{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "o6RxqIirisTj" }, "source": [ "To run this, press \"Runtime\" and press \"Run all\" on a **free** Tesla T4 Google Colab instance!\n", "
\n", " \n", " \n", " Join our Discord if you need help!\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 [DPO data prep](#Data), and how to [train via `DPOTrainer`](#Train).\n", "To learn more about DPO, read TRL's [blog post](https://huggingface.co/blog/dpo-trl). We follow [Huggingface's Alignment Handbook](https://github.com/huggingface/alignment-handbook) to replicate [Zephyr](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta)." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "id": "2eSvM9zX_2d3" }, "outputs": [], "source": [ "%%capture\n", "import torch\n", "major_version, minor_version = torch.cuda.get_device_capability()\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 \"unsloth[colab_ampere] @ git+https://github.com/unslothai/unsloth.git\"\n", "else:\n", " # Use this for older GPUs (V100, Tesla T4, RTX 20xx)\n", " !pip install \"unsloth[colab] @ git+https://github.com/unslothai/unsloth.git\"\n", "pass" ] }, { "cell_type": "markdown", "metadata": { "id": "QYds3fcii6gC" }, "source": [ "* We support Llama, Mistral, CodeLlama, TinyLlama, Vicuna, Open Hermes etc\n", "* And Yi, Qwen ([llamafied](https://huggingface.co/models?sort=trending&search=qwen+llama)), Deepseek, all Llama, Mistral derived archs.\n", "* We support 16bit LoRA or 4bit QLoRA. Both 2x faster.\n", "* `max_seq_length` can be set to anything, since we do automatic RoPE Scaling via [kaiokendev's](https://kaiokendev.github.io/til) method.\n", "* [**NEW**] With [PR 26037](https://github.com/huggingface/transformers/pull/26037), we support downloading 4bit models **4x faster**! [Our repo](https://huggingface.co/unsloth) has Llama, Mistral 4bit models.\n", "* DPO requires a model already trained by SFT on a similar dataset that is used for DPO. We use `HuggingFaceH4/mistral-7b-sft-beta` as the SFT model. Use this [notebook](https://colab.research.google.com/drive/1Dyauq4kTZoLewQ1cApceUQVNcnnNTzg_?usp=sharing) first to train a SFT model." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "E8-BWi7MzkRz", "outputId": "426df955-55fb-46c0-9213-e8873a5a3a15" }, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.10/dist-packages/unsloth/__init__.py:67: UserWarning: Running `ldconfig /usr/lib64-nvidia` to link CUDA.\n", " warnings.warn(\n" ] } ], "source": [ "# One must patch the DPO Trainer first!\n", "from unsloth import PatchDPOTrainer\n", "PatchDPOTrainer()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "id": "QmUBVEnvCDJv", "colab": { "base_uri": "https://localhost:8080/", "height": 586, "referenced_widgets": [ "15a7cbfe82fd485e82a90d374bab6c5e", "9b8e5300a1d24d45ab155dbcd29fac07", "5e06c5ca64804a329884d9c01ed8a2ef", "3fe5349f54084a8b9ac12801cc2e686e", 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"outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "config.json: 0%| | 0.00/708 [00:00user\\n' + message['content'] + eos_token + '\\n<|im_end|>' }}\\n{% elif message['role'] == 'system' %}\\n{{ '<|im_start|>system\\n' + message['content'] + '\\n<|im_end|>' }}\\n{% elif message['role'] == 'assistant' %}\\n{{ '<|im_start|>assistant\\n' + message['content'] + '\\n<|im_end|>' }}\\n{% endif %}\\n{% if loop.last and add_generation_prompt %}\\n{{ '<|im_start|>assistant' }}\\n{% endif %}\\n{% endfor %}\"\n", "\n", "\n", "def apply_chat_template(\n", " example, tokenizer, task: Literal[\"sft\", \"generation\", \"rm\", \"dpo\"] = \"sft\", assistant_prefix=\"<|im_start|>assistant\\n\"\n", "):\n", " def _strip_prefix(s, pattern):\n", " # Use re.escape to escape any special characters in the pattern\n", " return re.sub(f\"^{re.escape(pattern)}\", \"\", s)\n", "\n", " def _strip_suffix(s, pattern):\n", " # Use re.escape to escape any special characters in the pattern\n", " return re.sub(f\"{re.escape(pattern)}$\", \"\", s)\n", "\n", " if task in [\"sft\", \"generation\"]:\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(\n", " messages, tokenize=False, add_generation_prompt=True if task == \"generation\" else False\n", " )\n", " elif task == \"rm\":\n", " if all(k in example.keys() for k in (\"chosen\", \"rejected\")):\n", " chosen_messages = example[\"chosen\"]\n", " rejected_messages = example[\"rejected\"]\n", " # We add an empty system message if there is none\n", " if chosen_messages[0][\"role\"] != \"system\":\n", " chosen_messages.insert(0, {\"role\": \"system\", \"content\": \"\"})\n", " if rejected_messages[0][\"role\"] != \"system\":\n", " rejected_messages.insert(0, {\"role\": \"system\", \"content\": \"\"})\n", " example[\"text_chosen\"] = tokenizer.apply_chat_template(chosen_messages, tokenize=False)\n", " example[\"text_rejected\"] = tokenizer.apply_chat_template(rejected_messages, tokenize=False)\n", " else:\n", " raise ValueError(\n", " f\"Could not format example as dialogue for `rm` task! Require `[chosen, rejected]` keys but found {list(example.keys())}\"\n", " )\n", " elif task == \"dpo\":\n", " if all(k in example.keys() for k in (\"formatted_chosen\", \"formatted_rejected\")):\n", " # Compared to reward modeling, we filter out the prompt, so the text is everything after the last assistant token\n", " prompt_messages = [[msg for msg in example[\"formatted_chosen\"] if msg[\"role\"] == \"user\"][0]]\n", " # Insert system message\n", " if example[\"formatted_chosen\"][0][\"role\"] != \"system\":\n", " prompt_messages.insert(0, {\"role\": \"system\", \"content\": \"\"})\n", " else:\n", " prompt_messages.insert(0, example[\"formatted_chosen\"][0])\n", " # TODO: handle case where chosen/rejected also have system messages\n", " chosen_messages = example[\"formatted_chosen\"][2:]\n", " rejected_messages = example[\"formatted_rejected\"][2:]\n", " example[\"text_chosen\"] = tokenizer.apply_chat_template(chosen_messages, tokenize=False,\n", " chat_template=DEFAULT_CHAT_TEMPLATE\n", " )\n", " example[\"text_rejected\"] = tokenizer.apply_chat_template(rejected_messages, tokenize=False,\n", " chat_template=DEFAULT_CHAT_TEMPLATE\n", " )\n", " example[\"text_prompt\"] = tokenizer.apply_chat_template(\n", " prompt_messages, tokenize=False, add_generation_prompt=True,\n", " chat_template=DEFAULT_CHAT_TEMPLATE\n", " )\n", " example[\"text_prompt\"] = _strip_suffix(example[\"text_prompt\"], assistant_prefix)\n", " # example[\"text_chosen\"] = _strip_prefix(example[\"text_chosen\"], assistant_prefix)\n", " # example[\"text_rejected\"] = _strip_prefix(example[\"text_rejected\"], assistant_prefix)\n", " else:\n", " raise ValueError(\n", " f\"Could not format example as dialogue for `dpo` task! Require `[chosen, rejected]` keys but found {list(example.keys())}\"\n", " )\n", " else:\n", " raise ValueError(\n", " f\"Task {task} not supported, please ensure that the provided task is one of {['sft', 'generation', 'rm', 'dpo']}\"\n", " )\n", " return example\n", "\n", "\n", "def get_datasets(\n", " data_config: dict,\n", " splits: List[str] = [\"train\", \"test\"],\n", " shuffle: bool = True,\n", ") -> DatasetDict:\n", " \"\"\"\n", " Loads one or more datasets with varying training set proportions.\n", "\n", " Args:\n", " data_config (`DataArguments` or `dict`):\n", " Dataset configuration and split proportions.\n", " splits (`List[str]`, *optional*, defaults to `['train', 'test']`):\n", " Dataset splits to load and mix. Assumes the splits exist in all datasets and have a `train_` or `test_` prefix.\n", " shuffle (`bool`, *optional*, defaults to `True`):\n", " Whether to shuffle the training and testing/validation data.\n", "\n", " Returns\n", " [`DatasetDict`]: The dataset dictionary containing the loaded datasets.\n", " \"\"\"\n", "\n", " if type(data_config) is dict:\n", " # Structure of the input is:\n", " # dataset_mixer = {\n", " # \"dataset1\": 0.5,\n", " # \"dataset1\": 0.3,\n", " # \"dataset1\": 0.2,\n", " # }\n", " dataset_mixer = data_config\n", " else:\n", " raise ValueError(f\"Data config {data_config} not recognized.\")\n", "\n", " raw_datasets = mix_datasets(dataset_mixer, splits=splits, shuffle=shuffle)\n", " return raw_datasets\n", "\n", "\n", "def mix_datasets(dataset_mixer: dict, splits: Optional[List[str]] = None, shuffle=True) -> DatasetDict:\n", " \"\"\"\n", " Loads and mixes datasets according to proportions specified in `dataset_mixer`.\n", "\n", " Args:\n", " dataset_mixer (`dict`):\n", " Dictionary containing the dataset names and their training proportions. By default, all test proportions are 1.\n", " splits (Optional[List[str]], *optional*, defaults to `None`):\n", " Dataset splits to load and mix. Assumes the splits exist in all datasets and have a `train_` or `test_` prefix.\n", " shuffle (`bool`, *optional*, defaults to `True`):\n", " Whether to shuffle the training and testing/validation data.\n", " \"\"\"\n", " raw_datasets = DatasetDict()\n", " raw_train_datasets = []\n", " raw_val_datasets = []\n", " fracs = []\n", " for ds, frac in dataset_mixer.items():\n", " fracs.append(frac)\n", " for split in splits:\n", " try:\n", " # Try first if dataset on a Hub repo\n", " dataset = load_dataset(ds, split=split)\n", " except DatasetGenerationError:\n", " # If not, check local dataset\n", " dataset = load_from_disk(os.path.join(ds, split))\n", "\n", " if \"train\" in split:\n", " raw_train_datasets.append(dataset)\n", " elif \"test\" in split:\n", " raw_val_datasets.append(dataset)\n", " else:\n", " raise ValueError(f\"Split type {split} not recognized as one of test or train.\")\n", "\n", " if any(frac < 0 for frac in fracs):\n", " raise ValueError(\"Dataset fractions cannot be negative.\")\n", "\n", " if len(raw_train_datasets) > 0:\n", " train_subsets = []\n", " for dataset, frac in zip(raw_train_datasets, fracs):\n", " train_subset = dataset.select(range(int(frac * len(dataset))))\n", " train_subsets.append(train_subset)\n", " if shuffle:\n", " raw_datasets[\"train\"] = concatenate_datasets(train_subsets).shuffle(seed=42)\n", " else:\n", " raw_datasets[\"train\"] = concatenate_datasets(train_subsets)\n", " # No subsampling for test datasets to enable fair comparison across models\n", " if len(raw_val_datasets) > 0:\n", " if shuffle:\n", " raw_datasets[\"test\"] = concatenate_datasets(raw_val_datasets).shuffle(seed=42)\n", " else:\n", " raw_datasets[\"test\"] = concatenate_datasets(raw_val_datasets)\n", "\n", " if len(raw_datasets) == 0:\n", " raise ValueError(\n", " f\"Dataset {dataset_mixer} not recognized with split {split}. Check the dataset has been correctly formatted.\"\n", " )\n", "\n", " return raw_datasets" ] }, { "cell_type": "markdown", "metadata": { "id": "EQ-Cp2V6kDcr" }, "source": [ "\n", "### Data Prep\n", "We follow Huggingface's [Alignment Handbook](https://github.com/huggingface/alignment-handbook) for [Zephyr](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta) and use the [Ultra Feedback dataset](https://huggingface.co/datasets/HuggingFaceH4/ultrafeedback_binarized), and sample 0.5% of it to speed things up. You can sample the full dataset for a full run." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 461, "referenced_widgets": [ "194acd1165bc4951a6cefd17e0f9ddd9", "8fca3179c0144a24bf947ef377181615", "4c603d33d6f145f0969b9762086ca8b3", "387be0bbd47a4fcf81aeda958a131094", "b78cdf19645040869df3b2d272d8ed59", "49ba948392bb4fd2ae7b08e9cbd5f675", "13b776819662478692b43339492d0a72", "ab9da55608c148e7ad7e9f3be0581e55", "7ba4215ed7a249a4a2dba32cf067f918", "8d44fc6ca99645b381af35085655d365", "5f38615195d14e24880081a65f6fa904", "ec319a6b34d2439480dee1c372d5006f", "42992092bec947118fa609bdc8893a80", "b2e19120e5df43a5b4cd50f8e43b3c0b", "8392e2d4171c4c8f9142421c9ef2a80d", "570301d70d024b2fa5d4243a6eaad893", "4c647a99fe0d4077b92ba3e61af918df", "197b7664bb374a8398a9a65cfa80101c", "8d648f4dc7ff47b886587f66a59a53a9", "2cc938e1eddc43b7b3f21867812b4ac5", "b197e409fdac4a0aa9b60fada6eb693d", "885a73d2b271425aa3e65a1c2b4f6dec", "93351d9028914f07a214bcb328a51f72", "67a0ea9c0d6749db9acb6536dfc341cb", "693b8e2cfa5547b88210d738970b858f", "aaaccd8f42a542c28507b0b37eef35b2", "088b2e9d261c478395e761361d9c0d40", "43d2bde000c546698ef0bd70012ecc63", "5cd320f05f0d4dd7bf697a022f5e87d4", "f3f3ef0c63534600add33b4043e2ae1a", "5acf526e19a8474d892a56dd9745a83b", "c53e63c630c94ecea9a8e8b43480e5a3", "3f38758d846b417ab3ad22b54b80bf18", "c31914d701a54acdb57fead5d6fde30e", "f233bac88ae84cd1be04c933d64a0196", "f982f7dd36274660bc18a97d69ede655", "b1a74e1d9e264cbcb9942477c77b98d5", "d3e1611a1eb7419f90206538e79d770c", "4d2f816f621b421b94d93cc6875272fe", "2a5ae17a3876477bb120fa7cd96ad170", "7e18c74a998243babbb049c46a071ba3", "82a4ba3df9f842a98793c263adc6a8f8", "010dd1a4cfb143ea8f383a31b9b8e444", "842d34143da848788a67f11d6d4e3ac1", "be6612806f744433ba3f57f07c86e829", "f38b9e8f0dc54fa4920f4c89cd86d448", "378e765e69ac40bb824ad8d6986bb366", "2ad28a04693349c28478a425910b3389", "272ab21732394e13b790f52f4fa2ac0d", "7acfd2169e044ef69144fd4ba76a918f", "08b2484761fc4077aa6f881299b3a643", "b9b6b0f8c3b34fddb1ae6b052b9d048d", "d65103e2eab1417faef794331b880042", "3f916cbf57894d7986cafdb3eedb4e0d", "2f8ac33a64284a0886077d31f9bf1b9a", "557a97862bd04972ad0dd1ae25404245", "316c06212e1142b2bb4ec8399bed9e40", "6c09e554d49441a8a75c7bdb3899da5e", "532e101a8d9b4bf0bfdc3e892e98c217", "1e95e69e23b54a289cb6b271ed1e70a7", "6ab362797455486a8ac5af45506f105d", "194d9838e0794dbab4e65e0a9c5585ca", "4f8ca1bf349c4bf4994db0bcdf817ba8", "70fc1b6eba154760a952a5b46c2d091d", "ba22825f14fc4572bb73295131de995e", "5e9e7a29edca4e0f9e1498b521b5893e", "83c1667c61a240d583fc73982d14597a", "b4db9e01fdaa46deb45f47eeb61c1fbf", "629287fc078f417c99e151cde8947763", "19012d9425c14e6caf3285ff55ebe1a1", "24372afcb01244979c2db59b725ad6da", "0cee97769a58491d9478f3b0372ffbf4", "595554f94bc842bca0da08f2e190f21a", "d5751f68f929413eaff3c6aa0fe40818", "51500117e5c44aca854a150dcb861c05", "cdb87f0797104924bbd0cb70474d3c3b", "5277500b92044e35b7e60f1653537375", "79de24bf8d514d33bd526b51208e95a4", "72669652394c41a9acab5c149906e3ae", "0fcce1f723b64aec9e83b9cc51137939", "75554786314a4760a673e9e2b3aa303e", "d39ee4ef1a9c4718994f87104e653ef7", "f72f233fb585423eb8fefe5133b923fd", "a104df47f3af47d985ce2d244c287d4b", "75787b559fd642658db10f46c8152cc0", "aa01dce4d2f74e69b40128ca4632026f", "934542f12f2e429fb9a1bb13015d905f", "71ea66b1b9304f0996419b40f4f37c5a", "cb5da885a44b4d52a92186f84b95f3ce", "93b76a27cc064c6d9ac53c280f3af55b", "5f09ebfb09ff4f6382cd33b4f4b38128", "9fa4eb7f005e4b68965024c7c91fb789", "11e9a1391c544c0e9d321abc8e0b3a6c", "4ce818f582a24cbb84f49acdf26d86bb", "65d0c1d3cc3a4d408835a02329cb0b96", "45ebb1d9870d47d0a9f4ac14cf90267c", "b5019a7dba3a4ef9b91063597bd92a2c", "9f5e502292b94f8b9279e2e4b8a9ee5c", "53874d2d11c54536804990aa8e158638", "33fb954650b44f48bf884971963c094d", "93363d735e3649a18af2bc443713b72b", "16b69fa487b44b51a51f9f2ecda3301f", "56477fec6a4340f2b75bbabb5a3e9e71", "14c4d6c2f2ba42c39900346f1134e58d", "c969afac63e24ad9b78f4e1f2dcdd9d3", "c7b54ee4be4749db8d870b816d7dd3a1", "b67eefe02ab84c5287864f97ab318671", "11cd6bf15c884bf8940876813bf44898", "2696297869b643c0abe7171d9a46445c", "b463e275a208419d99d91938f739f65e", "ee3880ad4a554b8693fe447b66cef133", "f260b085a73b4fc39dde584d61f5b106", "b24fc7f7694c436c97967d7aeea55573", "a29b5b08fb6441a3b48d357747758bbb", "8d45ab543cdb4095bc804d9aa88874ea", "aa7fc0daa7cf4e7ba910ae5b3b0c45d4", "bb67f52bcc2e4c8f9e13e8378a4a5da0", "203c85319d3245dfb4964119bb027ff3", "3f1e09c9e8604d23929b4b7d8c9a81da", "a217063d730847278448e682d513b6d1", "b8c7e643730f4eb086b9bf07362cd5e6", "9bd259f37c7648989befaebdee302d63", "a41eef4ff3e84db9b3726005bf48d20b", "64853f71b22d410d9340d3b8fee46766", "e9f3ee61cfdd4b87ae0dcd8f296836c5", "7810697e2b934108b54ec34a9aec19df", "d3df09ade4e34ccfbada7d6f84f3e492", "de5705174b8b4cbdb43906fc3dd5d933", "8eec8b3f41f0439f93f3209198254b75", "a65a0b8a92704383aefdd59a857cc668", "2d33b6acb1a140f4b05f6f0fcc24edc6", "0972b0f989524dac8220bebaa7e7d6f3", "9256be23fa7e427bb0b2876853915792", "ee1f530ff5e54174ac4c76095fff688c", "92642984912b489f9b91fdc584d2765a", "02ceac7ebb4140ce9b41509658e78cb4", "44913691f19d4483a46ed589be024f8a", "6749ffcbb9584479a688ffa9a3a26bdc", "ed0dc02c991b493f8803c8caade18389", "921bf29f955642f39be797b59f73f366", "9637537343614ee8950f237a911dd920", "0a5169fb47e344a2ac4d8628443a9315", "b2a73a1aabbf473e9a0ea1b03f548361" ] }, "id": "r6bUnxe6N3pf", "outputId": "8d4b2169-8467-41a2-905d-2afb9d361cb9" }, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "Downloading readme: 0%| | 0.00/939 [00:00 chosen and text_rejected -> rejected\n", "for split in [\"train\"]:\n", " raw_datasets[split] = raw_datasets[split].rename_columns(\n", " {\"text_prompt\": \"prompt\", \"text_chosen\": \"chosen\", \"text_rejected\": \"rejected\"}\n", " )" ] }, { "cell_type": "markdown", "metadata": { "id": "7AxUmeAGkjDd" }, "source": [ "We shall print a random item from the dataset" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "oF63zQqNlNJC", "outputId": "a188d271-4521-40fc-aa42-1ef3f0c12d23" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "('<|im_start|>system\\n'\n", " 'You are an AI assistant. You will be given a task. You must generate a '\n", " 'detailed and long answer.\\n'\n", " '<|im_end|>\\n'\n", " '<|im_start|>user\\n'\n", " 'Q: I heard that Jordan spent a night in jail after something horrible and '\n", " 'regretting it. And I was wondering What will Jordan want to do next?\\n'\n", " '\\n'\n", " 'OPT: A. say nothing. B. cry quietly. C. break the law.\\n'\n", " 'A:\\n'\n", " '<|im_end|>\\n')\n", "('<|im_start|>assistant\\n'\n", " 'Jordan will likely want to take several steps to address the situation, '\n", " 'learn from the experience, and move on after spending a night in jail for a '\n", " 'horrible and regrettable incident. Here, we will explore these steps in more '\n", " 'depth.\\n'\n", " '\\n'\n", " '1. Reflect on the experience: Having gone through a challenging and '\n", " 'frightening experience, Jordan will likely want to reflect on what happened, '\n", " 'what led to the negative choices, and how those choices resulted in the '\n", " 'night in jail.\\n'\n", " '\\n'\n", " '2. Accept responsibility: Recognizing the harm caused, Jordan may feel '\n", " \"compelled to accept responsibility and acknowledge the incident's impact on \"\n", " 'them and anyone else involved.\\n'\n", " '\\n'\n", " '3. Seek advice or professional help: Depending on the severity of the '\n", " 'situation, Jordan might want to consider talking to a trusted friend, family '\n", " 'member, or professional counselor. This could be an essential step in '\n", " 'addressing any underlying emotional or psychological issues that contributed '\n", " 'to the incident and can guide Jordan in forging a plan to rectify the '\n", " 'situation.\\n'\n", " '\\n'\n", " \"4. Make amends: If Jordan's actions directly affected others, they might \"\n", " 'want to take steps to make amends. This could include offering apologies, '\n", " 'providing compensation or support, or engaging in community service to '\n", " 'demonstrate remorse and dedication to making things right.\\n'\n", " '\\n'\n", " '5. Learn from the experience: As part of the reflection process, Jordan will '\n", " 'need to identify mistakes and weaknesses that led to this regrettable event. '\n", " 'By acknowledging these factors, Jordan can strive to learn from them and '\n", " 'implement strategies to avoid future transgressions.\\n'\n", " '\\n'\n", " '6. Focus on self-improvement and personal growth: To overcome this difficult '\n", " 'moment, Jordan may want to prioritize self-improvement and personal growth. '\n", " 'This could involve setting goals, pursuing new hobbies, or developing '\n", " 'healthier coping mechanisms and habits.\\n'\n", " '\\n'\n", " '7. Seek positive influences and support: To help stay on the right path, '\n", " 'Jordan will want to surround themselves with positive and supportive people '\n", " 'who encourage and promote self-improvement and growth.\\n'\n", " '\\n'\n", " '8. Rebuild trust and reputation: Jordan might need to work hard to rebuild '\n", " 'trust with those affected by the event and, in a broader scope, their '\n", " 'reputation within the community. This will involve demonstrating sincerity, '\n", " 'commitment to change and consistent positive behavior that will help '\n", " 'alleviate any doubts or concerns.\\n'\n", " '\\n'\n", " '9. Move forward: Finally, Jordan will want to focus on moving forward and '\n", " 'not dwelling on past mistakes. By embracing the lessons learned and '\n", " 'committing to a better future, Jordan can use this experience as a turning '\n", " 'point.\\n'\n", " '\\n'\n", " 'In conclusion, after a night in jail for a regrettable event, Jordan will '\n", " 'want to reflect on the experience, take responsibility, seek help, make '\n", " 'amends, learn from it, focus on self-improvement, seek positive influences, '\n", " 'rebuild trust, and, most importantly, move forward.\\n'\n", " '<|im_end|>\\n')\n", "('<|im_start|>assistant\\n'\n", " ' As an AI assistant, I cannot provide a definitive answer to what Jordan may '\n", " 'want to do next, as this is a highly personal and subjective matter. '\n", " 'However, based on the information provided, it is possible that Jordan may '\n", " 'be experiencing a range of emotions, including regret, shame, and '\n", " 'potentially even depression.\\n'\n", " '\\n'\n", " 'If Jordan has spent a night in jail and is feeling overwhelmed and regretful '\n", " 'about their actions, it may be helpful for them to seek support from a '\n", " 'trusted friend, family member, or mental health professional. Talking '\n", " 'through their feelings and experiences with someone who cares about them can '\n", " 'provide a safe and non-judgmental space for Jordan to process their emotions '\n", " 'and gain a new perspective on their situation.\\n'\n", " '\\n'\n", " 'In addition to seeking support from others, Jordan may also want to consider '\n", " 'taking some time for self-care and self-reflection. This could involve '\n", " 'engaging in activities that bring them comfort and joy, such as exercise, '\n", " 'hobbies, or spending time in nature. It may also be helpful for Jordan to '\n", " 'reflect on the events that led to their night in jail, and to consider how '\n", " 'they can learn and grow from this experience.\\n'\n", " '\\n'\n", " \"It is important to note that Jordan's feelings and actions may be influenced \"\n", " 'by a range of factors, including their personal values, beliefs, and '\n", " 'experiences. Therefore, it is not appropriate to make assumptions about what '\n", " 'Jordan may want to do next, or to try to prescribe specific actions or '\n", " 'outcomes for them. Instead, it is important to offer support and '\n", " \"understanding, and to respect Jordan's autonomy and decision-making \"\n", " 'capacity.\\n'\n", " '\\n'\n", " 'In summary, if Jordan has spent a night in jail and is feeling regretful and '\n", " 'overwhelmed, it may be helpful for them to seek support from trusted others, '\n", " 'engage in self-care and self-reflection, and to take the time to process '\n", " 'their emotions and consider their next steps.\\n'\n", " '<|im_end|>\\n')\n" ] } ], "source": [ "import pprint\n", "row = raw_datasets[\"train\"][12]\n", "pprint.pprint(row[\"prompt\"])\n", "pprint.pprint(row[\"chosen\"])\n", "pprint.pprint(row[\"rejected\"])" ] }, { "cell_type": "markdown", "metadata": { "id": "86wyNoeMj-Ph" }, "source": [ "We now add LoRA adapters so we only need to update 1 to 10% of all parameters!" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "6bZsfBuZDeCL", "outputId": "17582795-e4db-49cd-97b6-a812822397ba" }, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "Unsloth 2024.2 patched 32 layers with 32 QKV layers, 32 O layers and 32 MLP layers.\n" ] } ], "source": [ "model = FastLanguageModel.get_peft_model(\n", " model,\n", " r = 64, # 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 = 64,\n", " lora_dropout = 0, # Currently only supports dropout = 0\n", " bias = \"none\", # Currently only supports bias = \"none\"\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", "metadata": { "id": "-kyd_iyz7DUM" }, "source": [ "\n", "### Train the DPO model\n", "Now let's use Huggingface TRL's `DPOTrainer`! More docs here: [TRL DPO docs](https://huggingface.co/docs/trl/dpo_trainer). We do 3 epochs on 0.5% of the dataset to speed things up." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "id": "v-2BFpDWzo1K" }, "outputs": [], "source": [ "# One must patch the DPO Trainer first!\n", "from unsloth import PatchDPOTrainer\n", "PatchDPOTrainer()" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 105, "referenced_widgets": [ "aedc288abcb34895a92758b2c9bb12c4", "70ff5e63e755438a9283be416fb4e854", "81b8d694d6c54441b29fbe966ab41c3e", "5848a81b58bf41c2abed34a9b35ca279", "d388b6c7f048475da0d6d57a29527d51", "1bb61febeb9a4120a3543d093f6d69ff", "2cbe49a4b9ba4739ae61b90ae8f3b7d2", "3f81157258e840a3b7d45b4ff4a28434", "f807b3aff3ab4ace95ae1e9689431a66", "d9b91ebe401349b48a823c6d587c2fa9", "5813f0339f0840e2ac056595f0cb233f" ] }, "id": "QtoqUw80QDV0", "outputId": "797d8cb0-f208-4684-b7e9-608cff843b7d" }, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.10/dist-packages/trl/trainer/dpo_trainer.py:314: UserWarning: When using DPODataCollatorWithPadding, you should set `remove_unused_columns=False` in your TrainingArguments we have set it for you, but you should do it yourself in the future.\n", " warnings.warn(\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "Map: 0%| | 0/11375 [00:00\n", " \n", " \n", " [ 736/4266 40:32 < 3:14:57, 0.30 it/s, Epoch 0.52/3]\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " 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StepTraining Lossrewards / chosenrewards / rejectedrewards / accuraciesrewards / marginslogps / rejectedlogps / chosenlogits / rejectedlogits / chosen
10.6931000.0000000.0000000.0000000.000000-236.120392-271.506989-1.517635-1.551257
20.6931000.0000000.0000000.0000000.000000-262.681091-480.255249-1.820580-1.751714
30.689000-0.007941-0.0168630.3750000.008922-343.534882-462.854095-1.439318-1.921890
40.676300-0.002009-0.0366960.7500000.034687-371.932007-498.821594-1.883607-1.992172
50.6931000.0062390.0059200.5000000.000318-262.281647-225.360443-1.668800-1.711984
60.701700-0.0088200.0080650.125000-0.016885-208.885910-108.879181-1.174132-1.700605
70.700400-0.0119480.0022530.125000-0.014201-102.469345-88.136703-1.767419-1.987599
80.697300-0.0029750.0052580.125000-0.008232-173.508270-58.528820-1.735717-2.406044
90.6984000.0050210.0152590.250000-0.010238-186.834366-188.345306-2.021888-2.009934
100.6892000.0086520.0004820.6250000.008170-289.465149-253.412430-1.599740-1.682284
110.6847000.011624-0.0056740.5000000.017298-162.076416-95.131889-1.288994-1.831110
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StepTraining Lossrewards / chosenrewards / rejectedrewards / accuraciesrewards / marginslogps / rejectedlogps / chosenlogits / rejectedlogits / chosen
10.6931000.0000000.0000000.0000000.000000-236.120392-271.506989-1.517635-1.551257
20.6931000.0000000.0000000.0000000.000000-262.681091-480.255249-1.820580-1.751714
30.689000-0.007941-0.0168630.3750000.008922-343.534882-462.854095-1.439318-1.921890
40.676300-0.002009-0.0366960.7500000.034687-371.932007-498.821594-1.883607-1.992172
50.6931000.0062390.0059200.5000000.000318-262.281647-225.360443-1.668800-1.711984
60.701700-0.0088200.0080650.125000-0.016885-208.885910-108.879181-1.174132-1.700605
70.700400-0.0119480.0022530.125000-0.014201-102.469345-88.136703-1.767419-1.987599
80.697300-0.0029750.0052580.125000-0.008232-173.508270-58.528820-1.735717-2.406044
90.6984000.0050210.0152590.250000-0.010238-186.834366-188.345306-2.021888-2.009934
100.6892000.0086520.0004820.6250000.008170-289.465149-253.412430-1.599740-1.682284
110.6847000.011624-0.0056740.5000000.017298-162.076416-95.131889-1.288994-1.831110
120.700000-0.0029040.0104540.125000-0.013358-298.587616-204.652374-1.566125-1.714261
130.6881000.000136-0.0102160.3750000.010352-283.442810-267.802551-1.567175-1.878443
140.6890000.0266270.0181170.5000000.008510-215.832458-284.664337-1.341850-1.278509
150.6827000.006666-0.0149380.2500000.021604-252.292831-301.594574-1.672732-1.593556
160.698600-0.013311-0.0026180.250000-0.010693-194.177597-99.114143-2.108666-2.366584
170.6866000.007993-0.0054380.2500000.013431-260.740936-167.931091-1.991597-2.320424
180.701900-0.022228-0.0051000.125000-0.017128-298.925629-169.974457-1.565908-2.094854
190.693800-0.010274-0.0096370.125000-0.000637-280.641174-253.316452-1.985314-2.060270
200.703300-0.0088470.0111530.000000-0.020000-181.764877-145.259811-1.754299-1.863950
210.6928000.0021520.0013360.2500000.000817-106.865776-51.206413-1.789710-2.068641
220.707400-0.0138440.0131500.375000-0.026994-470.079407-439.226349-1.403394-1.729417
230.6918000.0075120.0048750.2500000.002636-110.926682-141.787003-2.003387-2.155724
240.6963000.0000560.0061620.375000-0.006106-189.278320-91.860397-1.715267-2.273682
250.6893000.007139-0.0007010.5000000.007840-286.724243-321.263641-1.727899-1.872051
260.692700-0.000517-0.0017880.5000000.001271-240.846710-164.415161-1.690321-2.010958
270.6952000.0051560.0088040.375000-0.003648-315.878479-246.752213-1.474341-1.771195
280.690500-0.017940-0.0234570.6250000.005518-298.059570-293.948395-2.115559-2.069410
290.685800-0.011812-0.0273240.3750000.015512-259.493652-379.097168-1.657813-1.808470
300.696800-0.0060580.0009680.375000-0.007026-277.998993-99.315384-1.738920-2.308569
310.6890000.0100050.0014760.2500000.008530-47.540947-63.917229-2.082304-1.996938
320.6768000.029121-0.0054700.7500000.034591-259.555420-293.244720-1.739652-1.632540
330.692200-0.003771-0.0058370.2500000.002067-244.521896-128.886139-1.787432-2.414401
340.709100-0.0160880.0149670.000000-0.031054-155.857742-160.138321-2.008265-2.128524
350.695000-0.004634-0.0014510.250000-0.003183-215.159851-428.263672-1.942928-1.986691
360.682500-0.002519-0.0241810.6250000.021662-204.212585-154.711975-1.809813-1.856939
370.682600-0.002922-0.0245010.5000000.021579-206.215454-100.704849-1.691506-2.132716
380.700900-0.0061580.0089050.125000-0.015063-217.476410-132.238205-1.838670-2.311353
390.687700-0.012267-0.0239760.3750000.011709-230.393494-224.632263-1.381155-1.554531
400.691000-0.022880-0.0272510.5000000.004371-254.094208-420.828918-1.737657-1.656346
410.6581000.053051-0.0213560.7500000.074408-309.363892-415.807007-1.515318-1.842616
420.690500-0.003329-0.0087010.2500000.005373-122.785484-50.510704-2.324293-2.592887
430.6720000.021201-0.0226500.5000000.043852-291.385406-360.771484-1.433595-1.763903
440.6713000.003120-0.0416200.6250000.044740-205.336792-93.050842-2.262110-2.421654
450.711700-0.0236190.0118280.125000-0.035447-233.052399-177.299286-2.074605-1.878920
460.679600-0.007225-0.0350510.3750000.027826-213.902161-149.381714-1.927417-1.983497
470.692700-0.012913-0.0138180.3750000.000905-243.011963-158.649139-1.425713-1.768903
480.6899000.001759-0.0063740.2500000.008134-276.528961-288.710602-1.841656-2.067850
490.691700-0.010516-0.0134550.2500000.002939-130.017899-88.291946-1.476398-1.913859
500.6823000.006574-0.0156810.3750000.022255-98.465927-113.803596-1.971512-1.783947
510.691000-0.013784-0.0182130.3750000.004429-147.965683-252.318588-1.911373-1.862006
520.685600-0.022402-0.0381130.3750000.015711-277.419159-304.930115-2.002665-2.225685
530.678000-0.018972-0.0504660.3750000.031494-338.299866-199.500198-1.484331-2.133475
540.683800-0.023112-0.0422120.5000000.019100-246.781036-171.058304-1.334141-1.742860
550.701400-0.037466-0.0229590.500000-0.014507-317.539276-283.827087-1.429317-1.747704
560.689500-0.015410-0.0229310.3750000.007521-225.767365-104.510513-2.111746-2.481040
570.6553000.002388-0.0772010.7500000.079590-425.316345-176.179993-1.661623-2.126943
580.673600-0.023105-0.0636210.5000000.040516-297.989319-213.184433-1.005709-1.706408
590.685000-0.037610-0.0545340.5000000.016924-334.973145-180.659637-1.200751-1.945647
600.687100-0.033013-0.0462790.3750000.013266-186.056152-128.669815-1.869882-1.943241
610.698100-0.031303-0.0217110.125000-0.009592-150.762360-106.511871-1.515905-1.796172
620.647400-0.011928-0.1070270.7500000.095100-444.863770-145.027664-1.476206-2.401961
630.687900-0.008028-0.0190230.2500000.010994-154.740051-80.291168-1.543637-2.030140
640.693000-0.030983-0.0313710.2500000.000388-122.127327-87.250969-1.717951-1.971698
650.697800-0.093738-0.0853820.250000-0.008356-203.184174-266.558167-1.949340-1.714988
660.686500-0.106512-0.1277870.6250000.021275-372.693604-410.177429-1.812442-1.878477
670.668000-0.055321-0.1077050.7500000.052384-371.175232-262.808655-1.647112-2.065369
680.672000-0.054395-0.0994990.5000000.045104-225.186493-138.972382-1.679452-1.943656
690.652700-0.015565-0.1007510.5000000.085186-178.767044-113.834450-1.333952-1.908584
700.653200-0.049367-0.1340440.5000000.084677-293.965088-178.561722-1.488637-1.828894
710.666900-0.060622-0.1149010.6250000.054279-319.686951-313.591705-1.816466-2.271208
720.711600-0.101982-0.0667860.125000-0.035196-149.218643-242.255676-1.687428-1.506746
730.697900-0.075071-0.0664500.125000-0.008622-123.197647-206.390381-1.867890-1.860595
740.663100-0.066435-0.1334400.5000000.067005-201.509491-144.082184-1.188462-1.496844
750.654700-0.087483-0.1694260.5000000.081944-268.890503-221.860886-1.652626-1.905312
760.693000-0.162199-0.1632570.2500000.001059-290.985382-314.186157-1.464577-1.963989
770.619300-0.046235-0.2066510.6250000.160416-328.331818-139.123016-1.115253-2.128107
780.715900-0.119681-0.0777180.125000-0.041963-152.899551-201.775436-2.086996-1.642369
790.699300-0.183351-0.1718460.250000-0.011505-338.175293-355.359863-1.833611-1.958912
800.666500-0.076995-0.1345780.2500000.057582-217.746094-177.329193-2.109440-2.133928
810.706700-0.186894-0.1660850.500000-0.020810-264.704529-281.311035-1.706792-1.568883
820.684100-0.168182-0.2008820.5000000.032700-371.374207-336.238953-1.940623-2.205400
830.672700-0.044927-0.0871980.3750000.042271-118.206680-69.061958-1.827095-2.192908
840.664000-0.027551-0.0877430.6250000.060192-158.653381-67.103638-2.259972-2.462297
850.692100-0.038046-0.0418680.3750000.003822-65.555687-90.567116-2.307132-2.205169
860.656500-0.109106-0.1929780.6250000.083872-290.912537-188.447571-1.355110-1.880497
870.678100-0.181795-0.2235170.7500000.041723-323.338348-284.712341-1.442992-1.884149
880.666100-0.096404-0.1538640.5000000.057460-155.762390-195.526031-2.043696-2.089813
890.689600-0.146248-0.1572860.3750000.011038-259.348724-241.202789-1.668242-1.863370
900.666600-0.112488-0.1683580.3750000.055870-196.788849-204.459808-1.648813-1.849201
910.693600-0.251168-0.2568790.3750000.005711-310.404846-344.444397-1.953057-1.817873
920.645500-0.075294-0.1818970.6250000.106603-144.147812-83.762932-1.829682-2.057429
930.649900-0.030005-0.1245730.3750000.094567-131.493820-59.100719-2.019924-2.354006
940.615400-0.173638-0.3447070.6250000.171068-316.999207-207.454865-1.319293-1.660376
950.638500-0.235580-0.3681830.6250000.132603-301.625916-278.704529-1.122317-1.583289
960.656600-0.273428-0.3741310.6250000.100703-352.558136-242.493027-1.454043-1.761392
970.599100-0.262022-0.4678560.7500000.205834-353.078674-344.631317-1.269219-1.767855
980.664800-0.153988-0.2433540.5000000.089365-164.382904-120.321381-2.329014-2.364781
990.598900-0.164777-0.3726290.6250000.207852-323.316132-190.704834-1.732785-2.230679
1000.670300-0.128744-0.1768000.5000000.048056-126.862305-115.158577-1.869670-1.962420
1010.653600-0.222338-0.3090900.5000000.086752-275.805817-284.733246-1.854122-2.308006
1020.626600-0.130860-0.2771500.5000000.146290-225.164246-133.780701-1.603020-1.972434
1030.609800-0.260897-0.4474770.6250000.186580-299.373871-197.653214-1.307595-1.599266
1040.583800-0.322033-0.5712810.8750000.249248-373.110901-347.672546-1.400188-1.692186
1050.678200-0.177897-0.2409520.3750000.063054-196.448380-135.587311-1.769132-1.987163
1060.586100-0.438901-0.7718930.7500000.332991-432.868073-274.419403-0.981144-1.413649
1070.658900-0.084733-0.1577870.5000000.073053-104.238358-77.226257-2.181684-2.326242
1080.683200-0.405485-0.5429320.5000000.137446-243.340057-247.628784-1.774361-2.047462
1090.513900-0.330458-0.7625970.7500000.432139-461.325989-282.712280-1.368042-2.085570
1100.599900-0.503411-0.7310580.6250000.227647-281.929169-269.653961-1.648338-1.900135
1110.611400-0.143288-0.3427140.3750000.199426-155.985931-92.810814-1.870660-2.227154
1120.661600-0.388237-0.4600500.6250000.071812-197.299774-203.227463-1.468810-1.478374
1130.673600-0.141606-0.1912520.2500000.049646-81.790298-80.900475-1.593496-1.555458
1140.603900-0.499954-0.7141950.5000000.214240-326.953796-298.265961-1.460081-1.710271
1150.588300-0.571643-0.8331550.6250000.261512-336.500061-320.806946-1.297806-1.622943
1160.682700-0.836530-1.0218870.5000000.185357-425.477417-462.473022-1.662386-2.033597
1170.664500-0.550357-0.7574730.5000000.207116-290.892029-242.824295-1.508846-1.798826
1180.596800-0.521429-0.7468180.5000000.225389-269.674408-215.050766-1.621290-1.974091
1190.662000-0.059463-0.1269520.2500000.067489-45.818260-23.634642-2.089560-2.175679
1200.695800-0.715050-0.7279040.5000000.012854-331.302124-338.201263-1.402578-1.542377
1210.630000-0.485384-0.6455600.3750000.160176-205.437729-226.092834-1.221123-1.586198
1220.796100-1.131892-1.1803570.1250000.048464-359.860748-596.330627-2.246358-2.095072
1230.663900-0.238027-0.2993180.5000000.061291-104.413490-85.794067-2.124067-2.034063
1240.683200-0.410686-0.4913990.5000000.080713-156.839584-155.750000-1.908769-2.116091
1250.674100-0.386982-0.4341520.3750000.047170-148.956650-207.613312-2.249257-2.207691
1260.677100-0.871188-0.9690190.6250000.097831-307.043213-317.177307-1.457586-1.493385
1270.791000-0.346116-0.2421550.125000-0.103961-77.748909-109.140045-2.657577-2.487037
1280.714700-0.303015-0.3320090.3750000.028994-100.185760-110.493660-2.123177-2.104714
1290.542300-0.456225-0.8996400.6250000.443415-318.691772-201.504303-1.806675-1.877434
1300.678400-0.476321-0.5128370.2500000.036516-144.487640-162.403290-1.864520-2.032990
1310.629600-0.679162-0.8985370.6250000.219374-257.898071-326.736267-1.402675-1.625062
1320.606800-0.694001-0.9645070.5000000.270506-277.650574-324.548523-1.575459-1.809268
1330.611400-0.105062-0.2928340.3750000.187772-91.567360-30.325502-2.295392-2.493996
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1350.533300-0.208979-0.6750630.5000000.466084-227.808105-104.038979-1.855530-2.126464
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42660.912700-0.922611-2.4461590.5000001.523549-254.815689-290.553101-1.009849-0.776445

" ] }, "metadata": {} } ], "source": [ "model = dpo_trainer.train()" ] }, { "cell_type": "code", "source": [ "model" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "ozZy-fTl9hIK", "outputId": "c569ff46-836a-434a-b881-7c39c2c928af" }, "execution_count": 11, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "TrainOutput(global_step=4266, training_loss=0.49829238169620044, metrics={'train_runtime': 14226.9359, 'train_samples_per_second': 2.399, 'train_steps_per_second': 0.3, 'total_flos': 0.0, 'train_loss': 0.49829238169620044, 'epoch': 3.0})" ] }, "metadata": {}, "execution_count": 11 } ] }, { "cell_type": "code", "source": [ "model1 = dpo_trainer.model" ], "metadata": { "id": "VrHRoA_Q9zIO" }, "execution_count": 12, "outputs": [] }, { "cell_type": "code", "source": [ "model1" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "3PVjRulk98fj", "outputId": "b764af49-b8ac-4fdf-e05c-9d842db6f2f2" }, "execution_count": 13, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "PeftModelForCausalLM(\n", " (base_model): LoraModel(\n", " (model): LlamaForCausalLM(\n", " (model): LlamaModel(\n", " (embed_tokens): Embedding(32000, 4096)\n", " (layers): ModuleList(\n", " (0-31): 32 x LlamaDecoderLayer(\n", " (self_attn): LlamaAttention(\n", " (q_proj): lora.Linear4bit(\n", " (base_layer): Linear4bit(in_features=4096, out_features=4096, bias=False)\n", " (lora_dropout): ModuleDict(\n", " (default): Identity()\n", " )\n", " (lora_A): ModuleDict(\n", " (default): Linear(in_features=4096, out_features=64, bias=False)\n", " )\n", " (lora_B): ModuleDict(\n", " (default): Linear(in_features=64, out_features=4096, bias=False)\n", " )\n", " (lora_embedding_A): ParameterDict()\n", " (lora_embedding_B): ParameterDict()\n", " )\n", " (k_proj): lora.Linear4bit(\n", " (base_layer): Linear4bit(in_features=4096, out_features=4096, bias=False)\n", " (lora_dropout): ModuleDict(\n", " (default): Identity()\n", " )\n", " (lora_A): ModuleDict(\n", " (default): Linear(in_features=4096, out_features=64, bias=False)\n", " )\n", " (lora_B): ModuleDict(\n", " (default): Linear(in_features=64, out_features=4096, bias=False)\n", " )\n", " (lora_embedding_A): ParameterDict()\n", " (lora_embedding_B): ParameterDict()\n", " )\n", " (v_proj): lora.Linear4bit(\n", " (base_layer): Linear4bit(in_features=4096, out_features=4096, bias=False)\n", " (lora_dropout): ModuleDict(\n", " (default): Identity()\n", " )\n", " (lora_A): ModuleDict(\n", " (default): Linear(in_features=4096, out_features=64, bias=False)\n", " )\n", " (lora_B): ModuleDict(\n", " (default): Linear(in_features=64, out_features=4096, bias=False)\n", " )\n", " (lora_embedding_A): ParameterDict()\n", " (lora_embedding_B): ParameterDict()\n", " )\n", " (o_proj): lora.Linear4bit(\n", " (base_layer): Linear4bit(in_features=4096, out_features=4096, bias=False)\n", " (lora_dropout): ModuleDict(\n", " (default): Identity()\n", " )\n", " (lora_A): ModuleDict(\n", " (default): Linear(in_features=4096, out_features=64, bias=False)\n", " )\n", " (lora_B): ModuleDict(\n", " (default): Linear(in_features=64, out_features=4096, bias=False)\n", " )\n", " (lora_embedding_A): ParameterDict()\n", " (lora_embedding_B): ParameterDict()\n", " )\n", " (rotary_emb): LlamaRotaryEmbedding()\n", " )\n", " (mlp): LlamaMLP(\n", " (gate_proj): lora.Linear4bit(\n", " (base_layer): Linear4bit(in_features=4096, out_features=11008, bias=False)\n", " (lora_dropout): ModuleDict(\n", " (default): Identity()\n", " )\n", " (lora_A): ModuleDict(\n", " (default): Linear(in_features=4096, out_features=64, bias=False)\n", " )\n", " (lora_B): ModuleDict(\n", " (default): Linear(in_features=64, out_features=11008, bias=False)\n", " )\n", " (lora_embedding_A): ParameterDict()\n", " (lora_embedding_B): ParameterDict()\n", " )\n", " (up_proj): lora.Linear4bit(\n", " (base_layer): Linear4bit(in_features=4096, out_features=11008, bias=False)\n", " (lora_dropout): ModuleDict(\n", " (default): Identity()\n", " )\n", " (lora_A): ModuleDict(\n", " (default): Linear(in_features=4096, out_features=64, bias=False)\n", " )\n", " (lora_B): ModuleDict(\n", " (default): Linear(in_features=64, out_features=11008, bias=False)\n", " )\n", " (lora_embedding_A): ParameterDict()\n", " (lora_embedding_B): ParameterDict()\n", " )\n", " (down_proj): lora.Linear4bit(\n", " (base_layer): Linear4bit(in_features=11008, out_features=4096, bias=False)\n", " (lora_dropout): ModuleDict(\n", " (default): Identity()\n", " )\n", " (lora_A): ModuleDict(\n", " (default): Linear(in_features=11008, out_features=64, bias=False)\n", " )\n", " (lora_B): ModuleDict(\n", " (default): Linear(in_features=64, out_features=4096, bias=False)\n", " )\n", " (lora_embedding_A): ParameterDict()\n", " (lora_embedding_B): ParameterDict()\n", " )\n", " (act_fn): SiLU()\n", " )\n", " (input_layernorm): LlamaRMSNorm()\n", " (post_attention_layernorm): LlamaRMSNorm()\n", " )\n", " )\n", " (norm): LlamaRMSNorm()\n", " )\n", " (lm_head): Linear(in_features=4096, out_features=32000, bias=False)\n", " )\n", " )\n", ")" ] }, "metadata": {}, "execution_count": 13 } ] }, { "cell_type": "code", "source": [ "from google.colab import userdata\n", "hf_token = userdata.get('hf')\n", "!huggingface-cli login --token $hf_token" ], "metadata": { "id": "yh5-hRZmka8u", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "778caef1-a1e2-4e77-a447-fdf9a97028c5" }, "execution_count": 14, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Token will not been saved to git credential helper. Pass `add_to_git_credential=True` if you want to set the git credential as well.\n", "Token is valid (permission: write).\n", "Your token has been saved to /root/.cache/huggingface/token\n", "Login successful\n" ] } ] }, { "cell_type": "code", "source": [ "model1.push_to_hub(\"Technoculture/MedMerge-6-7b-alpha-dpo\", token = hf_token, )" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 120, "referenced_widgets": [ "119d3d78e7ff45aca9600ee30fdcc413", "4d71e94774a743f1b359ea744aca128c", "f74297dc23174098b334d959b3c60a06", "4328456c9f9a4915bf4ea0db6518b13f", "634e9dfda1cf43b98f733723c3f8e57b", "c7beb216e8a549008d0f1eef30f12d01", "c8eadf362a6942528d9f3ea7aa70c8af", "51ff54308b424abdacc93cd3e1629799", "d2aeb4e9cccd41079216dcd8f2c8c69e", "b7c901f34d7e47e09b439d3ee8fd49cd", "4849a468274847efb2b78588f8796e43" ] }, "id": "XJeWaRKXRgMq", "outputId": "b4d0ce24-1823-410b-b603-b12f958169c8" }, "execution_count": 15, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "adapter_model.safetensors: 0%| | 0.00/640M [00:00\n", "

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losslearning_raterewards/chosenrewards/rejectedrewards/accuraciesrewards/marginslogps/rejectedlogps/chosenlogits/rejectedlogits/chosenepochsteptrain_runtimetrain_samples_per_secondtrain_steps_per_secondtotal_flostrain_loss
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30.67634.683841e-08-0.002009-0.0366960.7500.034687-371.932007-498.821594-1.883607-1.9921720.04NaNNaNNaNNaNNaN
40.69315.854801e-080.0062390.0059200.5000.000318-262.281647-225.360443-1.668800-1.7119840.05NaNNaNNaNNaNNaN
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\n" }, "metadata": {} } ] }, { "cell_type": "code", "source": [ "smoothed_loss = df['loss'].rolling(window=100).mean()\n", "plt.figure(figsize=(25, 10))\n", "plt.plot(df.index, smoothed_loss, color='red', label='Smoothed Loss')\n", "plt.title('Training Loss Smoothed (window = 100)')\n", "plt.xlabel('Step')\n", "plt.ylabel('Loss')\n", "plt.legend()\n", "plt.grid(True)\n", "plt.show()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 374 }, "id": "0HnVlp_0TzuG", "outputId": "3726ff37-5b67-488a-eb25-9a8851f4f427" }, "execution_count": 19, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": 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}, "metadata": {} } ] }, { "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. Mistral 7b 2x faster [free Colab](https://colab.research.google.com/drive/1Dyauq4kTZoLewQ1cApceUQVNcnnNTzg_?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. Llama 7b [free Kaggle](https://www.kaggle.com/danielhanchen/unsloth-alpaca-t4-ddp)\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", "\n", "
\n", " \n", " \n", " Support our work if you can! Thanks!\n", "
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