{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "rqX9hLfEziis" }, "source": [ "Further documentation for the libraries used can be found at https://unsloth.ai/" ] }, { "cell_type": "markdown", "metadata": { "id": "9eJjsg0_D2Dj" }, "source": [ "For information on hugging face tokens go to https://huggingface.co/docs/hub/en/security-tokens" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "pdymVP95XA4_", "outputId": "9dd22bfa-2e4f-4c5f-f544-c73431671713" }, "outputs": [], "source": [ "# Add your hugging face token to secret keys or store your huggingface token as an environment variable.\n", "# It is used to download or upload models to your account.\n", "from google.colab import userdata\n", "from huggingface_hub import login\n", "login(userdata.get('TOKEN'))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "cBEG5Pq1lG-G" }, "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 --upgrade transformers\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": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "aRz4XCANNy8b", "outputId": "0ccf0b95-5daf-452f-8bf7-37e224f5ce81" }, "outputs": [], "source": [ "from unsloth import FastLanguageModel\n", "import torch\n", "\n", "max_seq_length = 2048\n", "\n", "def load_model(model_name, max_seq_length=max_seq_length, dtype=None, load_in_4bit=True):\n", " model, tokenizer = FastLanguageModel.from_pretrained(\n", " model_name=model_name,\n", " max_seq_length=max_seq_length,\n", " dtype=dtype,\n", " load_in_4bit=load_in_4bit,\n", " )\n", " return model, tokenizer" ] }, { "cell_type": "markdown", "metadata": { "id": "gShO0BiJIq1P" }, "source": [ "#Load the base model and attach LoRa adapters.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 362, "referenced_widgets": [ "3689cf3776564eb0aeee4330dfa3e3d3", "e041f54e04f54dcdb034b01061828560", "f678ea6d3cae4b8584ad2554455170c7", "0fdd0b53f0d8421582dc703055324f5a", "9376591750f84fca8cb28e9c3547d7c2", "a7dbef2655fd4910b4c77be13aeec794", "2dd41449ffba43aaab27524ed293ac5c", "ad69f28769ae4b058943d05f4f0106c9", "5d1a2752ee08428f9a20744b1b0c1a13", "89b90dada4a84f2784f8de3b56257237", "3abd9a7962c44661b9711bc72f0ae1c1", "b6ae505a739248ad9f4ae19fd2e27dfa", "482f20a8b0e34585826572068f372caf", "a44d2bb7eff54dba8b08cf688bb78e47", "1456f51cf75c46419ae41c935e91ca6c", "ee6ad031528e4a96821df3e4aa302aab", "1600c0a5ca3b4957ba043162cc87a9d4", "e2839ead63f34026a48b659b457cd287", "a35995218e48452d8e1d06519178cbff", "f682e8e835f54e118b31735241991610", "4029a9c9b7d94462b1c7769d09371837", "3423063d3cee4779890e0699f9942d87", "14a2f3c4a8e545d4b51df59ef1c9fb57", "2b9a63e62ddd4f7d9c662d894b8aba98", "f8e88ed0e5fc456d9ca0af2921b84567", "b44f4fff2e2c4d8b9fcf646c020ea120", "0eec7d5de3ab4c079223d8ff6025b926", "3d7bc27517644abebd7fc689c2572604", "9b25f95b98ec444ea895b49ec9389fe0", "7b26e464957d415692883cbb35eec48d", "0f29692848b047e798847da2f622d989", "5f32ee9393e8461c8d19ef9193db3883", "9f430a851527456a809dee52f4f6fa1e", "f253dd622f3e40639adac4e92121b850", "27fe49df0ce346f8a11876ab748b8e62", "47013ab3439741d09d1f480345cab67a", "91319f286c9244699008214edbeaa37c", "fea3d63aed214c31b85c7a61c223be70", "a31433fafff14d6db5295da689d6a2dc", "f755042c9d284beb9e62b46cabbce95a", "1f92034da40c472aa6d4ae1ad72304fa", "4fc8bb4fdfc34f4999ed4cb2f16cb9ec", "58f8bb95982846409adef80ee31181c3", "23f6e928e27749b28f146896adfb93c1", "435a19574aa240d1820e042ea58db8ab", "392f5e3c99054f9980820d566c82f9f3", "12fc8d312dcf404280074026890b08c1", "235195f2c3904776aabc1b3873ba73e6", "7c453e5e85a348a08b45e82338fd1827", "8883c661c0404f5c9eee1a50501b2049", "31a55dd3528a48ceb6c88f9165271dc1", "c8b909a793394f159e4539d42ee63efe", "66dda996d9ee48579583bb0d15056bef", "827108f23a1d45f493abe37e2026efce", "bb25cc648bd04e6da1a7bc431a9e3a1d", "c4326ba898bd4741b5dd1c26c1bdbe5b", "09e2dee7f7684deea98df4f04ad67a45", "93dc969e22dc4430962979f74965458e", "41cd9cc5172045aaa3eed8ca0d3715ca", "5ecda50fe7b64895b3869d238697f523", "90106cc2003f4968aa4c32a66ef8a5cf", "6147cf84651d455ebc91606ef761752c", "58aee35908e845f08bae7b2082fa139f", "107bf752d7194e45bb2e45031ec964f1", "6be7918e435a43819bdd95e04f502012", "9e6fd5d822f845628e4cae4bb1cc9260", "10249ba56f2c4718b938c741b0787dcc", "1ad218f809744ea48ba2f5f430c4d342", "cc1308b0d90a452981e72e1f3b4ba19a", "0dfd7601605b4ea189a844e329aa388d", "4a071fc3531c4d968483d81e9a91d5b4", "17b807ada6034094bfc17e8905d26293", "08f570ac3f6645b791dec4fc8f034313", "9d9f79126c4b4c629a6d863cd09f6c6c", "e41e17aa9d594c9b9df9c4cda16ff259", "6f8a14f3850b414ba6607b3560d869b0", "9a31dacd9bff4ac5a42f6549cb686819" ] }, "id": "urDIg3QRlCMx", "outputId": "b45d8a03-31ab-4d4a-ea31-bb9f7e6314fc" }, "outputs": [], "source": [ "models = [\n", " 'Phi-3.5-mini-instruct-bnb-4bit', # |Min Training Gpu : T4, Max Model size : 14.748 GB|\n", " 'gemma-2-27b-it-bnb-4bit', # |Min Training Gpu: A100, Min Testing GPU: T4, Max Model size: 39.564 GB|\n", " 'Meta-Llama-3.1-8B-Instruct-bnb-4bit' # |Min Training Gpu: T4, Min Testing GPU: T4, Max Model size : 14.748 GB|\n", " ]\n", "\n", "chat_templates = [\n", " 'phi-3',\n", " 'alpaca',\n", " 'llama-3.1'\n", "]\n", "\n", "# Select model and corresponding template.\n", "selection = 2\n", "model_name = models[selection]\n", "chat_template = chat_templates[selection]\n", "HfUsername = \"CooperW\"\n", "\n", "base_model = f\"unsloth/{model_name}\"\n", "LoRa_Adapters = f\"{HfUsername}/{model_name.replace('-', '_').replace('.', '_')}_128prompt\"\n", "\n", "# Load the base model.\n", "model, tokenizer = load_model(base_model)\n", "\n", "try:\n", " # For continued training load LoRa adapters to model.\n", " from peft import PeftModel\n", " model = PeftModel.from_pretrained(model, LoRa_Adapters)\n", "\n", " from huggingface_hub import snapshot_download\n", " import os\n", "\n", " download_path = snapshot_download(repo_id=LoRa_Adapters, ignore_patterns=[\"*.md\", \"*.safetensors\"])\n", " last_checkpoint_path = os.path.join(download_path, \"last-checkpoint\")\n", "except:\n", " # Initialise LoRa adapters.\n", " model = FastLanguageModel.get_peft_model(\n", " model,\n", " r = 128,\n", " target_modules = [\"q_proj\", \"k_proj\", \"v_proj\", \"o_proj\",\n", " \"gate_proj\", \"up_proj\", \"down_proj\",],\n", " lora_alpha = 8,\n", " lora_dropout = 0,\n", " bias = \"none\",\n", " use_gradient_checkpointing = \"unsloth\",\n", " random_state = 3407,\n", " use_rslora = True,\n", " loftq_config = None,\n", " )" ] }, { "cell_type": "markdown", "metadata": { "id": "2IrwuiZgVQ6h" }, "source": [ "#Data Mapping\n", "This function will take the given dataset and map the contents to the [\"text\"] field, this format is what is needed to be input to the model.\n", "\n", "Example:\n", "```\n", "<|user|>\n", "Hi!<|end|>\n", "<|assistant|>\n", "Hello! How are you?<|end|>\n", "<|user|>\n", "I'm doing great! And you?<|end|>\n", "\n", "```" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "QVEYv-pnlh9s" }, "outputs": [], "source": [ "from unsloth.chat_templates import get_chat_template\n", "\n", "tokenizer = get_chat_template(\n", " tokenizer,\n", " chat_template = chat_template,\n", " mapping = {\"role\" : \"from\", \"content\" : \"value\", \"user\" : \"human\", \"assistant\" : \"gpt\"},\n", ")\n", "\n", "def formatting_prompts_func(examples):\n", " convos = examples[\"conversations\"]\n", " texts = [tokenizer.apply_chat_template(convo, tokenize = False, add_generation_prompt = False) for convo in convos]\n", " return { \"text\" : texts, }\n", "pass" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 113, "referenced_widgets": [ "a11401c0389a431f91f121ac2c7a3b71", "4ee22d17a3cb47fda1719e794dfd19e9", "a1baf0fbc8034cb6a15930b9e698a0f4", "c6afc697668b4315a856fe991e1d7b8c", "f4dae16dc8a24d83b0b916a26a0255a1", "01e9840b9fa340b8a6a3be102bf13645", "2320db9057174d90883c07b4cd172ae7", "f78e4df64f9a473f8380c2f1f9b40414", "39dfd87409ce40b39576b07cff3674d0", "98c19e00fc8b4705872b3d397dd794e3", "b171a32eb8164515a651530b2ccd1573", "2d1c4c9228144ea79896dd01bad82e14", "8a948f04b0034c1f816bde4b29e89fba", "3300c67eed17403ebe38878931203d6b", "4e797a5bc60a4e1981497f536022f590", "f1c585c40fec428188d9c51d60504021", "2c955921188f440099398d30bfd2a4a2", "c2e9be71122945ed9e64b17039ee12e6", "81a1d102fed24ff79ee883ee7f3972de", "83cc0345dbb34a8ea025d13593d32f35", "9274b9e3a4874b09b55c1c59a0ac9674", "f7886b3fdf1c4988b7f201d728f44513", "8b99cb2061e04a6c82e2cb9d8bf4db4d", "b8f676098c9145448030032f80feeb6c", "f259acb3cd1948738dba98d6b2997e30", "d15e5841c3e441389f295966596f02e1", "76d38d8ee7d64ec2859f311eade510fa", "d3193af4ec29498b9d40f1cda371cd64", "aee5798e5e664aa0a728c949ff555d2a", "15ef70d1863f4757856566638830be3c", "ff154273b8e04867abbfe59848bc630d", "6095912b466b46f5834cfff9d55a9159", "685c9503fa1646258af5d20b54af346f" ] }, "id": "iVqRqJrqlpvD", "outputId": "d4ab65e0-7c53-476c-e351-2c73f891a556" }, "outputs": [], "source": [ "# This will load datasets from your hugging face repository, alternatively load locally stored datasets.\n", "from datasets import load_dataset\n", "\n", "dataset_repo = f\"{HfUsername}/jadidi\"\n", "dataset_name = \"train_network.jsonl\"\n", "\n", "train_dataset = load_dataset(dataset_repo, data_files=dataset_name, split='train')\n", "train_dataset = train_dataset.map(formatting_prompts_func, batched = True,)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "U9TEfn5l290J" }, "outputs": [], "source": [ "from transformers import Trainer, TrainingArguments\n", "from trl import SFTTrainer\n", "import os\n", "\n", "training_args = TrainingArguments(\n", " output_dir=\"outputs\",\n", " per_device_train_batch_size=8,\n", " gradient_accumulation_steps=16,\n", " lr_scheduler_type = \"cosine_with_restarts\",\n", " lr_scheduler_kwargs = { \"num_cycles\": len(trainer.get_train_dataloader()) / 100 },\n", " optim=\"adamw_8bit\",\n", " weight_decay=0.01,\n", " seed=3407,\n", " warmup_steps=10,\n", " # max_steps=100,\n", " num_train_epochs=3,\n", " learning_rate=2e-4,\n", " fp16=not torch.cuda.is_bf16_supported(),\n", " bf16=torch.cuda.is_bf16_supported(),\n", " logging_steps=1,\n", ")\n", "\n", "# Define the name of the repo your model is uploaded. By default : (YourHuggingFaceAccount/baseModelName)\n", "training_args = training_args.set_push_to_hub(\n", " model_id=LoRa_Adapters,\n", " strategy=\"checkpoint\",\n", " private_repo=True,\n", " always_push=False\n", ")\n", "\n", "# Define how often your model is uploaded during training.\n", "# Only LoRa adapters are saved.\n", "training_args = training_args.set_save(\n", " strategy='steps',\n", " steps=50,\n", " total_limit=2,\n", ")\n", "\n", "trainer = SFTTrainer(\n", " model=model,\n", " tokenizer=tokenizer,\n", " train_dataset=train_dataset,\n", " dataset_text_field=\"text\",\n", " max_seq_length=max_seq_length,\n", " dataset_num_proc=os.cpu_count(),\n", " packing=False,\n", " args=training_args,\n", ")\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "id": "0ajwRxIR4GXc", "outputId": "183abc90-a621-4882-be16-52b11e44d4c1" }, "outputs": [], "source": [ "try:\n", " trainer_stats = 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