{ "source_file": "0930", "source_platform": "Medium", "author": "John Adeojo", "title_listed": "Fine-Tune Your LLM Without Maxing Out Your GPU", "keywords": "", "domain": "大模型算法", "content_type": "深度技术解析", "is_paid_flag": "否", "url": "https://towardsdatascience.com/fine-tune-your-llm-without-maxing-out-your-gpu-db2278603d78/", "publish_date": "2023-08-01", "corpus_stage": "", "notes": "", "id": "da0d28d12cd2", "collected_at": "2026-10-02T06:50:03", "title": "Fine-Tune Your LLM Without Maxing Out Your GPU | Towards Data Science", "body_markdown": "![Revisit consent button](../images/body-df29beacd7496220cc94.svg)\nWe value your privacy\nWe use cookies to enhance your browsing experience, serve personalised ads or content, and analyse our traffic. 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As these models are so large, it has been challenging for businesses, researchers, or hobbyists with a modest budget to customise them for their own datasets.\nNow with innovations in parameter efficient fine-tuning (PEFT) methods, it is entirely possible to fine-tune large language models at a relatively low cost. In this article, I demonstrate how to achieve this in a [Google Colab](https://colab.research.google.com/).\nI anticipate that this article will prove valuable for practitioners, hobbyists, learners, and even hands-on start-up founders.\nSo, if you need to mock up a cheap prototype, test an idea, or create a cool data science project to stand out from the crowd - keep reading.\n* * *\nLearn this step by step with the interactive [AI Engineer](https://roadmap.sh/ai-engineer), [AI and Data Scientist](https://roadmap.sh/ai-data-scientist) and [Machine Learning](https://roadmap.sh/machine-learning) roadmaps.\n## Why Do We Fine-tune?\nBusinesses often have private datasets that drive some of their processes.\nTo give you an example, I worked for a bank where we logged customer complaints in an Excel spreadsheet. An analyst was responsible for categorising these complaints (manually) for reporting purposes. Dealing with thousands of complaints each month, this process was time-consuming and prone to human error.\nHad we had the resources, we could have fine-tuned a large language model to carry out this categorisation for us, saving time through automation and potentially reducing the rate of incorrect categorisations.\nInspired by this example, the remainder of this article demonstrates how we can fine-tune an LLM for categorising consumer complaints about financial products and services.\n* * *\n## The Dataset\nThe dataset comprises real consumer complaints data for financial services and products. It is open, publicly available data published by the [Consumer Financial Protection Bureau](https://www.consumerfinance.gov/complaint/data-use/).\nThere are over 120k anonymised complaints, categorised into approximately 214 \"subissues\".\nI have a version of the [dataset](https://huggingface.co/datasets/JAdeojo/consumer_complaints_cfpb) on my hugging face page that you can explore for yourself.\n* * *\n## The Hardware\nThe hardware I used for training was a V100 GPU with 16 GB of RAM, accessed via Google Colab. This is a relatively inexpensive and accessible infrastructure, available for rent via Google Colab Pro at approximately 9.99 USD per 100 compute units.\n* * *\n## The Large Language Model\nThe LLM used is RoBERTa¹ (XLM), which has approximately 563 million parameters. An overview of the model and its specification can be found [here](https://huggingface.co/xlm-roberta-large).\nThough not the largest model currently available, RoBERTa still presents a demanding workload for those with access only to small-scale infrastructure. This makes it an ideal choice to demonstrate that training a relatively large model on small-scale infrastructure is feasible.\n_Note - RoBERTa is a pre-trained model pulled from the_[ _Hugging Face Hub_](https://huggingface.co/) _._\n* * *\n## Fine-tuning at Low Cost with LoRA\nAs stated in the introduction, PEFT methods have made it possible to fine-tune LLMs at a low cost. One such method is LoRA, which stands for Low-Rank Adaptations of large language models.\nAt a high level, LoRA accomplishes two things. First, it freezes the existing weights of the LLM (rendering them non-trainable); second, it injects trainable \"lower-dimensional\" layers into specified layers of the architecture.\nThis technique yields a model with far fewer trainable parameters while still preserving performance. LoRA has been shown to reduce GPU memory consumption by a factor of three compared to standard fine-tuning.\nFor further details on LoRA, please read the full [paper](https://arxiv.org/pdf/2106.09685.pdf).\n* * *\n## Technical Details\nIn the past, the key challenge for training large language models on limited hardware was adapting the training parameters to prevent the process from crashing due to exceeding your GPU's memory capacity.\nWith LoRA, one can push the boundaries of their hardware with just a few adjustments.\n### Applying LoRA\nAssuming you have your dataset prepared, the first thing you need to do is set your LoRA configurations.\n * **task_type** - The task for which you're fine-tuning the model. For complaints classification, we are focusing on sequence classification.\n * **r** - A hyperparameter named the LoRA attention dimension which affects the scaling.\n * **lora_alpha** - Another hyperparameter that impacts the scaling.\n * **target_modules** - Here we specify that the transformation should be applied to the attention modules in our transformer, hence we set this to \"query\" and \"value\".\n * **lora_dropout** - The dropout probability for the LoRA layers.\n * **bias** - The bias type for LoRA.\n * **modules_to_save** - Excluding the LoRA layers, we declare which layers of our model we wish to make trainable and save at the final checkpoint. For our purposes, we require the classification head to be trainable.\n\n\nThe next part of the script sets up the model itself. Here we are simply loading the pre-trained RoBERTa model from Hugging Face. The additional model parameters are simply passing dictionaries to the classification IDs such that the returned labels are in text form rather than numerically encoded.\nFinally, we want to transform the pre-trained model based on our LoRA configuration. This is done with the get_peft_model function.\nAfter applying LoRA, we are left with a network with the following structure:\ncsharp\n\n```\ntrainable params: 4,110,764 \nall params: 562,951,596 \ntrainable%: 0.7302162440267778\n```\n\n_Note: the original RoBERTa parameters are still in the network; they are simply frozen and therefore not trainable. Instead, we have the LoRA layers, which are fewer in number and trainable._\n### Initiate Training\nOnce we have our LoRA model, it's simply a matter of setting up our trainer and initiating the training process.\nMost aspects in the script are standard for training deep learning models; however, we have incorporated a few additional elements to improve GPU memory efficiency. Let's briefly outline them.\n * **Per_device_train_batch_size** - Setting this parameter to lower values maintains low RAM usage on your GPU at the expense of training speed. The lower the number, the more training steps are required to complete one epoch. Remember, one epoch is a complete run through the entire training dataset.\n * **Per_device_eval_batch** - Setting this parameter dictates how many data samples are processed on each GPU core. Increasing this number increases the speed of evaluation and therefore the speed of training, but at the cost of GPU memory usage.\n * **Gradient_accumulation_steps** - Usually, when updating deep learning models, there is a forward pass through the network and a loss is calculated. Following this, through a process known as backpropagation, the gradients are calculated and used to update the model parameters. With gradient accumulation, model parameters are not updated immediately after one forward pass. Instead, the gradients are stored and accumulated over the number of batches specified. Only after all the batches have passed through the network will the accumulated gradients be applied to update the parameters in the network. The effect here is to increase the effective batch size, meaning the model can effectively train on a larger batch size enabling you to reap the benefits of training on larger batches without the cost in GPU RAM usage.\n * **Fp16 (mixed-precision training)** - Setting mixed precision training to true can help improve training speed at the cost of GPU usage. Essentially, some calculations in the network are done in 16-bit (half precision) instead of 32-bit (full precision) to speed up the calculations. However, the method requires both a 16-bit and 32-bit version of the model to be stored on your GPU device meaning 1.5 RAM usage.\n\n\n* * *\n## Model Diagnostics\nLet's see how everything performed. Since this is about training effectively with limited hardware, let's start by looking at the GPU RAM diagnostics.\n![Image by Author: GPU RAM usage chart](../images/001.png)\nImage by Author: GPU RAM usage chart\nWe were able to train our model for 9 hours without exceeding the RAM on our GPU. This is a good result.\nWhat about the model performance? Let's take a look at the validation and training loss.\n![](../images/body-48dfdba9f9943e880031.png)\n![Image by Author: validation and training loss](../images/002.png)\nImage by Author: validation and training loss\nUpon examining the loss charts, it seems that the models have yet to converge, despite the training session lasting approximately nine hours.\nThis might not be surprising considering the substantial model size and limited infrastructure at our disposal, alongside the fact that many of the training parameter decisions were made to prioritise memory preservation over training speed.\nThe model's precision, recall, and F1 show that it's prbably not ready for full production usage.\n![](../images/body-9d03bb36acb64dc41074.png)\n![image by Author: Precision and recall tracking for the model](../images/003.png)\nimage by Author: Precision and recall tracking for the model\n![Image by Author: F1 tracking for the model](../images/004.png)\nImage by Author: F1 tracking for the model\n## Cost\nGoogle levies charges for renting GPUs via Colab based on compute units. Consequently, to calculate the cost of training the model for nine hours, we need to ascertain the number of compute units utilised. I was unable to locate explicit details from Google on their computation of these units, but it is [suggested](https://help.apify.com/en/articles/3490384-what-is-a-compute-unit) that a compute unit is determined as follows:\n> 1 GB memory x 1 hour = 1 compute unit\nThe model was trained for approximately nine hours and utilised around 15 GB of RAM. Therefore, the training run consumed roughly 135 compute units. With a price of 9.99 USD per 100 compute units, the cost of training the model amounts to approximately 13.49 USD.\nTo achieve a usable state for the model, convergence may necessitate a slightly higher expenditure.\n* * *\n## Conclusion\nWe have demonstrated that LoRA makes it possible for individuals with limited resources to engage in the world of LLMs. For interested readers, I recommend exploring other [parameter efficient fine-tuning methods](https://huggingface.co/blog/peft), as well as strategies for improving data efficiency in your training processes.\n * The end-to-end Colab with the full model run is available [here](https://colab.research.google.com/drive/1jmUPbg6G2uLkpRg7DQzPzerDLU_d9N2-?usp=sharing).\n * I have hosted an app demonstrating the model [Hugging Face](https://huggingface.co/spaces/JAdeojo/consumer-finance-complaints-app-demo) for you to try for yourself.\n * The full model diagnostics for the training run is available for you to review [here](https://api.wandb.ai/links/data-centric-solutions/mai8vzsz).\n * The Link to the model checkpoint is [here](https://huggingface.co/JAdeojo/xlm-roberta-large-lora-consumer-complaints-cfpb_49k).\n * Watch a live tutorial on [YouTube](https://youtu.be/HMbctCYJLbw).\n\n\nThanks for reading.\n* * *\nIf you're keen to enhance your skills in artificial intelligence, join the waiting list for [my course](https://www.data-centric-solutions.com/course), where I will guide you through the process of developing large language model powered applications.\nIf you're seeking AI-transformation for your business, book a discovery call today.\n> [**Brainqub3 | AI software development**](https://www.brainqub3.com/)\nFor more insights on artificial intelligence, data science, and large language models you can subscribe to the [YouTube](https://www.youtube.com/channel/UCkXe-exqi25V4GnZendgEaA) channel.\n* * *\n## Citations\n[1] Conneau, A., Khandelwal, K., Goyal, N., Chaudhary, V., Wenzek, G., Guzmán, F., Grave, E., Ott, M., Zettlemoyer, L., & Stoyanov, V. 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It also helps in adverstising on the website.\n\n\n * Cookie\nedgebucket\n * Duration\nsession\n * Description\nReddit sets this cookie to save the information about a log-on Reddit user, for the purpose of advertisement recommendations and updating the content.\n\n\n * Cookie\ndid\n * Duration\n1 year\n * Description\nArbor sets this cookie to show targeted ads to site visitors.This cookie expires after 2 months or 1 year.\n\n\nUncategorised\nOther uncategorised cookies are those that are being analysed and have not been classified into a category as yet.\nNo cookies to display.\nReject All Save My Preferences Accept All\nToggle Search[](https://towardsdatascience.com/)Toggle Mobile Navigation\n[](https://towardsdatascience.com/)\nPublish AI, ML & data-science insights to a global community of data professionals.\nSign in[Submit an Article](https://write.insightmediagroup.io)\n[Latest](https://towardsdatascience.com/latest/)[Editor's Picks](https://towardsdatascience.com/tag/editors-pick/)[Newsletter](https://towardsdatascience.com/tag/the-variable/)[ShipAI](https://towardsdatascience.com/shipai/)\n* * *\n[Write For TDS](https://towardsdatascience.com/submissions/)\n[LinkedIn](https://www.linkedin.com/company/towards-data-science/)[X](https://x.com/TDataScience)Toggle Search\n[Artificial Intelligence](https://towardsdatascience.com/category/artificial-intelligence/)\n# Fine-Tune Your LLM Without Maxing Out Your GPU\nHow you can fine-tune your LLMs with limited hardware and a tight budget\n[John Adeojo](https://towardsdatascience.com/author/johnadeojo/)\nAugust 1, 20238 min read\n![Image by Author: Generated with Midjourney](https://assets.insightmediagroup.io/media/wp-content/uploads/2023/08/1goCRGZfcV9fSoMADtuNasQ.png)\nImage by Author: Generated with Midjourney\n## Demand for Bespoke LLMs\nWith the success of ChatGPT, we have witnessed a surge in demand for bespoke large language models.\nHowever, there has been a barrier to adoption. As these models are so large, it has been challenging for businesses, researchers, or hobbyists with a modest budget to customise them for their own datasets.\nNow with innovations in parameter efficient fine-tuning (PEFT) methods, it is entirely possible to fine-tune large language models at a relatively low cost. In this article, I demonstrate how to achieve this in a [Google Colab](https://colab.research.google.com/).\nI anticipate that this article will prove valuable for practitioners, hobbyists, learners, and even hands-on start-up founders.\nSo, if you need to mock up a cheap prototype, test an idea, or create a cool data science project to stand out from the crowd - keep reading.\n* * *\nLearn this step by step with the interactive [AI Engineer](https://roadmap.sh/ai-engineer), [AI and Data Scientist](https://roadmap.sh/ai-data-scientist) and [Machine Learning](https://roadmap.sh/machine-learning) roadmaps.\n## Why Do We Fine-tune?\nBusinesses often have private datasets that drive some of their processes.\nTo give you an example, I worked for a bank where we logged customer complaints in an Excel spreadsheet. An analyst was responsible for categorising these complaints (manually) for reporting purposes. Dealing with thousands of complaints each month, this process was time-consuming and prone to human error.\nHad we had the resources, we could have fine-tuned a large language model to carry out this categorisation for us, saving time through automation and potentially reducing the rate of incorrect categorisations.\nInspired by this example, the remainder of this article demonstrates how we can fine-tune an LLM for categorising consumer complaints about financial products and services.\n* * *\n## The Dataset\nThe dataset comprises real consumer complaints data for financial services and products. It is open, publicly available data published by the [Consumer Financial Protection Bureau](https://www.consumerfinance.gov/complaint/data-use/).\nThere are over 120k anonymised complaints, categorised into approximately 214 \"subissues\".\nI have a version of the [dataset](https://huggingface.co/datasets/JAdeojo/consumer_complaints_cfpb) on my hugging face page that you can explore for yourself.\n* * *\n## The Hardware\nThe hardware I used for training was a V100 GPU with 16 GB of RAM, accessed via Google Colab. This is a relatively inexpensive and accessible infrastructure, available for rent via Google Colab Pro at approximately 9.99 USD per 100 compute units.\n* * *\n## The Large Language Model\nThe LLM used is RoBERTa¹ (XLM), which has approximately 563 million parameters. An overview of the model and its specification can be found [here](https://huggingface.co/xlm-roberta-large).\nThough not the largest model currently available, RoBERTa still presents a demanding workload for those with access only to small-scale infrastructure. This makes it an ideal choice to demonstrate that training a relatively large model on small-scale infrastructure is feasible.\n_Note - RoBERTa is a pre-trained model pulled from the_[ _Hugging Face Hub_](https://huggingface.co/) _._\n* * *\n## Fine-tuning at Low Cost with LoRA\nAs stated in the introduction, PEFT methods have made it possible to fine-tune LLMs at a low cost. One such method is LoRA, which stands for Low-Rank Adaptations of large language models.\nAt a high level, LoRA accomplishes two things. First, it freezes the existing weights of the LLM (rendering them non-trainable); second, it injects trainable \"lower-dimensional\" layers into specified layers of the architecture.\nThis technique yields a model with far fewer trainable parameters while still preserving performance. LoRA has been shown to reduce GPU memory consumption by a factor of three compared to standard fine-tuning.\nFor further details on LoRA, please read the full [paper](https://arxiv.org/pdf/2106.09685.pdf).\n* * *\n## Technical Details\nIn the past, the key challenge for training large language models on limited hardware was adapting the training parameters to prevent the process from crashing due to exceeding your GPU's memory capacity.\nWith LoRA, one can push the boundaries of their hardware with just a few adjustments.\n### Applying LoRA\nAssuming you have your dataset prepared, the first thing you need to do is set your LoRA configurations.\n * **task_type** - The task for which you're fine-tuning the model. For complaints classification, we are focusing on sequence classification.\n * **r** - A hyperparameter named the LoRA attention dimension which affects the scaling.\n * **lora_alpha** - Another hyperparameter that impacts the scaling.\n * **target_modules** - Here we specify that the transformation should be applied to the attention modules in our transformer, hence we set this to \"query\" and \"value\".\n * **lora_dropout** - The dropout probability for the LoRA layers.\n * **bias** - The bias type for LoRA.\n * **modules_to_save** - Excluding the LoRA layers, we declare which layers of our model we wish to make trainable and save at the final checkpoint. For our purposes, we require the classification head to be trainable.\n\n\nThe next part of the script sets up the model itself. Here we are simply loading the pre-trained RoBERTa model from Hugging Face. The additional model parameters are simply passing dictionaries to the classification IDs such that the returned labels are in text form rather than numerically encoded.\nFinally, we want to transform the pre-trained model based on our LoRA configuration. This is done with the get_peft_model function.\nAfter applying LoRA, we are left with a network with the following structure:\ncsharp\n\n```\ntrainable params: 4,110,764 \nall params: 562,951,596 \ntrainable%: 0.7302162440267778\n```\n\n_Note: the original RoBERTa parameters are still in the network; they are simply frozen and therefore not trainable. Instead, we have the LoRA layers, which are fewer in number and trainable._\n### Initiate Training\nOnce we have our LoRA model, it's simply a matter of setting up our trainer and initiating the training process.\nMost aspects in the script are standard for training deep learning models; however, we have incorporated a few additional elements to improve GPU memory efficiency. Let's briefly outline them.\n * **Per_device_train_batch_size** - Setting this parameter to lower values maintains low RAM usage on your GPU at the expense of training speed. The lower the number, the more training steps are required to complete one epoch. Remember, one epoch is a complete run through the entire training dataset.\n * **Per_device_eval_batch** - Setting this parameter dictates how many data samples are processed on each GPU core. Increasing this number increases the speed of evaluation and therefore the speed of training, but at the cost of GPU memory usage.\n * **Gradient_accumulation_steps** - Usually, when updating deep learning models, there is a forward pass through the network and a loss is calculated. Following this, through a process known as backpropagation, the gradients are calculated and used to update the model parameters. With gradient accumulation, model parameters are not updated immediately after one forward pass. Instead, the gradients are stored and accumulated over the number of batches specified. Only after all the batches have passed through the network will the accumulated gradients be applied to update the parameters in the network. The effect here is to increase the effective batch size, meaning the model can effectively train on a larger batch size enabling you to reap the benefits of training on larger batches without the cost in GPU RAM usage.\n * **Fp16 (mixed-precision training)** - Setting mixed precision training to true can help improve training speed at the cost of GPU usage. Essentially, some calculations in the network are done in 16-bit (half precision) instead of 32-bit (full precision) to speed up the calculations. However, the method requires both a 16-bit and 32-bit version of the model to be stored on your GPU device meaning 1.5 RAM usage.\n\n\n* * *\n## Model Diagnostics\nLet's see how everything performed. Since this is about training effectively with limited hardware, let's start by looking at the GPU RAM diagnostics.\n![Image by Author: GPU RAM usage chart](https://assets.insightmediagroup.io/media/wp-content/uploads/2023/08/1GqIBKjc0QDUhvakt6hYzXw.png)\nImage by Author: GPU RAM usage chart\nWe were able to train our model for 9 hours without exceeding the RAM on our GPU. This is a good result.\nWhat about the model performance? Let's take a look at the validation and training loss.\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2023/08/1AKTZWopONiGNrWUb3SFBXg.png)\n![Image by Author: validation and training loss](https://assets.insightmediagroup.io/media/wp-content/uploads/2023/08/1fp2GpJhTrOYF-68QsMDPjg.png)\nImage by Author: validation and training loss\nUpon examining the loss charts, it seems that the models have yet to converge, despite the training session lasting approximately nine hours.\nThis might not be surprising considering the substantial model size and limited infrastructure at our disposal, alongside the fact that many of the training parameter decisions were made to prioritise memory preservation over training speed.\nThe model's precision, recall, and F1 show that it's prbably not ready for full production usage.\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2023/08/1CxUF0BHabouD4VzvU11GLw.png)\n![image by Author: Precision and recall tracking for the model](https://assets.insightmediagroup.io/media/wp-content/uploads/2023/08/15ylx7PwpaETGHggZ858SCg.png)\nimage by Author: Precision and recall tracking for the model\n![Image by Author: F1 tracking for the model](https://assets.insightmediagroup.io/media/wp-content/uploads/2023/08/1tWzBxOS5oT6uPiboQWeQag.png)\nImage by Author: F1 tracking for the model\n## Cost\nGoogle levies charges for renting GPUs via Colab based on compute units. Consequently, to calculate the cost of training the model for nine hours, we need to ascertain the number of compute units utilised. I was unable to locate explicit details from Google on their computation of these units, but it is [suggested](https://help.apify.com/en/articles/3490384-what-is-a-compute-unit) that a compute unit is determined as follows:\n> 1 GB memory x 1 hour = 1 compute unit\nThe model was trained for approximately nine hours and utilised around 15 GB of RAM. Therefore, the training run consumed roughly 135 compute units. With a price of 9.99 USD per 100 compute units, the cost of training the model amounts to approximately 13.49 USD.\nTo achieve a usable state for the model, convergence may necessitate a slightly higher expenditure.\n* * *\n## Conclusion\nWe have demonstrated that LoRA makes it possible for individuals with limited resources to engage in the world of LLMs. For interested readers, I recommend exploring other [parameter efficient fine-tuning methods](https://huggingface.co/blog/peft), as well as strategies for improving data efficiency in your training processes.\n * The end-to-end Colab with the full model run is available [here](https://colab.research.google.com/drive/1jmUPbg6G2uLkpRg7DQzPzerDLU_d9N2-?usp=sharing).\n * I have hosted an app demonstrating the model [Hugging Face](https://huggingface.co/spaces/JAdeojo/consumer-finance-complaints-app-demo) for you to try for yourself.\n * The full model diagnostics for the training run is available for you to review [here](https://api.wandb.ai/links/data-centric-solutions/mai8vzsz).\n * The Link to the model checkpoint is [here](https://huggingface.co/JAdeojo/xlm-roberta-large-lora-consumer-complaints-cfpb_49k).\n * Watch a live tutorial on [YouTube](https://youtu.be/HMbctCYJLbw).\n\n\nThanks for reading.\n* * *\nIf you're keen to enhance your skills in artificial intelligence, join the waiting list for [my course](https://www.data-centric-solutions.com/course), where I will guide you through the process of developing large language model powered applications.\nIf you're seeking AI-transformation for your business, book a discovery call today.\n> [**Brainqub3 | AI software development**](https://www.brainqub3.com/)\nFor more insights on artificial intelligence, data science, and large language models you can subscribe to the [YouTube](https://www.youtube.com/channel/UCkXe-exqi25V4GnZendgEaA) channel.\n* * *\n## Citations\n[1] Conneau, A., Khandelwal, K., Goyal, N., Chaudhary, V., Wenzek, G., Guzmán, F., Grave, E., Ott, M., Zettlemoyer, L., & Stoyanov, V. 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