{ "source_file": "0930", "source_platform": "Medium", "author": "Shawhin Talebi", "title_listed": "Local LLM Fine-Tuning on Mac (M1 16GB)", "keywords": "本地微调,MLX", "domain": "大模型算法", "content_type": "深度技术解析", "is_paid_flag": "部分免费/会员墙", "url": "https://towardsdatascience.com/local-llm-fine-tuning-on-mac-m1-16gb-f59f4f598be7/", "publish_date": "2024-08", "corpus_stage": "预训练/SFT", "notes": "", "id": "b964b6337d1d", "collected_at": "2026-10-01T22:41:51", "title": "Local LLM Fine-Tuning on Mac (M1 16GB) | Towards Data Science", "body_markdown": "![Revisit consent button](https://cdn-cookieyes.com/assets/images/revisit.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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MLX)\n[Shaw Talebi](https://towardsdatascience.com/author/shawhin/)\nAugust 1, 20248 min read\nThis article is part of a [larger series](https://shawhin.medium.com/list/large-language-models-llms-8e009ae3054c) on using large language models (LLMs) in practice. In a [previous post](https://towardsdatascience.com/qlora-how-to-fine-tune-an-llm-on-a-single-gpu-4e44d6b5be32), I showed how to fine-tune an LLM using a single (free) GPU on Google Colab. While that example (and many others) readily runs on Nvidia hardware, they are not easily adapted to M-series Macs. In this article, I walk through an easy way to fine-tune an LLM locally on a Mac.\n![Photo by Myron Mott on Unsplash](https://assets.insightmediagroup.io/media/wp-content/uploads/2024/08/07-r3F1guQD-CUR3v-scaled.jpg)\nPhoto by [Myron Mott](https://unsplash.com/@s2killa?utm_source=medium&utm_medium=referral) on [Unsplash](https://unsplash.com?utm_source=medium&utm_medium=referral)\n* * *\nWith the rise of open-source large language models (LLMs) and efficient fine-tuning methods, building custom ML solutions has never been easier. Now, anyone with **a single GPU can fine-tune an LLM on their local machine**.\nHowever, Mac users have been largely left out of this trend due to Apple's M-series chips. These chips employ a unified memory framework, which precludes the need for a GPU. Thus, many (GPU-centric) open-source tools for running and training LLMs are not compatible with (or don't fully utilize) modern Mac computing power.\nI had **almost** given up on my dreams of training LLMs locally until I discovered the MLX Python library.\nLearn this step by step with the interactive [AI Engineer](https://roadmap.sh/ai-engineer) roadmap.\n## **MLX**\n[**MLX**](https://ml-explore.github.io/mlx/build/html/index.html) is a Python library developed by Apple's Machine Learning research team to **run matrix operations efficiently on Apple silicon**. This is important because matrix operations are the core computations underlying neural networks.\nThe key benefit of MLX is it fully utilizes the M series chips' **unified memory paradigm** , which enables modest systems (like mine - M1 16GB) to run fine-tuning jobs on large models (e.g., Mistral 7b Instruct).\nWhile the library doesn't have high-level abstractions for training models like Hugging Face, there is an [example implementation of LoRA](https://github.com/ml-explore/mlx-examples/tree/main/lora) that can be readily hacked and adapted for another use case.\nThis is exactly what I do in the example below.\n## **Example Code: Fine-tuning Mistral 7b Instruct**\nThis example is similar to one from a [previous article](https://towardsdatascience.com/qlora-how-to-fine-tune-an-llm-on-a-single-gpu-4e44d6b5be32). However, instead of using Hugging Face's Transformers library and Google Colab, I will use the MLX library and my local machine (2020 Mac Mini M1 16GB).\nSimilar to the [previous example](https://towardsdatascience.com/qlora-how-to-fine-tune-an-llm-on-a-single-gpu-4e44d6b5be32), I will be **fine-tuning a quantized version of Mistral-7b-Instruct to respond to YouTube comments in my likeness**. I use the QLoRA parameter efficient fine-tuning method. If you are unfamiliar with QLoRA, I have an overview of the method [here](https://towardsdatascience.com/qlora-how-to-fine-tune-an-llm-on-a-single-gpu-4e44d6b5be32).\n> [**QLoRA - How to Fine-Tune an LLM on a Single GPU**](https://towardsdatascience.com/qlora-how-to-fine-tune-an-llm-on-a-single-gpu-4e44d6b5be32)\n🔗 [GitHub Repo](https://github.com/ShawhinT/YouTube-Blog/tree/main/LLMs/qlora-mlx) | [Training Data](https://github.com/ShawhinT/YouTube-Blog/tree/main/LLMs/qlora-mlx/data) | [Pre-trained Model](https://huggingface.co/mlx-community/Mistral-7B-Instruct-v0.2-4bit)\n* * *\n### **1) Setting Up Environment**\nBefore we run the example code, we will need to set up our Python environment. The first step is downloading the code from the [GitHub repo](https://github.com/ShawhinT/YouTube-Blog/tree/main/LLMs/qlora-mlx).\nbash\n\n```\ngit clone https://github.com/ShawhinT/YouTube-Blog.git\n```\n\nThe code for this example is in the _LLMs/qlora-mlx_ subdirectory. We can navigate to this folder and create a new Python env (here, I call it _mlx-env_).\nbash\n\n```\n# change dir\ncd LLMs/qlora-mlx\n\n# create py venv\npython -m venv mlx-env\n```\n\nNext, we activate the environment and install the requirements from the requirements.txt file. _Note: mlx requires your system to have an M series chip, Python >= 3.8, and macOS >= 13.5_.\nbash\n\n```\n# activate venv\nsource mlx-env/bin/activate\n\n# install requirements\npip install -r requirements.txt\n```\n\n### **2) Inference with Un-finetuned Model**\nNow that we have _mlx_ and other dependencies installed, let's run some Python code! We start by importing helpful libraries.\npython\n\n```\n# import modules (this is Python code now)\nimport subprocess\nfrom mlx_lm import load, generate\n```\n\nWe will use the _subprocess_ module to run terminal commands via Python and the _mlx-lm_ library to run inference on our pre-trained model.\n_mlx-lm_ is built on top of _mlx_ and is **specifically made for running models from the Hugging Face hub**. Here's how we can use it to generate text from an existing model.\npython\n\n```\n# define inputs\nmodel_path = \"mlx-community/Mistral-7B-Instruct-v0.2-4bit\"\nprompt = prompt_builder(\"Great content, thank you!\")\nmax_tokens = 140\n\n# load model\nmodel, tokenizer = load(model_path)\n\n# generate response\nresponse = generate(model, tokenizer, prompt=prompt, \n max_tokens = max_tokens, \n verbose=True)\n```\n\n_Note: Any of the hundreds of models on the Hugging Face_[ _mlx-community page_](https://huggingface.co/mlx-community) _can be readily used for inference. If you want to use a model that isn't available (unlikely), you can use the_[ _scripts/convert.py_](https://github.com/ShawhinT/YouTube-Blog/blob/main/LLMs/qlora-mlx/scripts/convert.py) _script to convert it into a compatible format._\nThe _prompt _builder()_ function takes in a YouTube comment and integrates it into a prompt template, as shown below.\npython\n\n```\n# prompt format\nintstructions_string = f\"\"\"ShawGPT, functioning as a virtual data science \nconsultant on YouTube, communicates in clear, accessible language, escalating \nto technical depth upon request. \nIt reacts to feedback aptly and ends responses with its signature '–ShawGPT'. \nShawGPT will tailor the length of its responses to match the viewer's comment, \nproviding concise acknowledgments to brief expressions of gratitude or \nfeedback, thus keeping the interaction natural and engaging.\n\nPlease respond to the following comment.\n\"\"\"\n\n# define lambda function\nprompt_builder = lambda comment: f'''[INST] {intstructions_string} n{comment} n[/INST]n'''\n```\n\nHere's how the model responds to the comment \"_Great content, thank you!_ \" **without fine-tuning**.\ntext\n\n```\n–ShawGPT: Thank you for your kind words! I'm glad you found the content helpful\nand enjoyable. If you have any specific questions or topics you'd like me to \ncover in more detail, feel free to ask!\n```\n\nWhile the response is coherent, there are **2 main problems** with it. 1) the signature \"-ShawGPT\" is placed at the front of the response instead of the end (as instructed), and 2) the response is much longer than how I would _actually_ respond to a comment like this.\n### **3) Preparing Training Data**\nBefore we can run the fine-tuning job, we must **prepare training, testing, and validation datasets**. Here, I use 50 real comments and responses from my YouTube channel for training and 10 comments/responses for validation and testing (70 total examples).\nA training example is given below. It is in the JSON format, i.e., a key-value pair where the key = \"text\" and the value = the merged prompt, comment, and response.\njson\n\n```\n{\"text\": \"[INST] ShawGPT, functioning as a virtual data science consultant \non YouTube, communicates in clear, accessible language, escalating to technical\n depth upon request. It reacts to feedback aptly and ends responses with its \nsignature 'u2013ShawGPT'. ShawGPT will tailor the length of its responses to \nmatch the viewer's comment, providing concise acknowledgments to brief \nexpressions of gratitude or feedback, thus keeping the interaction natural and \nengaging.nnPlease respond to the following comment.n nThis was a very \nthorough introduction to LLMs and answered many questions I had. Thank you. \nn[/INST]nGreat to hear, glad it was helpful :) -ShawGPT\"}\n```\n\nThe code to generate the train, test, and val datasets from a .csv file is available on [GitHub](https://github.com/ShawhinT/YouTube-Blog/blob/main/LLMs/qlora-mlx/data/prep-data.ipynb).\n### **4) Fine-tuning Model**\nWith our training data prepared, we can fine-tune our model. Here, I use the [_lora.py_](https://github.com/ml-explore/mlx-examples/blob/main/lora/lora.py) example script created by the _mlx_ team.\nThis script is saved in the [_scripts_](https://github.com/ShawhinT/YouTube-Blog/tree/main/LLMs/qlora-mlx/scripts) folder of the repo we cloned, and the train/test/val [data](https://github.com/ShawhinT/YouTube-Blog/tree/main/LLMs/qlora-mlx/data) are saved in the _data_ folder. To run the fine-tuning job, we can run the following terminal command.\nbash\n\n```\npython scripts/lora.py --model mlx-community/Mistral-7B-Instruct-v0.2-4bit \n --train \n --iters 100 \n --steps-per-eval 10 \n --val-batches -1 \n --learning-rate 1e-5 \n --lora-layers 16 \n --test\n\n# --train = runs LoRA training\n# --iters = number of training steps\n# --steps-per-eval = number steps to do before computing val loss\n# --val-batches = number val dataset examples to use in val loss (-1 = all)\n# --learning-rate (same as default)\n# --lora-layers (same as default)\n# --test = computes test loss at the end of training\n```\n\nTo have training run as quickly as possible, I closed out all other processes on my machine to allocate as much memory as possible to the fine-tuning process. On my M1 with 16GB of memory, this took about **15–20 minutes** to run and peaked at around **13–14 GB of memory**.\n_Note: I had to make one change in lines 340–341 of the_[ _lora.py script_](https://github.com/ml-explore/mlx-examples/blob/main/lora/lora.py) _to avoid overfitting, which was changing the rank of the LoRA adapters from r=8 to r=4._\n### **5) Inference with Fine-tuned Model**\nOnce training is complete, a file called _adapters.npz_ will appear in the working directory. This contains the LoRA adapter weights.\nTo run inference with these, we can again use the _lora.py._ This time, however, instead of running the script directly from the terminal, I used the _subprocess_ module to run the script in Python. This allows me to use the _prompt _builder()_ function defined earlier.\npython\n\n```\n# define inputs\nadapter_path = \"adapters.npz\" # same as default\nmax_tokens_str = \"140\" # must be string\n\n# define command\ncommand = ['python', 'scripts/lora.py', '--model', model_path, \n '--adapter-file', adapter_path, \n '--max-tokens', max_tokens_str, \n '--prompt', prompt]\n\n# run command and print results continuously\nrun_command_with_live_output(command)\n```\n\nThe _run_command_with_live _output()_ is a helper function (courtesy of ChatGPT) that **continuously prints process outputs from the terminal command**. This avoids having to wait until inference is done to see any outputs.\npython\n\n```\ndef run_command_with_live_output(command: list[str]) -> None:\n \"\"\"\n Courtesy of ChatGPT:\n Runs a command and prints its output line by line as it executes.\n\n Args:\n command (List[str]): The command and its arguments to be executed.\n\n Returns:\n None\n \"\"\"\n process = subprocess.Popen(command, stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True)\n\n # Print the output line by line\n while True:\n output = process.stdout.readline()\n if output == '' and process.poll() is not None:\n break\n if output:\n print(output.strip())\n\n # Print the error output, if any\n err_output = process.stderr.read()\n if err_output:\n print(err_output)\n```\n\nHere's how the model responds to the same comment (_Great content, thank you!_), but now **after fine-tuning**.\ntext\n\n```\nGlad you enjoyed it! -ShawGPT\n```\n\nThis response is much better than before fine-tuning. The \"-ShawGPT\" signature is in the right place, and it sounds like something I would _actually_ say.\nBut that's an easy comment to respond to. Let's look at something more challenging, like the one below.\ntext\n\n```\nComment: \nI discovered your channel yesterday and I am hucked, great job. \nIt would be nice to see a video of fine tuning ShawGPT using HF, I saw a video \nyou did running on Colab using Mistal-7b, any chance to do a video using your \nlaptop (Mac) or using HF spaces?\n```\n\ntext\n\n```\nResponse:\nThanks, glad you enjoyed it! I'm looking forward to doing a fine tuning video \non my laptop. I've got an M1 Mac Mini that runs the latest versions of the HF \nAPI. -ShawGPT\n```\n\nAt first glance, this is a great response. The model responds appropriately and does a proper sign-off. It also gets lucky in saying I have a M1 Mac Mini 😉\nHowever, there are two issues with this. First, Mac Minis are desktops, not laptops. Second, the example doesn't directly use the HF API.\n## What's Next?\nHere, I shared a simple local fine-tuning example for M-series Macs. The data and code for this example are freely available on the [GitHub repo](https://github.com/ShawhinT/YouTube-Blog/tree/main/LLMs/qlora-mlx).\nI hope that this can be a helpful jumping-off point for your use cases. If you have **any suggestions for future content** in this series, please let me know in the comments :)\n**More on LLMs 👇**\n> [**Large Language Models (LLMs)**](https://shawhin.medium.com/list/8e009ae3054c)\n* * *\n## Resources\n**Connect** : [My website](https://shawhintalebi.com/) | [Book a call](https://calendly.com/shawhintalebi)\n**Socials** : [YouTube 🎥 ](https://www.youtube.com/channel/UCa9gErQ9AE5jT2DZLjXBIdA) | [LinkedIn](https://www.linkedin.com/in/shawhintalebi/) | [Instagram](https://www.instagram.com/shawhintalebi)\n**Support** : [Buy me a coffee](https://www.buymeacoffee.com/shawhint) ☕️\n> [**Get FREE access to every new story I write**](https://shawhin.medium.com/subscribe)\n* * *\n[1] [MLX](https://ml-explore.github.io/mlx/build/html/index.html)\n[2] [Original code](https://github.com/ml-explore/mlx-examples/tree/main/lora)\n[3] [LoRA paper](https://arxiv.org/abs/2106.09685)\n## Register Now to Continue Reading\n### Unlock Towards Data Science with a Free Account\nAll articles on Towards Data Science are available to registered users. 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