
160.762500
170.861800
181.049300
190.855900
200.708800
210.783800
220.825000
230.736800
240.800500
250.904500
260.913500
270.889100
280.739600
290.742800
300.774400
310.753000
320.747600
330.827600
340.697500
350.787500
360.736200
370.755600
380.635000
390.915200
400.977700
410.746400
420.809000
430.753600
440.751200
450.756700
460.790200
470.703700
480.975400
490.756200
500.854900
510.835500
520.798900
530.837600
540.993200
550.659400
560.873400
570.752800
580.675600
590.700200
600.746100

" ] }, "metadata": {} } ], "source": [ "trainer_stats = trainer.train()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "pCqnaKmlO1U9", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "3e493356-b68b-4598-ff04-de0f700a6a8c" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "532.6662 seconds used for training.\n", "8.88 minutes used for training.\n", "Peak reserved memory = 6.564 GB.\n", "Peak reserved memory for training = 1.939 GB.\n", "Peak reserved memory % of max memory = 44.508 %.\n", "Peak reserved memory for training % of max memory = 13.148 %.\n" ] } ], "source": [ "#@title Show final memory and time stats\n", "used_memory = round(torch.cuda.max_memory_reserved() / 1024 / 1024 / 1024, 3)\n", "used_memory_for_lora = round(used_memory - start_gpu_memory, 3)\n", "used_percentage = round(used_memory /max_memory*100, 3)\n", "lora_percentage = round(used_memory_for_lora/max_memory*100, 3)\n", "print(f\"{trainer_stats.metrics['train_runtime']} seconds used for training.\")\n", "print(f\"{round(trainer_stats.metrics['train_runtime']/60, 2)} minutes used for training.\")\n", "print(f\"Peak reserved memory = {used_memory} GB.\")\n", "print(f\"Peak reserved memory for training = {used_memory_for_lora} GB.\")\n", "print(f\"Peak reserved memory % of max memory = {used_percentage} %.\")\n", "print(f\"Peak reserved memory for training % of max memory = {lora_percentage} %.\")" ] }, { "cell_type": "markdown", "source": [ "\n", "### Inference\n", "Let's run the model! You can change the instruction and input - leave the output blank!" ], "metadata": { "id": "ekOmTR1hSNcr" } }, { "cell_type": "code", "source": [ "# 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": "5a1aadfe-15a2-4ba7-ba24-666c2007b879" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n" ] }, { "output_type": "execute_result", "data": { "text/plain": [ "[' Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\\n\\n### Instruction:\\nContinue the fibonnaci sequence.\\n\\n### Input:\\n1, 1, 2, 3, 5, 8\\n\\n### Response:\\n13, 21, 34, 55, 89, 144, 233, 377, 610, 987, 1597, 2584, 4181, 6']" ] }, "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": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "e2pEuRb1r2Vg", "outputId": "5a5e1c68-00d6-4b48-dacf-9af1016f13b7" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n" ] }, { "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, 233, 377, 610, 987, 1597, 2584, 4181, 6765, 10946, 17711, 28657, 46368, 75025, 121393, 196418, 317811, \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": "203c75df-0481-4e60-decd-bb1fc3bf99f2" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n" ] }, { "output_type": "execute_result", "data": { "text/plain": [ "[' Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\\n\\n### Instruction:\\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 Eiffel Tower is one']" ] }, "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", "9. Gemma 6 trillion tokens is 2.5x faster! [free Colab](https://colab.research.google.com/drive/10NbwlsRChbma1v55m8LAPYG15uQv6HLo?usp=sharing)\n", "\n", "

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