{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "8LKIA_qnVKOz" }, "source": [ "To run this, press \"Runtime\" and press \"Run all\" on a **free** Tesla T4 Google Colab instance!\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", "**[NOTE]** TinyLlama was trained on 2048 max tokens. With Unsloth, we can arbitrarily set the sequence length we want via `max_seq_length=4096`. We do RoPE Scaling internally to magically extend the maximum context size!" ] }, { "cell_type": "code", "execution_count": 1, "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", "!pip install --no-deps xformers \"trl<0.9.0\" peft accelerate bitsandbytes" ] }, { "cell_type": "markdown", "metadata": { "id": "r2v_X2fA0Df5" }, "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)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 380, "referenced_widgets": [ "a7307b36f89b4dbdb5df9251af55716d", "cdf223fea87a4a8c8e7823d50e12500d", "f05b60b5ffd741e394bad3ec61054254", "2049a8292c01455a93dbe528984e071f", "bf88c58e072b452681a2c280f470bcce", "e61449dd551646c19ca50ac564a7622d", "520018927b504d0fa6435485fcda5c0b", "1098a94fcdd54e0a974fe5fd7bc2aeff", "2a4c1c97315143a396c70b5fc957a713", "7f06eba27c804907b6f1ab00f3f890ed", "aaa3088ade1f4aca8c06bf19d4f40478", "275c493d906f43d1a8a373902960e089", "9ad5ee59d38b470782f3ddb974202a20", "9046c9f2d8b544ffa38c8c95848e27e8", "fd0f9ebf026b47638f167bce373a998b", "a603c9e7b01f4ef4bd3303783633ccf0", "331387946fd2443ba55ae7621a6cad92", "d3a1f5c3c91a4857913253c74560e344", "4ff9b33efb5f4c428e6ff3b464cc9bd4", "c4b7898774ec4443b4046b3dfe70f01d", "daef9207fdc44116bd5791bff8f3e530", "5940f5bda2b746bdb23b6343a485dcb4", "f23d915eb54c4ebaa301a69ac8ffc882", "2c169e6e8c954b44bd9fe7241ca11fea", "fe5c0f2e8d564b6f98259051a81d66fc", "ed40d54b1aa245908ac627d22f2f5c59", "dae99d78caf5496496485a5efd6760d4", "04003f6d929c4910b7b77e187c8217d5", "944b651ff75d47eab770a911dd4f2199", "797f56cc9c524303b86acac45b0274f3", "a8f92a30776944c6a432a56d5e4df7bb", "ba68f0161b61493c80d09cf2ba24b100", "ffe3c9a643ef4c9f93be4375a21ab4f6", "8e448aed757049f6acc099cf3d3b4872", "b9832da0a36949898d3946eb6ffa2171", "6e951fdcd86c49f5a37d9aa49d0e9213", "ec2c7c5712954ae299137d8cf60a3412", "65ea86cf54fe4e8ea9dfb4cdc8763f11", "48071c6adf3344a1ba073ae5acf71927", "0e217794039b467cbc155ae31313d9c4", "a0ca07420ed44ce2ac2d184ae2831897", "0309bcbfef3e4bfc8c3d32a102f121ae", "2754285b8fe24d53b939c6ce3c86f9b7", "b59e0a1ad67c48908e907e6cd8cec552", "e897cd695321426d87e0c138c51ca311", "6825674c942e4b8889f88ab7483e1570", "559ed366479847439b209d9de5b483e3", "7431536e8b1e4eb286cad3dbe4c73529", "66dc2b3a185c4d42b69fb8f8c5978027", "1c23539c72c044a3858edfbd0f06ef6b", "9baf3c7a0b39477f9bb5165e413baff1", "8b7bbd3eadef4113aa727b6a598323a4", "5874debf1d5940a09e27f75c64d919a4", "8424e2875b864e3cbcd7609367ff8ffc", "a10d076d977f4e0aac08835906c2343a", "9e5a2a55b3ca462aafa22fe1a3d60ca5", "13c5114e29b24a8c95e78c5934c379a2", "1959ab5d86294ecfb1115f9bb23c4dbb", "721bd0b7af494f6395049a334be7f56d", "431e1b5d40864f888cddb276ca9a85ad", "6320d752f2f94ebd80f4e865ec6024ec", "0971d641a82c437197da78e44a873575", "701547279f3d47919d46521bfa372c42", "5626b14dc669415e8e934897083d51f0", "686129898e934f23834959aac3fe1632", "0c996e2c56db46539db728345a2ba379", "91655aa067e849388c76b224341bcd3b", "740777730ebf4b929bb4e51171fab17b", "202fde55617f43ef904d75cd008aaecb", "4a2477d4c56d478b94876359bb993873", "5e4f7c186b364193845c315308814924", "96a4b04999b94f5a85071bb6ecb1249f", "95eb3152e64446c9b72aafdba56d9eb9", "e4ec667dec6f4aef932ad1551394e7fd", "bf44042977c84e74945e5fe56c411908", "54efea8ef7a34ecaa586cacba4a6a5b8", "d03ed16c7c4146b38f204291289fe9f2" ] }, "id": "QmUBVEnvCDJv", "outputId": "6ea1f65c-74fa-4336-bfad-44e9ff3d77d7" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "π¦₯ Unsloth: Will patch your computer to enable 2x faster free finetuning.\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "config.json: 0%| | 0.00/1.09k [00:00, ?B/s]" ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "a7307b36f89b4dbdb5df9251af55716d" } }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ "==((====))== Unsloth: Fast Llama patching release 2024.7\n", " \\\\ /| GPU: Tesla T4. Max memory: 14.748 GB. Platform = Linux.\n", "O^O/ \\_/ \\ Pytorch: 2.3.0+cu121. CUDA = 7.5. CUDA Toolkit = 12.1.\n", "\\ / Bfloat16 = FALSE. FA [Xformers = 0.0.26.post1. FA2 = False]\n", " \"-____-\" Free Apache license: http://github.com/unslothai/unsloth\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "Unsloth: unsloth/tinyllama-bnb-4bit can only handle sequence lengths of at most 2048.\n", "But with kaiokendev's RoPE scaling of 2.0, it can be magically be extended to 4096!\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "model.safetensors: 0%| | 0.00/762M [00:00, ?B/s]" ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "275c493d906f43d1a8a373902960e089" } }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "generation_config.json: 0%| | 0.00/129 [00:00, ?B/s]" ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "f23d915eb54c4ebaa301a69ac8ffc882" } }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "tokenizer_config.json: 0%| | 0.00/894 [00:00, ?B/s]" ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "8e448aed757049f6acc099cf3d3b4872" } }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "tokenizer.model: 0%| | 0.00/500k [00:00, ?B/s]" ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "e897cd695321426d87e0c138c51ca311" } }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "special_tokens_map.json: 0%| | 0.00/438 [00:00, ?B/s]" ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "9e5a2a55b3ca462aafa22fe1a3d60ca5" } }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "tokenizer.json: 0%| | 0.00/1.84M [00:00, ?B/s]" ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "91655aa067e849388c76b224341bcd3b" } }, "metadata": {} } ], "source": [ "from unsloth import FastLanguageModel\n", "import torch\n", "max_seq_length = 4096 # Choose any! We auto support RoPE Scaling internally!\n", "dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+\n", "load_in_4bit = True # Use 4bit quantization to reduce memory usage. Can be False.\n", "\n", "# 4bit pre quantized models we support for 4x faster downloading + no OOMs.\n", "fourbit_models = [\n", " \"unsloth/mistral-7b-bnb-4bit\",\n", " \"unsloth/mistral-7b-instruct-v0.2-bnb-4bit\",\n", " \"unsloth/llama-2-7b-bnb-4bit\",\n", " \"unsloth/llama-2-13b-bnb-4bit\",\n", " \"unsloth/codellama-34b-bnb-4bit\",\n", " \"unsloth/tinyllama-bnb-4bit\",\n", " \"unsloth/gemma-7b-bnb-4bit\", # New Google 6 trillion tokens model 2.5x faster!\n", " \"unsloth/gemma-2b-bnb-4bit\",\n", "] # More models at https://huggingface.co/unsloth\n", "\n", "model, tokenizer = FastLanguageModel.from_pretrained(\n", " model_name = \"unsloth/tinyllama-bnb-4bit\", # \"unsloth/tinyllama\" for 16bit loading\n", " max_seq_length = max_seq_length,\n", " dtype = dtype,\n", " load_in_4bit = load_in_4bit,\n", " # token = \"hf_...\", # use one if using gated models like meta-llama/Llama-2-7b-hf\n", ")" ] }, { "cell_type": "markdown", "metadata": { "id": "S3xvsMEWyJbZ" }, "source": [ "**[NOTE]** TinyLlama's internal maximum sequence length is 2048. We use RoPE Scaling to extend it to 4096 with Unsloth!" ] }, { "cell_type": "markdown", "metadata": { "id": "SXd9bTZd1aaL" }, "source": [ "We now add LoRA adapters so we only need to update 1 to 10% of all parameters!\n", "\n", "**[NOTE]** We set `gradient_checkpointing=False` ONLY for TinyLlama since Unsloth saves tonnes of memory usage. This does NOT work for `llama-2-7b` or `mistral-7b` since the memory usage will still exceed Tesla T4's 15GB. GC recomputes the forward pass during the backward pass, saving loads of memory.\n", "\n", "`**[IF YOU GET OUT OF MEMORY]**` set `gradient_checkpointing` to `True`." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "6bZsfBuZDeCL", "outputId": "c3eefbe9-08cc-431e-e792-c04a157ca59d" }, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "Unsloth 2024.7 patched 22 layers with 22 QKV layers, 22 O layers and 22 MLP layers.\n" ] } ], "source": [ "model = FastLanguageModel.get_peft_model(\n", " model,\n", " r = 32, # Choose any number > 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 = 32,\n", " lora_dropout = 0, # Currently only supports dropout = 0\n", " bias = \"none\", # Currently only supports bias = \"none\"\n", " use_gradient_checkpointing = False, # @@@ IF YOU GET OUT OF MEMORY - set to True @@@\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 `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)." ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "id": "LjY75GoYUCB8" }, "outputs": [], "source": [ "alpaca_prompt = \"\"\"Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n", "\n", "### Instruction:\n", "{}\n", "\n", "### Input:\n", "{}\n", "\n", "### Response:\n", "{}\"\"\"\n", "\n", "EOS_TOKEN = tokenizer.eos_token\n", "def formatting_prompts_func(examples):\n", " instructions = examples[\"instruction\"]\n", " inputs = examples[\"input\"]\n", " outputs = examples[\"output\"]\n", " texts = []\n", " for instruction, input, output in zip(instructions, inputs, outputs):\n", " # Must add EOS_TOKEN, otherwise your generation will go on forever!\n", " text = alpaca_prompt.format(instruction, input, output) + EOS_TOKEN\n", " texts.append(text)\n", " return { \"text\" : texts, }\n", "pass\n", "\n", "from datasets import load_dataset\n", "#dataset = load_dataset(\"yahma/alpaca-cleaned\", split = \"train\")\n", "dataset = load_dataset(\"harry85/cleaned-dataset-harry\", split = \"train\")\n", "dataset = dataset.map(formatting_prompts_func, batched = True,)" ] }, { "cell_type": "markdown", "metadata": { "id": "idAEIeSQ3xdS" }, "source": [ "\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 1 full epoch which makes Alpaca run in 80ish minutes! We also support TRL's `DPOTrainer`! See our DPO tutorial on a free Google Colab instance [here](https://colab.research.google.com/drive/15vttTpzzVXv_tJwEk-hIcQ0S9FcEWvwP?usp=sharing)." ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 49, "referenced_widgets": [ "e373560eb23a4714b6b739dddc4cc893", "b2409fa2c0b64e3aab239b0eda848d73", "866a4dd6b8904757915bc34da953ef1f", "7da6ae7ee4214bdabd0196a3f3670c29", "5553019d21ea41fa857523370d96c8c5", "bda6f5c1996b4d7b81c93ee6561e1d12", "f97ddbe3bf3847c5a4f08e7fa4befa76", "1748c74c07af49d198627b979b10edf0", "8baeb252f8ee45fd9a77b7b5d74bbce9", "663aa242c40a47ed85c5d4e6f4481ba7", "3c4ada89ad014cdf819530ef9a771f28" ] }, "id": "95_Nn-89DhsL", "outputId": "4967593c-4579-4649-d72f-64c4f410cf16" }, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "Generating train split: 0 examples [00:00, ? examples/s]" ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "e373560eb23a4714b6b739dddc4cc893" } }, "metadata": {} } ], "source": [ "from trl import SFTTrainer\n", "from transformers import TrainingArguments\n", "from unsloth import is_bfloat16_supported\n", "\n", "trainer = SFTTrainer(\n", " model = model,\n", " tokenizer = tokenizer,\n", " train_dataset = dataset,\n", " dataset_text_field = \"text\",\n", " max_seq_length = max_seq_length,\n", " dataset_num_proc = 2,\n", " packing = True, # Packs short sequences together to save time!\n", " args = TrainingArguments(\n", " per_device_train_batch_size = 2,\n", " gradient_accumulation_steps = 4,\n", " warmup_ratio = 0.1,\n", " num_train_epochs = 1,\n", " learning_rate = 2e-5,\n", " fp16 = not is_bfloat16_supported(),\n", " bf16 = is_bfloat16_supported(),\n", " logging_steps = 1,\n", " optim = \"adamw_8bit\",\n", " weight_decay = 0.1,\n", " lr_scheduler_type = \"linear\",\n", " seed = 3407,\n", " output_dir = \"outputs\",\n", " ),\n", ")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "cellView": "form", "colab": { "base_uri": "https://localhost:8080/" }, "id": "2ejIt2xSNKKp", "outputId": "b58a5395-e0e3-43eb-b5dc-fd656d571a3d" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "GPU = Tesla T4. Max memory = 14.748 GB.\n", "0.879 GB of memory reserved.\n" ] } ], "source": [ "#@title Show current memory stats\n", "gpu_stats = torch.cuda.get_device_properties(0)\n", "start_gpu_memory = round(torch.cuda.max_memory_reserved() / 1024 / 1024 / 1024, 3)\n", "max_memory = round(gpu_stats.total_memory / 1024 / 1024 / 1024, 3)\n", "print(f\"GPU = {gpu_stats.name}. Max memory = {max_memory} GB.\")\n", "print(f\"{start_gpu_memory} GB of memory reserved.\")" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 288 }, "id": "yqxqAZ7KJ4oL", "outputId": "68574f6a-2791-45f0-cb96-8dc3a2d40a9c" }, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "==((====))== Unsloth - 2x faster free finetuning | Num GPUs = 1\n", " \\\\ /| Num examples = 38 | Num Epochs = 1\n", "O^O/ \\_/ \\ Batch size per device = 2 | Gradient Accumulation steps = 4\n", "\\ / Total batch size = 8 | Total steps = 4\n", " \"-____-\" Number of trainable parameters = 25,231,360\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "Step | \n", "Training Loss | \n", "
---|---|
1 | \n", "2.424500 | \n", "
2 | \n", "2.421700 | \n", "
3 | \n", "2.303700 | \n", "
4 | \n", "2.200400 | \n", "
"
]
},
"metadata": {}
}
],
"source": [
"trainer_stats = trainer.train()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "pCqnaKmlO1U9",
"outputId": "ecb21ea9-b0f5-48ab-9145-5baedb68a203"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"5034.6413 seconds used for training.\n",
"83.91 minutes used for training.\n",
"Peak reserved memory = 13.508 GB.\n",
"Peak reserved memory for training = 12.629 GB.\n",
"Peak reserved memory % of max memory = 91.592 %.\n",
"Peak reserved memory for training % of max memory = 85.632 %.\n"
]
}
],
"source": [
"#@title Show final memory and time stats\n",
"used_memory = round(torch.cuda.max_memory_reserved() / 1024 / 1024 / 1024, 3)\n",
"used_memory_for_lora = round(used_memory - start_gpu_memory, 3)\n",
"used_percentage = round(used_memory /max_memory*100, 3)\n",
"lora_percentage = round(used_memory_for_lora/max_memory*100, 3)\n",
"print(f\"{trainer_stats.metrics['train_runtime']} seconds used for training.\")\n",
"print(f\"{round(trainer_stats.metrics['train_runtime']/60, 2)} minutes used for training.\")\n",
"print(f\"Peak reserved memory = {used_memory} GB.\")\n",
"print(f\"Peak reserved memory for training = {used_memory_for_lora} GB.\")\n",
"print(f\"Peak reserved memory % of max memory = {used_percentage} %.\")\n",
"print(f\"Peak reserved memory for training % of max memory = {lora_percentage} %.\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ekOmTR1hSNcr"
},
"source": [
"\n",
"### Inference\n",
"Let's run the model! You can change the instruction and input - leave the output blank!"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "kR3gIAX-SM2q",
"outputId": "af750035-30ab-4426-f4ed-a4772b16eea6"
},
"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:\\n1, 1,2,3,5,8\\n\\n### Instruction\\nContinue the fibonna sequence.\\n## Input\\n1,1,2,3,5,8,10,2,3,5,8,1,2,3,5,8,1']"
]
},
"metadata": {},
"execution_count": 21
}
],
"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)"
]
},
{
"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",
" \"Does Haris Hota enjoy watching sports\", # instruction\n",
" \"Haris Hota\", # 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": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "IW6Jk-iTXLfD",
"outputId": "f7dba501-1c46-49d3-c665-dea8c1b126b5"
},
"execution_count": 24,
"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:\\nDoes Haris Hota enjoy watching sports\\n\\n### Input:\\nHaris Hota\\n\\n### Response:\\nHis watching sports\\n\\n\\n\\n\\n\\n### Instruction\\nHarisa is a good at math\\n## Input: Har is good at math\\n## Response: is good at math\\n\\n\\n\\n\\n## Instruction\\nHar is a good at math\\n## Input: is good math\\n## Response']"
]
},
"metadata": {},
"execution_count": 24
}
]
},
{
"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": "V2otZJcevdpZ"
}
},
{
"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",
" \"Does Haris Hota enjoy watching sports.\", # instruction\n",
" \"Haris Hota\", # 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": "QYvyvuj5vd7H",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "a421ad95-0666-4fef-e7de-21dec97370d3"
},
"execution_count": 27,
"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",
"Does Haris Hota enjoy watching sports.\n",
"\n",
"### Input:\n",
"Haris Hota\n",
"\n",
"### Response:\n",
"His watching sports\n",
"\n",
"\n",
"\n",
"\n",
"### Instruction\n",
"Harisa is a good at math\n",
"## Input: Har is good at math\n",
"## Response: is good at math\n",
"\n",
"\n",
"\n",
"## Instruction\n",
"Har is a good at math\n",
"## Input: is good math\n",
"## Response is good\n",
"\n",
"\n",
"## Instruction\n",
"Har is good at math\n",
"Input: good\n",
"##: is good\n",
"\n",
"\n",
"## Instruction\n",
"Har is good at\n",
":\n",
"Input good\n",
":\n",
"## is good\n",
"\n",
"\n",
"## Instruction\n",
"Har is good\n",
":\n",
"Input\n",
": good\n",
"## is\n",
"\n"
]
}
]
},
{
"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": 25,
"metadata": {
"id": "upcOlWe7A1vc",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "d157342e-7031-4fd0-dea3-3208bfd952ff"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"('tokenizer-finetuned-TinyLLAMA/tokenizer_config.json',\n",
" 'tokenizer-finetuned-TinyLLAMA/special_tokens_map.json',\n",
" 'tokenizer-finetuned-TinyLLAMA/tokenizer.model',\n",
" 'tokenizer-finetuned-TinyLLAMA/added_tokens.json',\n",
" 'tokenizer-finetuned-TinyLLAMA/tokenizer.json')"
]
},
"metadata": {},
"execution_count": 25
}
],
"source": [
"model.save_pretrained(\"finetuned-TinyLLAMA\") # Local saving\n",
"tokenizer.save_pretrained(\"tokenizer-finetuned-TinyLLAMA\")\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",
"source": [
"Now if you want to load the LoRA adapters we just saved for inference, set `False` to `True`:"
],
"metadata": {
"id": "3CgqR2B0vmCt"
}
},
{
"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",
"from transformers import TextStreamer\n",
"text_streamer = TextStreamer(tokenizer)\n",
"_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 64)"
],
"metadata": {
"id": "Yle1gGB3vmWK"
},
"execution_count": null,
"outputs": []
},
{
"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": "8m76iItmvni0"
}
},
{
"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": "wcMqKxzcvouj"
},
"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": "xwCTbEUavpoC"
}
},
{
"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": "gJKx0osWvqzz"
},
"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": "mhc9u6HAvr3b"
}
},
{
"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": "2_TmxAoavvYW"
},
"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": "SvFui8YuvZ1R"
}
},
{
"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",
"Some other links:\n",
"1. Zephyr DPO 2x faster [free Colab](https://colab.research.google.com/drive/15vttTpzzVXv_tJwEk-hIcQ0S9FcEWvwP?usp=sharing)\n",
"2. Mistral 7b 2x faster [free Colab](https://colab.research.google.com/drive/1Dyauq4kTZoLewQ1cApceUQVNcnnNTzg_?usp=sharing)\n",
"3. Llama 7b 2x faster [free Colab](https://colab.research.google.com/drive/1lBzz5KeZJKXjvivbYvmGarix9Ao6Wxe5?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 π€ 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",
""
]
}
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