{ "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": null, "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": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "E8-BWi7MzkRz", "outputId": "bd452125-61e2-4ba2-c175-a3edbb6f0c0b" }, "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": null, "metadata": { "id": "QmUBVEnvCDJv", "colab": { "base_uri": "https://localhost:8080/", "height": 562, "referenced_widgets": [ "1f97e1b9c70b45fb85bcbc275f052d3e", "b29c909a86c64a018bde0869df5eda01", "6da80d20ee3746afa6e0a3b2a8eeccad", "97a4c453e86a40d3bef6d3d16d4c9eb1", 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"66eef789197a4df782fc2038645e010e", "41e81423ce71410f8bce73654ae03e36", "4532f09c1cee49de85c7fef09ddad541", "070f42ac4fb34d1ab6a1f1cc248b8a77", "57bc8c7eb63a476fa70b651a57a79a2d", "b7cdf3e14464478b892105c8d8fb4b83", "c2c30bba38014cb29f2b48a6c5897c15", "08457657f23e456fa4a39e2ff84d5561", "57e260d588584665b1305fbe21765131", "066f7ecbb1364733ba07130ee8a8b7ad", "a27361f5e70c4bac8aa51f4e69f6d9a6", "43526b18e92f46ae8522bbc35ecf8097", "7733c667b2fe464b827acb3f880cef29", "6e59dc42ee734badbad66b88bbc7c344", "e68cf13d40e74575846e8537e880dbe4", "2d8d23f259e040318a953af7dd682b45", "50742eab88844f648e33cacc75876f15", "42655b3288d14fe280062de5601640ae", "3367d5553d6f4a72bdee984b1a287656", "0fb100b750e6453c801c28c3cb0f2115", "6f35722683114977ac5b23c9c805eb2e", "05263398a230441a9b2c3d97189f80a0", "3b27946663b94c28b9a0bdb5dadd7423", "6f294f7b03da4c068c2f0b1fc760c81a", "5120ff0a8c7e44ebb0a20baea45b33b6", "94db1c7762df4b2696c46cdb251e2448", "e53999d680ef49ac9f0908f59f993d97", "11c03bad185548af8a0928b258a220a0", "cc38038e8eea41e094b32b74713d0ec7", "a279ccb38784436bbecb5832079871d3", "133150ef46e8484db0c30eaeedb9f7df", "bada6f358f6f429486d8badf05c03aa1", "ab5b4ee3205e497f83b13148be3b9149", "fb31283a99644dbb87a7ed188f7631ff", "b86f3f1dfc2f4c25ae17d6bed9bf0ed1", "a8ef257e38fd4cffa0d5aefbf1f263c1", "7c129bf653e746b5a4f47caab21a835a", "cfa5185ed2ed44e0b118b69b85849625", "58d8d5805503467ea6b1c834f324f513", "ce859295af6b47ebb7984289d829364b", "cfc3a950613046c6ae051f40be0f3dba", "64b881ad489e469683e4207526535ab8", "7ede2eedfac14f0b9fef6c0010370a3a", "a179720aa1ce4da8b7bfaf7cf456927b", "a03d66ecd8ea47f38fd6fee117a2be4a", "0ea43ab24b854069ab3b304b38709cb1", "a8e24a61c4d64ad9983a4fafda866c6c", "7d39af595fda4473aad51a0059bb5bba", "12a7d0625c3549c5881faf8e31a49660", "e5fa3a69709944adade39a9a5a3817d9", "4cea6a7cf04c48c299710f14237beaab", "02242c460b0d4e989ab3cb97ab8de910", "0d6ba8652f794114bc58164f90df85bd", "9e9d4bb8cd4743559a19bc611b22ade9", "db8a94b917054b98b634e3854935e777", "dbce38d8fa0e4ee4978847e9ed1aa83f", "9171c5cbaf3745e58adc5b5c93f6e1c6", "9292fa02e5604f62aeb49c9304c6407c", "73dfdaca314b4bb2a4999e27aea092b6", "365a9cc126d6478d96bd73f4e69529f2", "0e49ca1665f94a8e94404092f75c544a", "7eb2776fad8540cba6da4b241cd56259", "1574a49d594a4ab69013e8ab92a43c68", "af0d15dba9744505871a1ead96e0c207", "321dfce22e1341f0b6bc59e38c9addc9", "e41b6c81cbc744f593a8007c1cba662f", "2b92fb0fef864d9ab0a4472ad6d48636", "db632dcdb5ed4968963a2c044a056e97", "f9e514cd9ff646dab1a6b6ee3e511ca8", "70f29f030d974094bdc1e9f3fd0d2912", "1655e685275c460391d2d2893a231219", "4274f1b2ecb446f587ee5a39daef97b0", "05b7b5980b804ab69edb13d718f22a69", "32610ee101654c399668b3406309739f" ] }, "outputId": "c5094b88-4111-4459-e8e4-7c053ae90316" }, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "config.json: 0%| | 0.00/687 [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": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 433, "referenced_widgets": [ "aa58892ec2a24fcc9cf49396400944cb", "500693e1f93c4f0e93e1e1f3e7ec7716", "0e6341ae83a44a288a9800ea104ee0ca", "fc6cc19a26f748b28647504df2835274", "a97cc827f1064f82b7fc29731587633a", "1f3046cf533c48f48d7673fa9ab8b510", "74454479e4cc42ba982fb6ba7f10666f", "97d27d3229f141b7ab35d90673da3d0d", "08c2f332cd57476f84a721edc8b8681a", "79a82a46c22b449f9b859cdabde9ceb6", "bf5c85efc1434ff99d31825ac982194d", "c2e21efac8cc48099f6d07462832ba8f", "70e12b38aaaf4e3abd40f54b1323bca5", "00adafde39a84a4d8c4cff52cb723aa6", "6b1de41162424ba59535dafd7ba3d779", "3410b7080d43406eb769cf791b8a1dc0", "dbfb98082db44f6787d600ee4b03fd37", "e7034562e6ae4db9a39892d35637e5e7", "294ad68004164041a06afd074d340f34", "f59dcb0f9ce7433f82cf7a149b750efc", "942d708433624014bcf29d926e7bb0fe", "f64f15c4f0e14c19ade0df3f1cb430c9", 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a random item from the dataset" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "oF63zQqNlNJC", "outputId": "42a3a748-f39e-4876-e166-e90ee6cbd097" }, "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": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "6bZsfBuZDeCL", "outputId": "fa4b8baf-b4fc-4bae-8467-86da69188c30" }, "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": null, "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": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 105, "referenced_widgets": [ "2bcba83341574765b4f51812baaac949", "6a5451e2bd3f4093bfa3d50933a55677", "1cbc131a86b042bbaca1ff474a54863a", "957f28b2cd734e268a2808e0d3b16e26", "aa898ad8803345bbb3b0752228afa506", "7fb7391014fb496992ba5a813f39970b", "51ff712789a941098210afc857ddfe8c", "ea5472e269c448859120220eb286ec9c", "4730b7bb98fd49d686d81812b3d74f6a", "df15e3347afb41c2a250d736218b820a", "06e81bc48cbd4168bd365c81e86eeb4c" ] }, "id": "QtoqUw80QDV0", "outputId": "702b9058-e17d-4569-8f63-43de03f09fe5" }, "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" ], "text/html": [ "\n", "
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StepTraining Lossrewards / chosenrewards / rejectedrewards / accuraciesrewards / marginslogps / rejectedlogps / chosenlogits / rejectedlogits / chosen
10.6931000.0000000.0000000.0000000.000000-172.062836-178.390350-1.646064-1.653886
20.6931000.0000000.0000000.0000000.000000-191.147125-324.398499-1.662246-1.690201
30.688800-0.010748-0.0205280.3750000.009780-250.792435-291.157349-1.566861-1.642228
40.6804000.008985-0.0176770.5000000.026662-268.217163-365.586609-1.720513-1.614048
50.706400-0.0160050.0093590.125000-0.025364-191.802887-166.006714-1.709749-1.757837
60.6893000.0273490.0190840.3750000.008265-168.309097-89.989273-1.394496-1.616924
70.6822000.008974-0.0135250.5000000.022499-89.057358-70.771759-1.623334-1.767092
80.6966000.0018760.0085660.250000-0.006689-134.975281-55.301453-1.605695-1.747033
90.6800000.012498-0.0145750.5000000.027074-133.304245-118.602829-1.800967-1.800762
100.6889000.009064-0.0003680.6250000.009432-209.245789-156.965012-1.665605-1.697382
110.6883000.0122360.0021130.5000000.010123-136.648514-87.441132-1.669984-1.828278
120.688400-0.011154-0.0211680.2500000.010014-179.227509-147.174042-1.659540-1.728266
130.6804000.020158-0.0058540.5000000.026012-210.068573-189.834961-1.548160-1.690681
140.722800-0.0370560.0204100.250000-0.057466-164.691971-197.795853-1.602406-1.533207
150.6760000.024730-0.0115340.2500000.036264-174.971878-194.063416-1.571296-1.540136
160.695000-0.006095-0.0028950.500000-0.003200-126.687737-71.229401-1.678308-1.741515
170.678100-0.003574-0.0344010.5000000.030827-187.025986-109.309845-1.646213-1.771547
180.6854000.0231640.0063030.5000000.016860-221.268967-119.521484-1.354111-1.435570
190.6929000.0022170.0015670.5000000.000650-201.210693-151.956940-1.682772-1.699662
200.6710000.039321-0.0058320.8750000.045153-141.809601-115.696053-1.718660-1.749636
210.690100-0.007914-0.0140970.2500000.006183-78.438965-33.538116-1.665504-1.821263
220.713000-0.0089920.0290650.250000-0.038056-315.776367-268.983734-1.739412-1.761542
230.696100-0.008715-0.0032590.250000-0.005456-85.940468-112.857117-1.676049-1.731551
240.7058000.0022590.0261900.500000-0.023931-147.698944-68.444839-1.679932-1.680524
250.698000-0.019621-0.0102130.250000-0.009408-196.696716-217.262604-1.934222-1.944539
260.6896000.0110110.0034840.3750000.007526-172.690369-116.405472-1.746984-1.856682
270.6915000.0097860.0060030.2500000.003782-227.404572-179.602875-1.630332-1.796793
280.6816000.0266020.0025690.5000000.024032-207.215271-197.887939-1.582958-1.590803
290.708800-0.0298700.0001750.375000-0.030044-185.163803-212.278397-1.657916-1.635438
300.6941000.0079030.0095800.250000-0.001677-218.613922-77.711510-1.803937-2.053845
310.694400-0.0001530.0023360.125000-0.002490-43.155174-56.543617-1.843162-1.765702
320.697400-0.011429-0.0037140.375000-0.007716-193.528305-214.576614-1.742684-1.732372
330.692800-0.008236-0.0095230.3750000.001286-156.506927-90.624130-1.639076-1.878650
340.6881000.009541-0.0008310.5000000.010371-118.951454-109.241058-1.640622-1.699749
350.692200-0.016157-0.0182230.3750000.002066-151.710022-266.537445-1.589978-1.527948
360.697900-0.013202-0.0051820.375000-0.008020-138.025040-112.756744-1.632450-1.669280
370.682400-0.006740-0.0291620.5000000.022422-144.873352-79.237534-1.830550-1.948524
380.694000-0.004473-0.0028950.500000-0.001578-162.765060-104.380676-1.610659-1.903133
390.6915000.0107920.0064040.6250000.004387-194.825226-179.301453-1.472502-1.608745
400.694200-0.017148-0.0161500.375000-0.000998-175.471832-277.874512-1.831391-1.650784
410.714900-0.0146290.0276170.250000-0.042246-228.208008-249.077286-1.778710-1.776061
420.685400-0.008176-0.0238820.2500000.015706-88.931664-39.048317-1.534191-1.628754
430.6900000.0116630.0050620.3750000.006601-209.746399-256.175385-1.541801-1.603356
440.6842000.000158-0.0183530.6250000.018512-151.971588-73.928558-1.591548-1.718430
450.698200-0.014932-0.0055750.250000-0.009358-161.698990-125.457832-1.764796-1.643935
460.6850000.005999-0.0110230.6250000.017022-148.931503-93.947708-1.730049-1.770324
470.7146000.0151130.0545520.125000-0.039439-168.882889-130.256683-1.771551-1.797976
480.6943000.0057830.0078220.125000-0.002039-176.619095-186.371033-1.644471-1.663225
490.6857000.004200-0.0110380.3750000.015238-90.677567-72.256729-1.660511-1.791634
500.694100-0.014218-0.0124950.250000-0.001722-69.804626-81.563904-1.668860-1.659535
510.6935000.0119150.0108800.2500000.001036-104.264732-172.760910-1.780317-1.658186
520.696300-0.0003760.0057450.250000-0.006121-193.507538-196.115173-1.600473-1.589134
530.698800-0.012769-0.0021400.250000-0.010629-239.809036-124.849449-1.446959-1.537223
540.6856000.007419-0.0086480.5000000.016067-170.380249-116.507309-1.614149-1.621376
550.669600-0.003287-0.0513200.8750000.048034-223.089676-208.370209-1.725162-1.813332
560.6717000.002034-0.0434810.3750000.045515-165.002563-82.849907-1.587736-1.675771
570.698300-0.0083580.0012540.500000-0.009612-282.312286-124.904099-1.744009-1.918866
580.6907000.0151100.0098950.5000000.005215-216.715637-144.941299-1.530328-1.717937
590.646300-0.004282-0.1060610.6250000.101779-249.226517-126.370255-1.411061-1.704296
600.687200-0.005976-0.0184810.5000000.012505-137.375092-93.663582-1.869054-1.956205
610.692300-0.000181-0.0020930.6250000.001912-115.239838-78.241684-1.698221-1.755788
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2230.472200-1.671093-2.9204470.8750001.249355-328.134674-340.353577-1.589670-1.568521
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" ] }, "metadata": {} } ], "source": [ "model = dpo_trainer.train()" ] }, { "cell_type": "code", "source": [ "model" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "ozZy-fTl9hIK", "outputId": "fd02874e-d61a-4f55-dde5-10d6f090bdaf" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "TrainOutput(global_step=4266, training_loss=0.4691216720135396, metrics={'train_runtime': 14402.2434, 'train_samples_per_second': 2.369, 'train_steps_per_second': 0.296, 'total_flos': 0.0, 'train_loss': 0.4691216720135396, 'epoch': 3.0})" ] }, "metadata": {}, "execution_count": 11 } ] }, { "cell_type": "code", "source": [ "model1 = dpo_trainer.model" ], "metadata": { "id": "VrHRoA_Q9zIO" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "model1" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "3PVjRulk98fj", "outputId": "c378b430-0c4d-4888-b59b-3c021ca628d6" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "PeftModelForCausalLM(\n", " (base_model): LoraModel(\n", " (model): LlamaForCausalLM(\n", " (model): LlamaModel(\n", " (embed_tokens): Embedding(32017, 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): LlamaLinearScalingRotaryEmbedding()\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=32017, 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": "be3da7f9-51a6-4538-d1b1-29bcfde0f2ef" }, "execution_count": null, "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/MT7Bi-alpha-dpo-v0.2\", token = hf_token, )" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 117, "referenced_widgets": [ "b54ec12f1ebc48249c462f19708ddf8e", "67fc51904fb74bb9a9c49d3e080ed666", "b224a65fbf874128937ae16cc24eac0f", "a5c377cf3be14a0db7ae1a341bf5fcac", "24f6a7448642422b8ff8888a385e82f3", "9cd6017074e64015b488d4f5700907ea", "5c6aea35cd7c4f7eb6f89145bb6c8dd4", "c24afd57341342abbda1ad8871cd9be1", "8a7d45c1a3044d859269f7d706b38225", "044ad55cf7594979ad3de1873aaa5f08", "a8c45a3c23f94dcb974dc2ddf5893b58", "2ad7373ac4d944dabcabfeb9dd452800", "59ef05fdaf294c6eb60bd9067f28a3d6", "6a48057e42dc45909f73e296be645cf1", "51d3a6369abc4ff09728d57660d27acc", "36ec9ba2171a476eb281a31de47f2074", "d1d2942e88ff4df998a4a5cbcf43c44f", "edbaf4e1250d4461925d6eb2e5239541", "3abb10ac55be4bfbbfe82ff7b383669e", "37e5aabf5ad74320893ccf195289ca53", "c06324dc28d449f29168919150c54cb1", "3dedc9a6659849a5bcd9ca6c1353185c" ] }, "id": "XJeWaRKXRgMq", "outputId": "b68cec05-bc95-4ca8-cf46-6ec6cd9b44c0" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "README.md: 0%| | 0.00/21.0 [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
00.69311.170960e-080.0000000.0000000.0000.000000-172.062836-178.390350-1.646064-1.6538860.01NaNNaNNaNNaNNaN
10.69312.341920e-080.0000000.0000000.0000.000000-191.147125-324.398499-1.662246-1.6902010.02NaNNaNNaNNaNNaN
20.68883.512881e-08-0.010748-0.0205280.3750.009780-250.792435-291.157349-1.566861-1.6422280.03NaNNaNNaNNaNNaN
30.68044.683841e-080.008985-0.0176770.5000.026662-268.217163-365.586609-1.720513-1.6140480.04NaNNaNNaNNaNNaN
40.70645.854801e-08-0.0160050.0093590.125-0.025364-191.802887-166.006714-1.709749-1.7578370.05NaNNaNNaNNaNNaN
......................................................
42620.43503.907268e-09-2.407023-4.6996770.3752.292654-89.597084-58.371151-1.540244-1.5137713.04263NaNNaNNaNNaNNaN
42630.34742.604845e-09-2.779045-6.8094830.5004.030438-236.978180-178.128052-1.458513-1.5031693.04264NaNNaNNaNNaNNaN
42640.52881.302423e-09-5.662349-9.4512730.7503.788924-317.740051-245.170883-1.702635-1.7606203.04265NaNNaNNaNNaNNaN
42651.12660.000000e+00-4.381310-6.7796190.5002.398309-238.000000-226.756332-1.379322-1.1906523.04266NaNNaNNaNNaNNaN
4266NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN3.0426614402.24342.3690.2960.00.469122
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\n", " \n" ] }, "metadata": {}, "execution_count": 17 } ] }, { "cell_type": "code", "source": [ "import matplotlib.pyplot as plt\n", "plt.figure(figsize=(25, 10))\n", "plt.plot(df.index, df['loss'], color='blue')\n", "plt.title('Training Loss')\n", "plt.xlabel('Step')\n", "plt.ylabel('Loss')\n", "plt.grid(True)\n", "plt.show()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 608 }, "id": "8inaBtD_Tgzf", "outputId": "fcb6d3e9-261f-498e-9ee8-38220363415e" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
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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": 605 }, "id": "0HnVlp_0TzuG", "outputId": "5eb129a4-4f32-4695-fcfb-93ff322dc7f1" }, "execution_count": null, "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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