{
"source_file": "0930",
"source_platform": "Medium",
"author": "Shawhin Talebi",
"title_listed": "LLM Fine-tuning - FAQs",
"keywords": "微调,LLM,FAQ",
"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/llm-fine-tuning-faqs-200442827c99/",
"publish_date": "2024-09",
"corpus_stage": "预训练/SFT",
"notes": "",
"id": "8c8e378b3ef9",
"collected_at": "2026-10-01T22:30:37",
"title": "LLM Fine-tuning - FAQs | Towards Data Science",
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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[Large Language Models](https://towardsdatascience.com/category/artificial-intelligence/large-language-models/)\n# LLM Fine-tuning - FAQs\nAnswering the most common questions I received as an AI consultant\n[Shaw Talebi](https://towardsdatascience.com/author/shawhin/)\nSeptember 26, 20247 min read\n# **LLM Fine-Tuning - FAQs**\nLast year, I posted an article on [fine-tuning large language models (LLMs)](https://towardsdatascience.com/fine-tuning-large-language-models-llms-23473d763b91). To my surprise, this turned out to be one of my most-read blogs ever, and it led to dozens of conversations with clients about their fine-tuning questions and AI projects. Here, I will summarize these conversations' most frequently asked questions and my responses.\n\nImage from Canva.\n* * *\nLearn this step by step with the interactive [AI Engineer](https://roadmap.sh/ai-engineer) and [Machine Learning](https://roadmap.sh/machine-learning) roadmaps.\n## **What is Fine-tuning?**\nI like to define [**fine-tuning**](https://towardsdatascience.com/fine-tuning-large-language-models-llms-23473d763b91) as **taking an existing (pre-trained) model and training at least 1 model parameter to adapt it to a particular use case**.\nIt's important to note the \"_training at least 1 model parameter_ \" part of the definition. Some will define fine-tuning without this nuance (including me at times). However, this distinguishes fine-tuning from approaches like prompt engineering or prefix-tuning, which adapt a model's behavior without modifying its internal operations.\n> [**Fine-Tuning Large Language Models (LLMs)**](https://towardsdatascience.com/fine-tuning-large-language-models-llms-23473d763b91)\n## **When NOT to Fine-tune**\nPeople tend to think that LLM engineering techniques like prompt engineering, RAG, and fine-tuning all live on a spectrum, where fine-tuning is an objectively more powerful version of these other approaches. **However, this is misleading**.\nThe effectiveness of any approach **will depend on the details of the use case**. For example, fine-tuning is less effective than [retrieval augmented generation (RAG)](https://towardsdatascience.com/how-to-improve-llms-with-rag-abdc132f76ac) to provide LLMs with specialized knowledge [1].\nThis makes sense when we consider training data volume. For instance, Llama 3.1 8B was trained on ~15T tokens _(~1M novels)_ [2]. A _robust_ fine-tuning dataset might be 1M tokens (1,000 examples consisting of ~1000 tokens each), which is about **10 million times smaller**! Thus, any _knowledge_ from a fine-tuning dataset is negligible compared to what was learned at pre-training.\n## **When do I Fine-tune?**\nThis is not to say that fine-tuning is useless. A central benefit of fine-tuning an AI assistant is **lowering inference costs** [3].\nThe standard way of adapting LLM outputs for a particular application is [**prompt engineering**](https://towardsdatascience.com/prompt-engineering-how-to-trick-ai-into-solving-your-problems-7ce1ed3b553f). In this method, **users craft prompts that elicit helpful responses from the model**. While this provides a simple and flexible way to adjust model responses, effective prompts may require detailed descriptions of the desired task and several examples the model can mimic, like the one shown below.\nmarkdown\n\n```\n--Example Prompt (before fine-tuning)--\n\nYou are an expert sentiment analysis tool. Given a text, classify its \nsentiment as \"Positive,\" \"Negative,\" or \"Neutral.\" Here are some examples:\n\nExamples\nText: \"I absolutely love the new design of this product! It's user-friendly \nand looks amazing.\" Sentiment: Positive\n\nText: \"The service was terrible. I had to wait for over an hour, and the staff \nwas rude.\" Sentiment: Negative\n\nText: \"The movie was okay. It wasn't particularly exciting, but it wasn't bad \neither.\" Sentiment: Neutral\n\nText: \"I'm so happy with the customer support I received. They resolved my \nissue quickly.\" Sentiment: Positive\n\nText: \"The meal was bland, and I wouldn't recommend it to anyone.\" Sentiment: \nNegative\n\nText: \"The weather is unpredictable today.\" Sentiment: Neutral\n\nNow analyze the following text:\nText: \"[Your input text here]\" Sentiment:\n```\n\nFine-tuning, on the other hand, can compress prompt sizes by directly training the model on examples. Shorter prompts mean fewer tokens at inference, leading to **lower compute costs and faster model responses** [3]. For instance, after fine-tuning, the above prompt could be compressed to the following.\nmarkdown\n\n```\n--Example Prompt (after fine-tuning)--\n\nYou are an expert sentiment analysis tool. Given a text, classify its \nsentiment as \"Positive,\" \"Negative,\" or \"Neutral.\"\n\nAnalyze the following text:\nText: \"[Your input text here]\" Sentiment:\n```\n\n## **RAG vs Fine-tuning?**\nWe've already mentioned situations where RAG and fine-tuning perform well. However, since this is such a common question, it's worth reemphasizing when each approach works best.\n**RAG** is when we **inject relevant context into an LLM's input prompt so that it can generate more helpful responses**. For example, if we have a domain-specific knowledge base (e.g., internal company documents and emails), we might identify the items most relevant to the user's query so that an LLM can synthesize information in an accurate and digestible way.\n> [**How to Improve LLMs with RAG**](https://towardsdatascience.com/how-to-improve-llms-with-rag-abdc132f76ac)\nHere's high-level guidance on when to use each.\n * **RAG** : necessary knowledge for the task is not commonly known or available on the web but can be stored in a database\n * **Fine-tuning** : necessary knowledge for the task is already baked into the model, but you want to reduce the prompt size or refine response quality\n * **RAG + Fine-tuning** : the task requires specialized knowledge, and we would like to reduce the prompt size or refine the response quality\n\n\nNotice that these approaches are not mutually exclusive. In fact, the original RAG system proposed by Facebook researchers used fine-tuning to better use retrieved information for generating responses [4].\n## **How Do I Pick a Model?**\nMost state-of-the-art language models are trained in similar ways on similar datasets. Thus, **performance differences of comparable models are often negligible across use cases**.\nHowever, performance is only one of many considerations important for an AI project. Others include privacy, technical requirements, and cost.\n### **Open-weights vs closed-weights?**\nAn **open-weight model's parameters are accessible to the public** , while the parameters of a closed-weight model are not.\n * **Open-weight models** : Llama (Meta), Mistral, Gemma (Google), Phi (Microsoft)\n * **Closed-weight models** : GPT 3+ (OpenAI), Claude (Anthropic), Gemini (Google)\n\n\nThe two main considerations when deciding open-weight vs closed-weight are **privacy** and **flexibility**. For instance, some projects may have strict controls on where user data can be sent, which may disqualify models accessible only via API.\nOn the other hand, some use cases require greater flexibility than those afforded by closed-weight model APIs, such as retraining specific parameters in a model or accessing intermediate model representations.\n### **How big?**\nAnother key question is how big a model should be used. This comes down to a **trade-off between model performance and inference costs**.\nThe approach I'd recommend is to start big to confirm the desired performance is obtainable, then gradually explore smaller and smaller models until performance drops below what's required. From here, an evaluation can be made on which size choice provides the greatest ROI based on the use case.\n### **Instruction-tuned or foundation model?**\nToday's most popular large language models undergo **instruction tuning**. In this process, an **initial foundation model is fine-tuned to respond to user queries**.\nMost business cases I've encountered involved an **AI chatbot or assistant**. For these types of applications, **instruction-tuned models are the best choice**.\nHowever, there are situations where directly using foundation models may work better. Namely, if the **LLM is not used for question-answering or conversational tasks**.\n## **How to Prepare Data for Fine-tuning?**\nWhile much of the attention goes to model leaderboards and fine-tuning approaches, these are secondary to the quality of the training dataset. In other words, the **data you use to fine-tune your model will be the key driver of its performance**.\nFrom my experience, most clients are interested in making a \"_custom chatbot_ \" or \"_ChatGPT for X_.\" In these cases, the best fine-tuning approach is so-called **supervised fine-tuning**. This consists of **generating a set of example query-response pairs** from which a model can be fine-tuned.\nFor example, if I wanted to fine-tune an LLM to respond to viewer questions on [YouTube](https://youtu.be/4RAvJt3fWoI?t=1813), I would need to gather a set of comments with questions and my associated responses. For a concrete example of this, check out the code walk-through on YouTube.\nPlease accept cookies to access this content\n## **Advanced Fine-tuning**\nMuch of this article has focused on fine-tuning large language models to create AI assistants. Despite the popularity of such use cases, this is a relatively narrow application of model fine-tuning.\n### Classification\nAnother way we can **fine-tune language models is for classification tasks** , such as classifying support ticket tiers, detecting spam emails, or determining the sentiment of a customer review. A classic fine-tuning approach for this is called **transfer learning** , where we **replace the head of a language model to perform a new classification task**.\n### Text Embeddings\nFine-tuning can also be applied to [**text embeddings**](https://towardsdatascience.com/text-embeddings-classification-and-semantic-search-8291746220be), which are most **commonly used today to form vector databases** in RAG systems. A major shortcoming of out-the-shelf text embedding models is that they may not perform well on domain-specific language or jargon. Fine-tuning a text embedding model for a particular domain can help overcome this.\n### Compression\n[**Model compression**](https://towardsdatascience.com/compressing-large-language-models-llms-9f406eea5b5e) aims to **reduce the size of an LLM without sacrificing performance**. While this does not necessarily fit under the fine-tuning definition above, it is in the same spirit of _adapting a model_ for a particular use case. Three popular compression techniques include quantization, pruning, and knowledge distillation.\n## What's Next?\nHere, I summarized the most common fine-tuning questions I've received over the past 12 months. While fine-tuning is not a panacea for all LLM use cases, it has key benefits.\nIf you have questions that were not covered in the article, drop a comment, and I'll share my thoughts :)\n* * *\n**My website** : \n> [**Large Language Models (LLMs)**](https://shawhin.medium.com/list/8e009ae3054c)\n[1] \n[2] [Llama 3 paper](https://arxiv.org/abs/2407.21783)\n[3] [OpenAI API Doc](https://platform.openai.com/docs/guides/fine-tuning)\n[4] [Original RAG paper](https://arxiv.org/abs/2005.11401)\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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