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| "<a href=\"https://colab.research.google.com/gist/wesslen/fbd696cef602e9cc51de634eed444367/hf-sft-qlora-mistral7b-t4.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>" | |
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| "source": [ | |
| "# Introduction\n", | |
| "\n", | |
| "This notebook runs supervised fine-tuning using QLoRA with Mistral 7B and using `HuggingFaceH4/ultrachat_200k` data. This was designed to run only on Colab T4 (free GPU) and not optimized (e.g., doesn't use flash attention, uses torch.float16 not torch.bfloat16, and doesn't use more modern GPUs like A100). I intend to create a version like that down the road.\n", | |
| "\n", | |
| "This was largely inspired by:\n", | |
| "* HF's [Methods and tools for efficient training on a single GPU](https://huggingface.co/docs/transformers/perf_train_gpu_one)\n", | |
| "* Tim Dettmers' YouTube talk [QLoRA: Efficient Finetuning of Quantized LLMs](https://youtu.be/fQirE9N5q_Y?feature=shared)" | |
| ], | |
| "metadata": { | |
| "id": "l_Kx9IfRYHlp" | |
| } | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "Let's first install our core packages." | |
| ], | |
| "metadata": { | |
| "id": "dqOnoXfmYtxc" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "7qv5avXDx1BT", | |
| "outputId": "c5740976-9590-470c-f76b-defb38b9dc0e" | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| " Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n", | |
| " Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n", | |
| " Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n", | |
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| "\u001b[?25h Building wheel for peft (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n", | |
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| "\u001b[?25h" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "!pip install -q -U trl transformers accelerate git+https://github.com/huggingface/peft.git\n", | |
| "!pip install -q datasets bitsandbytes wandb" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "# https://huggingface.co/docs/trl/main/en/sft_trainer#using-flash-attention-1\n", | |
| "# !pip install -U optimum" | |
| ], | |
| "metadata": { | |
| "id": "yNXWOdsowxdF" | |
| }, | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "# Setup\n", | |
| "\n", | |
| "Note the sections where I commented out to ensure compatitibility for T4 GPU." | |
| ], | |
| "metadata": { | |
| "id": "tfVODXUgaLzZ" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "import torch\n", | |
| "from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, AutoTokenizer\n", | |
| "\n", | |
| "# if first is > 7.5, then set bnb_4bit_compute_dtype=torch.bfloat\n", | |
| "torch.cuda.get_device_capability()\n", | |
| "\n", | |
| "model_name = \"mistralai/Mistral-7B-v0.1\"\n", | |
| "\n", | |
| "bnb_config = BitsAndBytesConfig(\n", | |
| " load_in_4bit=True,\n", | |
| " bnb_4bit_use_double_quant=True,\n", | |
| " bnb_4bit_quant_type=\"nf4\",\n", | |
| " bnb_4bit_compute_dtype=torch.float16 # change to torch.bfloat16 for Ampere\n", | |
| ")\n", | |
| "\n", | |
| "device_map = {\"\": torch.cuda.current_device()} if torch.cuda.is_available() else None\n", | |
| "\n", | |
| "model = AutoModelForCausalLM.from_pretrained(\n", | |
| " model_name,\n", | |
| " quantization_config=bnb_config,\n", | |
| " use_cache=False, # set to False as we're going to use gradient checkpointing\n", | |
| " device_map=device_map,\n", | |
| " # attn_implementation=\"flash_attention_2\", # Can set to flash attention 2 if Ampere GPU\n", | |
| " # torch_dtype=\"auto\",\n", | |
| " trust_remote_code=True\n", | |
| ")" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 348, | |
| "referenced_widgets": [ | |
| "8339e124f5bb499ea532f761dd625512", | |
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| "a9df896bf02e4ca09ebe561c6d516940", | |
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| ] | |
| }, | |
| "id": "8xySgOlXx9GX", | |
| "outputId": "c492160d-8b1f-4140-f3f2-c879a6c69b4e" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stderr", | |
| "text": [ | |
| "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: UserWarning: \n", | |
| "The secret `HF_TOKEN` does not exist in your Colab secrets.\n", | |
| "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n", | |
| "You will be able to reuse this secret in all of your notebooks.\n", | |
| "Please note that authentication is recommended but still optional to access public models or datasets.\n", | |
| " warnings.warn(\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
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| "model_id": "8339e124f5bb499ea532f761dd625512" | |
| } | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "display_data", | |
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| "model_id": "3c63d53eac284a5682873f5b9d6ee114" | |
| } | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "Downloading shards: 0%| | 0/2 [00:00<?, ?it/s]" | |
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| "application/vnd.jupyter.widget-view+json": { | |
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| } | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
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| "application/vnd.jupyter.widget-view+json": { | |
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| "model_id": "9e1aa564b1c04712b822778f0471cc7f" | |
| } | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
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| "application/vnd.jupyter.widget-view+json": { | |
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| "model_id": "338d736488eb459ca0cf62b064cfc8a1" | |
| } | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "Loading checkpoint shards: 0%| | 0/2 [00:00<?, ?it/s]" | |
| ], | |
| "application/vnd.jupyter.widget-view+json": { | |
| "version_major": 2, | |
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| "model_id": "ea5c6aae63e34ce8949a04d27fb2e273" | |
| } | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "display_data", | |
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| "application/vnd.jupyter.widget-view+json": { | |
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| "model_id": "c049180288e44d738158ae2a1f5bb222" | |
| } | |
| }, | |
| "metadata": {} | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "## Tokenization" | |
| ], | |
| "metadata": { | |
| "id": "HMOfaUZCaVjW" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)\n", | |
| "\n", | |
| "# set pad_token_id equal to the eos_token_id if not set\n", | |
| "if tokenizer.pad_token_id is None:\n", | |
| " tokenizer.pad_token_id = tokenizer.eos_token_id\n", | |
| "\n", | |
| "# Set reasonable default for models without max length\n", | |
| "if tokenizer.model_max_length > 100_000:\n", | |
| " tokenizer.model_max_length = 2048\n", | |
| "\n", | |
| "# Most LLMs don't have a pad token by default\n", | |
| "tokenizer.pad_token = tokenizer.eos_token\n", | |
| "\n", | |
| "# Fix weird overflow issue with fp16 training\n", | |
| "# tokenizer.padding_side = \"right\"" | |
| ], | |
| "metadata": { | |
| "id": "58bajiRgyI0W", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 145, | |
| "referenced_widgets": [ | |
| "b53b111d45734141aac74c081b10a121", | |
| "41d5d84350bd4add9629d34e0544213c", | |
| "0488ed1bd52f421b9d272f99cbf4ddab", | |
| "efd60f5965c94a79b6cac1b7051cc0f3", | |
| "5cafc414b73f4a059846b3944ad3ff6c", | |
| "543d0cdea3a24eb0afcfc2add626ace6", | |
| "8fe2d8dd2b054891bfe3e3bf57e029dd", | |
| "a64f43c6adb34f35b30b219de908b3e8", | |
| "588b2679930c487995d85cc2876adcdc", | |
| "1e3de4ca8d9147f38755a096b039b53b", | |
| "e1fd9d0687e346f1b19dbdabcf21bdd6", | |
| "5552f5f9b59a4579ba9ab78ab1f7dadf", | |
| "f5c1c5f48900436aafc9cfb56c08398c", | |
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| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "This is alternative dataset I explored." | |
| ], | |
| "metadata": { | |
| "id": "gO31v2uiaYVv" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "# from datasets import load_dataset\n", | |
| "# from trl import DataCollatorForCompletionOnlyLM\n", | |
| "\n", | |
| "# dataset_name = \"timdettmers/openassistant-guanaco\"\n", | |
| "# dataset_train = load_dataset(dataset_name, split=\"train\")\n", | |
| "# dataset_test = load_dataset(dataset_name, split=\"test\")\n", | |
| "\n", | |
| "# # see https://huggingface.co/docs/trl/main/en/sft_trainer#train-on-completions-only\n", | |
| "# instruction_template = \"### Human:\"\n", | |
| "# response_template = \"### Assistant:\"\n", | |
| "# collator = DataCollatorForCompletionOnlyLM(instruction_template=instruction_template, response_template=response_template, tokenizer=tokenizer, mlm=False)" | |
| ], | |
| "metadata": { | |
| "id": "-9pNRqmVx7DI" | |
| }, | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "## Chat templates" | |
| ], | |
| "metadata": { | |
| "id": "TmM3PmS0adZq" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "# assume chat\n", | |
| "from datasets import load_dataset\n", | |
| "\n", | |
| "# based on config\n", | |
| "raw_datasets = load_dataset(\"HuggingFaceH4/ultrachat_200k\")\n", | |
| "\n", | |
| "from datasets import DatasetDict\n", | |
| "\n", | |
| "# remove this when done debugging\n", | |
| "indices = range(0,1000)\n", | |
| "\n", | |
| "dataset_dict = {\"train\": raw_datasets[\"train_sft\"].select(indices),\n", | |
| " \"test\": raw_datasets[\"test_sft\"].select(indices)}\n", | |
| "\n", | |
| "raw_datasets = DatasetDict(dataset_dict)\n", | |
| "raw_datasets" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 612, | |
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| "text/plain": [ | |
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| ], | |
| "application/vnd.jupyter.widget-view+json": { | |
| "version_major": 2, | |
| "version_minor": 0, | |
| "model_id": "3ec90a51c4a34aaeb0c4a124ba6db066" | |
| } | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
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| ], | |
| "application/vnd.jupyter.widget-view+json": { | |
| "version_major": 2, | |
| "version_minor": 0, | |
| "model_id": "779f67b3dae644788ade021e1c160135" | |
| } | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
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| ], | |
| "application/vnd.jupyter.widget-view+json": { | |
| "version_major": 2, | |
| "version_minor": 0, | |
| "model_id": "e35069647e104a9d9b5ac8f623e3446e" | |
| } | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "DatasetDict({\n", | |
| " train: Dataset({\n", | |
| " features: ['prompt', 'prompt_id', 'messages'],\n", | |
| " num_rows: 1000\n", | |
| " })\n", | |
| " test: Dataset({\n", | |
| " features: ['prompt', 'prompt_id', 'messages'],\n", | |
| " num_rows: 1000\n", | |
| " })\n", | |
| "})" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 6 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "import re\n", | |
| "import random\n", | |
| "from multiprocessing import cpu_count\n", | |
| "\n", | |
| "# Set chat template\n", | |
| "DEFAULT_CHAT_TEMPLATE = \"{% for message in messages %}\\n{% if message['role'] == 'user' %}\\n{{ '<|user|>\\n' + message['content'] + eos_token }}\\n{% elif message['role'] == 'system' %}\\n{{ '<|system|>\\n' + message['content'] + eos_token }}\\n{% elif message['role'] == 'assistant' %}\\n{{ '<|assistant|>\\n' + message['content'] + eos_token }}\\n{% endif %}\\n{% if loop.last and add_generation_prompt %}\\n{{ '<|assistant|>' }}\\n{% endif %}\\n{% endfor %}\"\n", | |
| "tokenizer.chat_template = DEFAULT_CHAT_TEMPLATE\n", | |
| "\n", | |
| "def apply_chat_template(example, tokenizer):\n", | |
| " messages = example[\"messages\"]\n", | |
| " # We add an empty system message if there is none\n", | |
| " if messages[0][\"role\"] != \"system\":\n", | |
| " messages.insert(0, {\"role\": \"system\", \"content\": \"\"})\n", | |
| " example[\"text\"] = tokenizer.apply_chat_template(messages, tokenize=False)\n", | |
| "\n", | |
| " return example\n", | |
| "\n", | |
| "column_names = list(raw_datasets[\"train\"].features)\n", | |
| "raw_datasets = raw_datasets.map(apply_chat_template,\n", | |
| " num_proc=cpu_count(),\n", | |
| " fn_kwargs={\"tokenizer\": tokenizer},\n", | |
| " remove_columns=column_names,\n", | |
| " desc=\"Applying chat template\",)\n", | |
| "\n", | |
| "# create the splits\n", | |
| "train_dataset = raw_datasets[\"train\"]\n", | |
| "eval_dataset = raw_datasets[\"test\"]\n", | |
| "\n", | |
| "for index in random.sample(range(len(raw_datasets[\"train\"])), 1):\n", | |
| " print(f\"Sample {index} of the processed training set:\\n\\n{raw_datasets['train'][index]['text']}\")" | |
| ], | |
| "metadata": { | |
| "id": "9h7XXBO_wayz", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 619, | |
| "referenced_widgets": [ | |
| "aa00beeac6054f2186ca1cd62b9e736d", | |
| "46e9dd3cd94745d6924576e9f520ac90", | |
| "a0589fe7b97146f494887961d960e0b3", | |
| "e02ed4a5a9ce4832849c3d5652a8d086", | |
| "6bef0579f9dd47fab54e1f85613f86e2", | |
| "79f35538bb554c0db8601fb537df2061", | |
| "f70961f9c64d4cc7aac5ef365a98d010", | |
| "cb3bc05d142345c498b8acdbcf535d6e", | |
| "dffcd564e688469f9112de43c63e9f03", | |
| "2f57ebfaffc14db99162a26d47d6b696", | |
| "8fb04a9df2ad4b0093496b29453f9f82", | |
| "8676771e3f604d9596dc8b18e7faa66b", | |
| "1fe6d913086049c08eec633c01f2ad10", | |
| "95251debe9bb40a999c3eae371dbe110", | |
| "555f3347d3e54906b3c06a9ee70c2038", | |
| "ae30aa4923ac4d04b62b875e212b5b91", | |
| "66f9d9e1407d4a00a605f3e170acfc8a", | |
| "e7846e6d60a640a1a0b4aa9fbe47cbf4", | |
| "183bd040f9b446abb4ce7ebafc30cef1", | |
| "1d94b4507353425cb2b801a378972a62", | |
| "4cd6aaf586934f53a57a8fea5bc9d7ef", | |
| "8f776447869f4a5e87b7b56140fe3621" | |
| ] | |
| }, | |
| "outputId": "c9bf3d81-baa2-4fdd-fbe8-36ed4060f0df" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "Applying chat template (num_proc=2): 0%| | 0/1000 [00:00<?, ? examples/s]" | |
| ], | |
| "application/vnd.jupyter.widget-view+json": { | |
| "version_major": 2, | |
| "version_minor": 0, | |
| "model_id": "aa00beeac6054f2186ca1cd62b9e736d" | |
| } | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "Applying chat template (num_proc=2): 0%| | 0/1000 [00:00<?, ? examples/s]" | |
| ], | |
| "application/vnd.jupyter.widget-view+json": { | |
| "version_major": 2, | |
| "version_minor": 0, | |
| "model_id": "8676771e3f604d9596dc8b18e7faa66b" | |
| } | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "Sample 17 of the processed training set:\n", | |
| "\n", | |
| "<|system|>\n", | |
| "</s>\n", | |
| "<|user|>\n", | |
| "Is it possible to have a combination of drawers, cupboards, and wine racks in the same sideboard?: Hand Made any size, any combination or drawers, cupboards, shelving, glazed, solid doors, wine racks.\n", | |
| "Stunning Oak top shown here but can be made with pine, Reclaimed Timber, Driftwood, Rustic Plank and beech tops also.\n", | |
| "4 Door Oslo sideboard as shown from £815. Can be made any size with your combination of drawers, cupboards, wine racks, shelving, door and plinth styles available, choice of different worktops (shown here with 20mm bull nose oak top) and finished in any Farrow and Ball colour of your choice.\n", | |
| "n.b prices are correct as of 01-05/16 but may be subject to change – please contact us for a quote based on your requirements, sizes and needs.</s>\n", | |
| "<|assistant|>\n", | |
| "Yes, it is possible to have a combination of drawers, cupboards, and wine racks in the same sideboard. The Hand Made sideboard can be customized to include any combination of these features, as well as shelving, glazed or solid doors, and different types of worktops. Prices for custom orders may vary, so interested customers should contact the seller for a quote based on their specific needs and requirements.</s>\n", | |
| "<|user|>\n", | |
| "Can you provide me with the contact information of the seller of the Hand Made sideboard? I would like to request a quote based on my specific needs and requirements.</s>\n", | |
| "<|assistant|>\n", | |
| "I do not have access to specific information about sellers or their contact details. However, if you have seen this sideboard online or in a physical store, you may be able to find the seller's contact information on their website or by reaching out to the store directly. Alternatively, you could search for similar handcrafted furniture makers in your area or online and reach out to them for custom orders.</s>\n", | |
| "<|user|>\n", | |
| "Can you provide me with some examples of the different worktops available for the Hand Made sideboard?</s>\n", | |
| "<|assistant|>\n", | |
| "Certainly, here are some examples of the different worktops available for the Hand Made sideboard:\n", | |
| "\n", | |
| "1. Oak - this is a classic and timeless choice that is known for its durability and natural beauty.\n", | |
| "2. Pine - a softer wood that has a warm, rustic feel and is often used in traditional or country-style interiors.\n", | |
| "3. Reclaimed Timber - this is wood that has been salvaged from old buildings, furniture or other sources, and can add character and history to a piece.\n", | |
| "4. Driftwood - a unique and striking option that is made from wood that has been washed up on shore and then treated and smoothed for use in furniture.\n", | |
| "5. Rustic Plank - this is a style of wood where individual planks of wood are combined to create a surface that has a rough-hewn, textured appearance.\n", | |
| "6. Beech - a smooth and hard-wearing option that has a uniform grain and a light, pale color.\n", | |
| "\n", | |
| "These are just a few examples of the different worktops available, and the options may vary based on the seller's availability and preferences.</s>\n", | |
| "\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "# PEFT / QLoRA" | |
| ], | |
| "metadata": { | |
| "id": "v8emMLgtagWv" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "from peft import prepare_model_for_kbit_training\n", | |
| "\n", | |
| "model.gradient_checkpointing_enable()\n", | |
| "model = prepare_model_for_kbit_training(model)" | |
| ], | |
| "metadata": { | |
| "id": "craPCvWg3nX3" | |
| }, | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "It's helpful to look at the model's architecture:" | |
| ], | |
| "metadata": { | |
| "id": "kUfc-4S3ajlA" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "print(model)" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "u3Q9WK8y3jp4", | |
| "outputId": "ee49f698-e2f2-4a07-e4cc-09419509b189" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "MistralForCausalLM(\n", | |
| " (model): MistralModel(\n", | |
| " (embed_tokens): Embedding(32000, 4096)\n", | |
| " (layers): ModuleList(\n", | |
| " (0-31): 32 x MistralDecoderLayer(\n", | |
| " (self_attn): MistralSdpaAttention(\n", | |
| " (q_proj): Linear4bit(in_features=4096, out_features=4096, bias=False)\n", | |
| " (k_proj): Linear4bit(in_features=4096, out_features=1024, bias=False)\n", | |
| " (v_proj): Linear4bit(in_features=4096, out_features=1024, bias=False)\n", | |
| " (o_proj): Linear4bit(in_features=4096, out_features=4096, bias=False)\n", | |
| " (rotary_emb): MistralRotaryEmbedding()\n", | |
| " )\n", | |
| " (mlp): MistralMLP(\n", | |
| " (gate_proj): Linear4bit(in_features=4096, out_features=14336, bias=False)\n", | |
| " (up_proj): Linear4bit(in_features=4096, out_features=14336, bias=False)\n", | |
| " (down_proj): Linear4bit(in_features=14336, out_features=4096, bias=False)\n", | |
| " (act_fn): SiLU()\n", | |
| " )\n", | |
| " (input_layernorm): MistralRMSNorm()\n", | |
| " (post_attention_layernorm): MistralRMSNorm()\n", | |
| " )\n", | |
| " )\n", | |
| " (norm): MistralRMSNorm()\n", | |
| " )\n", | |
| " (lm_head): Linear(in_features=4096, out_features=32000, bias=False)\n", | |
| ")\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "Let's setup the configuration parameters." | |
| ], | |
| "metadata": { | |
| "id": "aFNfN00saoEw" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "from peft import LoraConfig\n", | |
| "\n", | |
| "lora_alpha = 16\n", | |
| "lora_dropout = 0.05\n", | |
| "lora_r = 8\n", | |
| "\n", | |
| "peft_config = LoraConfig(\n", | |
| " lora_alpha=lora_alpha,\n", | |
| " lora_dropout=lora_dropout,\n", | |
| " r=lora_r,\n", | |
| " bias=\"none\",\n", | |
| " task_type=\"CAUSAL_LM\",\n", | |
| " target_modules=[\n", | |
| " \"q_proj\",\n", | |
| " \"v_proj\",\n", | |
| " \"k_proj\",\n", | |
| " \"out_proj\",\n", | |
| " #\"gate_proj\",\n", | |
| " #\"up_proj\",\n", | |
| " #\"down_proj\",\n", | |
| " #\"lm_head\"\n", | |
| " ]\n", | |
| ")" | |
| ], | |
| "metadata": { | |
| "id": "0nH4Chxy3kX8" | |
| }, | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "from transformers import TrainingArguments\n", | |
| "\n", | |
| "output_dir = \"./results\"\n", | |
| "save_steps = 10\n", | |
| "logging_steps = 10\n", | |
| "\n", | |
| "per_device_train_batch_size = 4\n", | |
| "optim = \"paged_adamw_32bit\"\n", | |
| "max_steps = 500\n", | |
| "\n", | |
| "gradient_accumulation_steps = 4\n", | |
| "learning_rate = 2e-4\n", | |
| "\n", | |
| "max_grad_norm = 0.3\n", | |
| "warmup_ratio = 0.03\n", | |
| "lr_scheduler_type = \"cosine\"\n", | |
| "\n", | |
| "training_arguments = TrainingArguments(\n", | |
| " do_eval=True,\n", | |
| " evaluation_strategy=\"epoch\",\n", | |
| " save_strategy=\"no\",\n", | |
| " output_dir=output_dir,\n", | |
| " per_device_train_batch_size=per_device_train_batch_size,\n", | |
| " gradient_accumulation_steps=gradient_accumulation_steps,\n", | |
| " optim=optim,\n", | |
| " save_steps=save_steps,\n", | |
| " logging_steps=logging_steps,\n", | |
| " learning_rate=learning_rate,\n", | |
| " fp16=True,#fp16=False,\n", | |
| " #bf16=True,\n", | |
| " max_grad_norm=max_grad_norm,\n", | |
| " max_steps=max_steps,\n", | |
| " warmup_ratio=warmup_ratio,\n", | |
| " group_by_length=True,\n", | |
| " report_to='wandb',\n", | |
| " lr_scheduler_type=lr_scheduler_type,\n", | |
| " gradient_checkpointing=True,\n", | |
| " overwrite_output_dir=True,\n", | |
| ")" | |
| ], | |
| "metadata": { | |
| "id": "454DYptu345P" | |
| }, | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "from trl import SFTTrainer\n", | |
| "\n", | |
| "max_seq_length = 512\n", | |
| "\n", | |
| "trainer = SFTTrainer(\n", | |
| " model=model,\n", | |
| " train_dataset=train_dataset,\n", | |
| " eval_dataset=eval_dataset,\n", | |
| " peft_config=peft_config,\n", | |
| " dataset_text_field=\"text\",\n", | |
| " # data_collator=collator,\n", | |
| " max_seq_length=max_seq_length,\n", | |
| " tokenizer=tokenizer,\n", | |
| " args=training_arguments,\n", | |
| ")" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 190, | |
| "referenced_widgets": [ | |
| "fe1dad4eb5e4417392bf91b82d9474c0", | |
| "ffeb38cfed0143779aa428746be0e983", | |
| "452aea643bfc4bac8f69b6d5f96931fd", | |
| "2efae0ef393c4f3d8c086b4116f33144", | |
| "e50335bd6db34569abe5a5734b3f1a00", | |
| "1c665ed8ffba42c29f5c9bf7bec61d30", | |
| "db610cec463f441c8352bfab07e92f50", | |
| "cac5a01d9570488daf661b6fe0fd8fcc", | |
| "fa6615edea4447b691738f08c76849da", | |
| "51896c979f0746c193cdc7906b07d998", | |
| "db49fb6bf2ad48ac9c2b183919ee385f", | |
| "e4060cc8fe6b47109527c05f6f2ad450", | |
| "0da174cfd6034aa5859db6cbe315206c", | |
| "9177a7dfccfc4d768d5e24dc10ad88d0", | |
| "825e6034296643deb0162056d701f520", | |
| "203c2236cccc4799a4bcf06b55a7b1cd", | |
| "479639e0194c4d9099c0bcedb177e1d5", | |
| "7553f4c9384f431eb2671fc4e3601cee", | |
| "37da2e5d87854a398e53059dec4b2112", | |
| "0cc4bdf5dc0a4404be07c460191c6731", | |
| "9fb1595a667f4728839c5785df58ee18", | |
| "5ade1ec9e24c4d75b5737960512260c8" | |
| ] | |
| }, | |
| "id": "IWI_a1oQ3794", | |
| "outputId": "aa92590c-0ddf-4dd3-aa91-a9930efec0ad" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "Map: 0%| | 0/1000 [00:00<?, ? examples/s]" | |
| ], | |
| "application/vnd.jupyter.widget-view+json": { | |
| "version_major": 2, | |
| "version_minor": 0, | |
| "model_id": "fe1dad4eb5e4417392bf91b82d9474c0" | |
| } | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "Map: 0%| | 0/1000 [00:00<?, ? examples/s]" | |
| ], | |
| "application/vnd.jupyter.widget-view+json": { | |
| "version_major": 2, | |
| "version_minor": 0, | |
| "model_id": "e4060cc8fe6b47109527c05f6f2ad450" | |
| } | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stderr", | |
| "text": [ | |
| "/usr/local/lib/python3.10/dist-packages/trl/trainer/sft_trainer.py:294: UserWarning: You passed a tokenizer with `padding_side` not equal to `right` to the SFTTrainer. This might lead to some unexpected behaviour due to overflow issues when training a model in half-precision. You might consider adding `tokenizer.padding_side = 'right'` to your code.\n", | |
| " warnings.warn(\n", | |
| "/usr/local/lib/python3.10/dist-packages/accelerate/accelerator.py:432: FutureWarning: Passing the following arguments to `Accelerator` is deprecated and will be removed in version 1.0 of Accelerate: dict_keys(['dispatch_batches', 'split_batches', 'even_batches', 'use_seedable_sampler']). Please pass an `accelerate.DataLoaderConfiguration` instead: \n", | |
| "dataloader_config = DataLoaderConfiguration(dispatch_batches=None, split_batches=False, even_batches=True, use_seedable_sampler=True)\n", | |
| " warnings.warn(\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "# Remove to add flash attention 1\n", | |
| "#with torch.backends.cuda.sdp_kernel(enable_flash=False, enable_math=False, enable_mem_efficient=False):\n", | |
| "train_result = trainer.train()" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 544 | |
| }, | |
| "id": "weVMFIS0zebW", | |
| "outputId": "b3294bde-4d8f-4b90-fee8-9e412c876148" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "application/javascript": [ | |
| "\n", | |
| " window._wandbApiKey = new Promise((resolve, reject) => {\n", | |
| " function loadScript(url) {\n", | |
| " return new Promise(function(resolve, reject) {\n", | |
| " let newScript = document.createElement(\"script\");\n", | |
| " newScript.onerror = reject;\n", | |
| " newScript.onload = resolve;\n", | |
| " document.body.appendChild(newScript);\n", | |
| " newScript.src = url;\n", | |
| " });\n", | |
| " }\n", | |
| " loadScript(\"https://cdn.jsdelivr.net/npm/postmate/build/postmate.min.js\").then(() => {\n", | |
| " const iframe = document.createElement('iframe')\n", | |
| " iframe.style.cssText = \"width:0;height:0;border:none\"\n", | |
| " document.body.appendChild(iframe)\n", | |
| " const handshake = new Postmate({\n", | |
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| "\u001b[34m\u001b[1mwandb\u001b[0m: You can find your API key in your browser here: https://wandb.ai/authorize\n", | |
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| "/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n", | |
| " warnings.warn(\n" | |
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| " [126/500 1:05:16 < 3:16:53, 0.03 it/s, Epoch 2/9]\n", | |
| " </div>\n", | |
| " <table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: left;\">\n", | |
| " <th>Epoch</th>\n", | |
| " <th>Training Loss</th>\n", | |
| " <th>Validation Loss</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <td>0</td>\n", | |
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| " <td>1.155820</td>\n", | |
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| " \n", | |
| " <progress value='501' max='500' style='width:300px; height:20px; vertical-align: middle;'></progress>\n", | |
| " [500/500 4:48:02, Epoch 8/9]\n", | |
| " </div>\n", | |
| " <table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: left;\">\n", | |
| " <th>Epoch</th>\n", | |
| " <th>Training Loss</th>\n", | |
| " <th>Validation Loss</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <td>0</td>\n", | |
| " <td>1.078200</td>\n", | |
| " <td>1.155820</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>2</td>\n", | |
| " <td>1.055500</td>\n", | |
| " <td>1.156595</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>4</td>\n", | |
| " <td>1.022600</td>\n", | |
| " <td>1.211415</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>6</td>\n", | |
| " <td>0.915500</td>\n", | |
| " <td>1.257879</td>\n", | |
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| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "import os\n", | |
| "\n", | |
| "def train_and_save_model(trainer, output_dir):\n", | |
| " \"\"\" Handles the training process and saves the model. \"\"\"\n", | |
| " trainer.train()\n", | |
| " trainer.save_model(output_dir)\n", | |
| " final_output_dir = os.path.join(output_dir, \"final_checkpoint\")\n", | |
| " trainer.model.save_pretrained(final_output_dir)\n", | |
| " tokenizer.save_pretrained(final_output_dir)" | |
| ], | |
| "metadata": { | |
| "id": "B-0roMqvQL2f" | |
| }, | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "# Evaluation" | |
| ], | |
| "metadata": { | |
| "id": "QDBxdoWlaxTZ" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "metrics = train_result.metrics\n", | |
| "# max_train_samples = training_args.max_train_samples if training_args.max_train_samples is not None else len(dataset)\n", | |
| "# metrics[\"train_samples\"] = min(max_train_samples, len(dataset))\n", | |
| "trainer.log_metrics(\"train\", metrics)\n", | |
| "trainer.save_metrics(\"train\", metrics)\n", | |
| "trainer.save_state()" | |
| ], | |
| "metadata": { | |
| "id": "5ZzZeFwM4A5e" | |
| }, | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "# Inference" | |
| ], | |
| "metadata": { | |
| "id": "QUcwW_3Ha34g" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "# from transformers import AutoTokenizer, AutoModelForCausalLM\n", | |
| "# from peft import PeftModel\n", | |
| "# import torch\n", | |
| "\n", | |
| "# tokenizer = AutoTokenizer.from_pretrained(output_dir)\n", | |
| "# model = AutoModelForCausalLM.from_pretrained(output_dir, load_in_4bit=True, device_map=\"auto\")\n", | |
| "# model = PeftModel.from_pretrained(output_dir, peft_model_id)\n", | |
| "# merged_model = model.merge_and_unload()\n", | |
| "\n", | |
| "# # We use the tokenizer's chat template to format each message - see https://huggingface.co/docs/transformers/main/en/chat_templating\n", | |
| "# messages = [\n", | |
| "# {\n", | |
| "# \"role\": \"system\",\n", | |
| "# \"content\": \"You are a friendly chatbot who always responds in the style of a pirate\",\n", | |
| "# },\n", | |
| "# {\"role\": \"user\", \"content\": \"How many helicopters can a human eat in one sitting?\"},\n", | |
| "# ]\n", | |
| "\n", | |
| "# # prepare the messages for the model\n", | |
| "# input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors=\"pt\").to(\"cuda\")\n", | |
| "\n", | |
| "# # inference\n", | |
| "# outputs = model.generate(\n", | |
| "# input_ids=input_ids,\n", | |
| "# max_new_tokens=256,\n", | |
| "# do_sample=True,\n", | |
| "# temperature=0.7,\n", | |
| "# top_k=50,\n", | |
| "# top_p=0.95\n", | |
| "# )\n", | |
| "# print(tokenizer.batch_decode(outputs, skip_special_tokens=True)[0])" | |
| ], | |
| "metadata": { | |
| "id": "nuvgzJZu4ELY" | |
| }, | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "# Appendix\n", | |
| "\n", | |
| "Use this if you need to flush out GPU" | |
| ], | |
| "metadata": { | |
| "id": "Ta244b-Kazd8" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "import gc\n", | |
| "import torch\n", | |
| "\n", | |
| "def flush():\n", | |
| " gc.collect()\n", | |
| " torch.cuda.empty_cache()\n", | |
| " torch.cuda.reset_peak_memory_stats()\n", | |
| "\n", | |
| "flush()" | |
| ], | |
| "metadata": { | |
| "id": "_b7nY5_4OreN" | |
| }, | |
| "execution_count": null, | |
| "outputs": [] | |
| } | |
| ] | |
| } |
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TODO: compare to https://brev.dev/blog/fine-tuning-mistral