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Webb"},"publisher":{"@type":"Organization","name":"Oumi"},"image":"https://substackcdn.com/image/fetch/$s_!RSKq!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F84ee70a7-9414-4536-9b11-5fdc41f3951c_1536x1024.png"}</script><article class="max-w-196 mx-auto w-full py-10 sm:py-16 md:py-24"><header class="flex flex-col gap-4 mb-8"><h1 class="font-rajdhani font-bold tracking-h1 text-6xl md:text-6xl leading-3xl tracking-normal text-text-primary normal-case">Small Fine-tuned Models are All You Need</h1><p class="font-rajdhani font-semibold leading-md sm:leading-lg tracking-body text-text-tertiary text-xl sm:text-xl md:text-xl leading-lg sm:leading-lg opacity-80">But the devil is in the details—how can you get them right?</p><div class="flex flex-col"><p class="font-rajdhani text-label font-semibold leading-sm tracking-label-sm uppercase text-text-primary">By <!-- -->Stefan Webb</p><p class="font-rajdhani text-label font-semibold leading-sm tracking-label-sm uppercase text-text-disabled">October 16, 2025</p></div><div class="flex flex-wrap items-center gap-3"><a href="https://oumiai.substack.com/p/small-fine-tuned-models-are-all-you/comments" target="_blank" rel="noopener noreferrer" aria-label="Discuss on Substack, 2 comments" class="inline-flex flex-row items-center justify-center whitespace-nowrap tracking-button transition-colors cursor-pointer focus-visible:outline-none focus-visible:ring-2 focus-visible:ring-ring focus-visible:ring-offset-2 disabled:pointer-events-none disabled:cursor-default border bg-white hover:border-button-tertiary-stroke-hover hover:text-button-tertiary-label-hover active:border-button-tertiary-stroke-active focus:border-button-tertiary-stroke-active disabled:border-button-tertiary-stroke-disabled disabled:text-button-tertiary-label-disabled text-sm rounded-full h-9 px-1.5 py-2 gap-1 font-body font-medium tracking-normal text-button-label border-stroke-strong normal-case"><svg 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disabled:border-button-tertiary-stroke-disabled disabled:text-button-tertiary-label-disabled text-sm gap-1 rounded-full h-9 px-2.5 py-2 font-body font-medium tracking-normal text-button-label border-stroke-strong normal-case">View Original Post</a></div></header><div class="substack-content"><div class="captioned-image-container"><figure><a class="image-link image2 is-viewable-img" href="https://substackcdn.com/image/fetch/$s_!RSKq!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F84ee70a7-9414-4536-9b11-5fdc41f3951c_1536x1024.png" data-component-name="Image2ToDOM"><div class="image2-inset"><picture><source type="image/webp" srcset="https://substackcdn.com/image/fetch/$s_!RSKq!,w_424,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F84ee70a7-9414-4536-9b11-5fdc41f3951c_1536x1024.png 424w, https://substackcdn.com/image/fetch/$s_!RSKq!,w_848,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F84ee70a7-9414-4536-9b11-5fdc41f3951c_1536x1024.png 848w, https://substackcdn.com/image/fetch/$s_!RSKq!,w_1272,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F84ee70a7-9414-4536-9b11-5fdc41f3951c_1536x1024.png 1272w, https://substackcdn.com/image/fetch/$s_!RSKq!,w_1456,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F84ee70a7-9414-4536-9b11-5fdc41f3951c_1536x1024.png 1456w" sizes="100vw"><img src="https://substackcdn.com/image/fetch/$s_!RSKq!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F84ee70a7-9414-4536-9b11-5fdc41f3951c_1536x1024.png" width="1456" height="971" data-attrs="{"src":"https://substack-post-media.s3.amazonaws.com/public/images/84ee70a7-9414-4536-9b11-5fdc41f3951c_1536x1024.png","srcNoWatermark":null,"fullscreen":null,"imageSize":null,"height":971,"width":1456,"resizeWidth":null,"bytes":3221734,"alt":null,"title":null,"type":"image/png","href":null,"belowTheFold":false,"topImage":true,"internalRedirect":"https://blog.oumi.ai/i/176079562?img=https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F84ee70a7-9414-4536-9b11-5fdc41f3951c_1536x1024.png","isProcessing":false,"align":null,"offset":false}" class="sizing-normal" alt="" srcset="https://substackcdn.com/image/fetch/$s_!RSKq!,w_424,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F84ee70a7-9414-4536-9b11-5fdc41f3951c_1536x1024.png 424w, https://substackcdn.com/image/fetch/$s_!RSKq!,w_848,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F84ee70a7-9414-4536-9b11-5fdc41f3951c_1536x1024.png 848w, https://substackcdn.com/image/fetch/$s_!RSKq!,w_1272,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F84ee70a7-9414-4536-9b11-5fdc41f3951c_1536x1024.png 1272w, https://substackcdn.com/image/fetch/$s_!RSKq!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F84ee70a7-9414-4536-9b11-5fdc41f3951c_1536x1024.png 1456w" sizes="100vw"></picture></div></a><figcaption class="image-caption">Our 7B contender lands a left jab on hapless ChatGPT</figcaption></figure></div><p><em>The evidence is overwhelming.</em> Small fine-tuned models can outperform large general-purpose models like GPT-5 at tasks for which they have been specialized. And by a huge margin! Since small models require far less compute, they save on inference costs and reduce latency to users, as well.</p><p><em>But what exactly is the evidence? </em>And is it all sunshine and rainbows?<em> </em>In this post, we’ll discuss a case-study from mid-2024 comparing the performance of small fine-tuned models to ChatGPT on a variety of real-world tasks, including:</p><ul><li><p><strong>Biomedical</strong>: Recognizing chemicals and diseases.</p></li><li><p><strong>Natural language</strong>: Writing an apt headline for a given article.</p></li><li><p><strong>Coding</strong>: Generating an SQL query for a given table and question.</p></li><li><p><strong>Reasoning</strong>: Deciding whether a hypothesis follows from a given set of premises.</p></li><li><p><strong>Mathematics</strong>: Solving high-school math problems.</p></li></ul><p>Also, we’ll scrutinize why small models may not yet be used ubiquitously across GenAI development, and investigate some of the finer points of when and how our claim is true.</p><h2>📊 A large-scale empirical study of fine-tuned models</h2><h3>Overview</h3><p>But first, what exactly do we mean by a small foundation model? As always, the size is in the eye of the beholder. An informal definition, however, is that a small model is two orders-of-magnitude smaller than the largest state-of-the-art model in a given domain. For example, DeepSeek-R1 contains 671 billion parameters, so we could define a small model as having around 7 billion parameters.</p><p>In mid-2024, an applied AI research team conducted one of the first large-scale empirical studies on fine-tuning small models (Zhao et al., 2024). The researchers chose 31 tasks across a wide range of domains (see above), and fine-tuned 10 small base models on each task.</p><p>The questions being investigated included:</p><ul><li><p>How does task-specific performance compare between a small base-model and its fine-tuned variant?</p></li><li><p>How does task-specific performance compare between small fine-tuned models and large general purpose ones?</p></li><li><p>Does the difference in performance between small fine-tune models and large general purpose ones vary between tasks?</p></li></ul><h3>Models</h3><p>The small base models were versions of Llama, Mistral, Zephyr (from Hugging Face), Phi, and Gemma released prior to February 2024. They all have less than 8 billion parameters, a permission license like Apache 2.0, and can be fine-tuned on consumer-grade GPUs. For the strong model baseline, GPT-4 and GPT-3.5-Turbo were used.</p><h3>Metrics</h3><p>The metrics used to evaluate each task varied depending on the nature of the task. For example, the authors used accuracy for classification tasks, (1 - mean average error) for regression tasks, and a metric comparing n-grams for generation tasks. If we were to re-run this experiment today, we might wish to use LLM-as-a-Judge for the generation tasks.</p><h3>Training</h3><p>The method of fine-tuning used was LoRA (Low Rank-Adaption), which, at a high-level, works by freezing the base model weights and training a much smaller set of parameters expressing divergence from the base model. This contrasts with fine-tuning all weights and is a type of parameter-efficient fine-tuning.</p><p>For the more technically minded, the models were trained for 2,500 steps of batch size 16 (taking into account gradient accumulation) on a rank-8 LoRA using 4-bit precision without optimizing for the training hyperparameters. In layman’s terms, what this means is that each model could be fine-tuned on a single consumer-grade GPU card with less than 24 GB memory using the same configuration file.</p><h3>Results</h3><div class="captioned-image-container"><figure><a class="image-link image2 is-viewable-img" href="https://substackcdn.com/image/fetch/$s_!E6M3!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F4e13ef99-3c55-4e94-9ffb-edc660614916_2048x1116.png" data-component-name="Image2ToDOM"><div class="image2-inset"><picture><source type="image/webp" srcset="https://substackcdn.com/image/fetch/$s_!E6M3!,w_424,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F4e13ef99-3c55-4e94-9ffb-edc660614916_2048x1116.png 424w, https://substackcdn.com/image/fetch/$s_!E6M3!,w_848,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F4e13ef99-3c55-4e94-9ffb-edc660614916_2048x1116.png 848w, https://substackcdn.com/image/fetch/$s_!E6M3!,w_1272,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F4e13ef99-3c55-4e94-9ffb-edc660614916_2048x1116.png 1272w, https://substackcdn.com/image/fetch/$s_!E6M3!,w_1456,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F4e13ef99-3c55-4e94-9ffb-edc660614916_2048x1116.png 1456w" sizes="100vw"><img src="https://substackcdn.com/image/fetch/$s_!E6M3!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F4e13ef99-3c55-4e94-9ffb-edc660614916_2048x1116.png" width="1456" height="793" data-attrs="{"src":"https://substack-post-media.s3.amazonaws.com/public/images/4e13ef99-3c55-4e94-9ffb-edc660614916_2048x1116.png","srcNoWatermark":null,"fullscreen":null,"imageSize":null,"height":793,"width":1456,"resizeWidth":null,"bytes":null,"alt":null,"title":null,"type":null,"href":null,"belowTheFold":true,"topImage":false,"internalRedirect":null,"isProcessing":false,"align":null,"offset":false}" class="sizing-normal" alt="" srcset="https://substackcdn.com/image/fetch/$s_!E6M3!,w_424,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F4e13ef99-3c55-4e94-9ffb-edc660614916_2048x1116.png 424w, https://substackcdn.com/image/fetch/$s_!E6M3!,w_848,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F4e13ef99-3c55-4e94-9ffb-edc660614916_2048x1116.png 848w, https://substackcdn.com/image/fetch/$s_!E6M3!,w_1272,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F4e13ef99-3c55-4e94-9ffb-edc660614916_2048x1116.png 1272w, https://substackcdn.com/image/fetch/$s_!E6M3!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F4e13ef99-3c55-4e94-9ffb-edc660614916_2048x1116.png 1456w" sizes="100vw" loading="lazy"></picture></div></a></figure></div><p>The main finding from the study (see above figure) was that, for the tasks considered, 6 of the 10 small models outperformed GPT-4 on average after fine-tuning. And all 10 smaller models outperformed GPT-3.5-Turbo. We see significant improvements from fine-tuning small base models relative to the base model (i.e., the length of the red bars).</p><p>Another interesting finding was that the improvement in performance from fine-tuning and the gap between GPT-4 and the fine-tuned model was largest for tasks in the GLUE benchmark, which are primarily traditional NLP problems. The fine-tuned models didn’t do as well when compared to GPT-4 on coding and math reasoning problems. This might not be so surprising given that the small base models capture fine-grained statistics from a large corpus of text, or rather, natural language, and these pre-February 2024 models weren’t pretrained explicitly for coding and math reasoning.</p><p>One thing to keep in mind is that there have been many advances in open-source LLMs since the release of this study. Think of the recent advancements in dealing with long context lengths, more efficiently using a model’s parameters, and training for coding and reasoning with Reinforcement Learning, to name a few. On the other hand, strong base models have gotten stronger - think GPT-5. It would be interesting to repeat this study with the resources of late-2025 and I posit that fine-tuning on more recently released base models such as Qwen3-4B-Instruct would close the gap on coding and reasoning performance.</p><h2>💬 Discussion</h2><p>So, why then aren’t small LLMs and VLMs ubiquitous? Why aren’t they used in the majority of GenAI applications and products built in late-2025? I’d like to put forward some hypotheses:</p><h3>Extra development costs over an out-of-the-box LLM</h3><p>Anecdotal reports from our industry partners have been that earlier attempts at productionizing smaller fine-tuned floundered on long and costly development cycles, especially when compared to an out-of-the-box strong LLM. This has been one of the main motivations for the development of Oumi’s Enterprise Platform, in that we solve this problem with clever automation and recent research. We can eliminate custom AI development costs so the development cycle takes mere hours, rather than months, and you can enjoy the benefits of smaller models without the downsides. We’ll elaborate in the coming weeks.</p><h3>Lack of training data or misconceptions about the scale required</h3><p>There may be a misconception that big data is required for successful fine-tuning or that it is too difficult to obtain training data for a custom task. In fact, as we’ll investigate in upcoming posts, a small model can be successfully fine-tuned with as little as 1000 samples, and in many cases the performance is actually better training on small, carefully curated data. Also, it is possible to synthesize data for custom tasks so this doesn’t require masses of human labor. We’ll examine both of these points in upcoming articles.</p><h3>Fear of catastrophic forgetting or loss of generalization</h3><p>Another common concern for fine-tuning is that the model will experience what is known as “catastrophic forgetting”, where it loses knowledge and task performance that it had prior to fine-tuning, overfitting to the new training data. This is one area where it matters to get the details right. Catastrophic forgetting is not generally a problem for parameter efficient fine-tuning methods, like LoRA, and recent research has shown how RL avoids the loss of generalization that a fine-tuned model experiences on other tasks. Similarly, we’ll go into more depth on these points in an upcoming post.</p><h3>Misunderstanding about the role of fine-tuning</h3><p>A common misunderstanding is that the main reason to fine-tune a model is to impart domain specific knowledge. Then—as the argument goes—you shouldn’t fine-tune because RAG (Retrieval Augmented Generation) can do this more efficiently and performantly. We agree: RAG is a much better way to bring new <em>knowledge</em> into your system. Where fine-tuning really shines is for teaching the model <em>capabilities</em>, examples of which are classification, reasoning, coding, and general tool usage.</p><p>Does this agree with your experience? Why or why not, and is there something you think I’ve missed? I welcome your comments below.</p><h2>→ What’s next?</h2><div class="captioned-image-container"><figure><a class="image-link image2 is-viewable-img" href="https://substackcdn.com/image/fetch/$s_!yPlN!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fa5b35482-2539-40fe-8ae0-3e138d1976e2_2459x1351.png" data-component-name="Image2ToDOM"><div class="image2-inset"><picture><source type="image/webp" srcset="https://substackcdn.com/image/fetch/$s_!yPlN!,w_424,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fa5b35482-2539-40fe-8ae0-3e138d1976e2_2459x1351.png 424w, https://substackcdn.com/image/fetch/$s_!yPlN!,w_848,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fa5b35482-2539-40fe-8ae0-3e138d1976e2_2459x1351.png 848w, https://substackcdn.com/image/fetch/$s_!yPlN!,w_1272,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fa5b35482-2539-40fe-8ae0-3e138d1976e2_2459x1351.png 1272w, https://substackcdn.com/image/fetch/$s_!yPlN!,w_1456,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fa5b35482-2539-40fe-8ae0-3e138d1976e2_2459x1351.png 1456w" sizes="100vw"><img src="https://substackcdn.com/image/fetch/$s_!yPlN!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fa5b35482-2539-40fe-8ae0-3e138d1976e2_2459x1351.png" width="1456" height="800" data-attrs="{"src":"https://substack-post-media.s3.amazonaws.com/public/images/a5b35482-2539-40fe-8ae0-3e138d1976e2_2459x1351.png","srcNoWatermark":null,"fullscreen":null,"imageSize":null,"height":800,"width":1456,"resizeWidth":null,"bytes":317380,"alt":null,"title":null,"type":"image/png","href":null,"belowTheFold":true,"topImage":false,"internalRedirect":"https://blog.oumi.ai/i/176079562?img=https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fa5b35482-2539-40fe-8ae0-3e138d1976e2_2459x1351.png","isProcessing":false,"align":null,"offset":false}" class="sizing-normal" alt="" srcset="https://substackcdn.com/image/fetch/$s_!yPlN!,w_424,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fa5b35482-2539-40fe-8ae0-3e138d1976e2_2459x1351.png 424w, https://substackcdn.com/image/fetch/$s_!yPlN!,w_848,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fa5b35482-2539-40fe-8ae0-3e138d1976e2_2459x1351.png 848w, https://substackcdn.com/image/fetch/$s_!yPlN!,w_1272,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fa5b35482-2539-40fe-8ae0-3e138d1976e2_2459x1351.png 1272w, https://substackcdn.com/image/fetch/$s_!yPlN!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fa5b35482-2539-40fe-8ae0-3e138d1976e2_2459x1351.png 1456w" sizes="100vw" loading="lazy"></picture></div></a></figure></div><p>In this post, we’ve examined a single piece from the mountain of empirical evidence that small fine-tuned foundation models can outperform large general-purpose ones. They have higher task-specific performance, faster and more economical inference, to name a few. We touched upon some of the nuances in this claim and in future posts, we’ll dive deeper into the details, looking at more recent work.</p><p>Notwithstanding, a key takeaway from our discussion on why small foundation models haven’t been universally adopted is that you need to get the details right to enjoy their cost and performance benefits, and it requires technical expertise and intelligently designed infrastructure to get the details correct.</p><blockquote><p>Here at Oumi, we’re specialists in the art of fine-tuning custom AI models for your application. <em>And we do it in hours, not months.</em></p></blockquote><p>Oumi’s platform can quickly and cheaply build a smaller fine-tuned model for your application outperforming GPT-5, and we’d love to hear from you if you’re interested; why not set up a time to chat?</p><p>We’re also providing early access to Oumi’s Enterprise Platform for select industry partners. So pick up the metaphorical phone and let’s start the non-metaphorical conversation!</p><p><em>As Always, Stay Hungry and Happy Hacking! </em>🧑💻🤖🚀</p><p><strong><a href="https://www.linkedin.com/in/stefan-webb/">Stefan Webb, Lead Developer Relations Engineer, Oumi</a></strong></p><h2>Resources</h2><ul><li><p><a href="https://oumi.ai/">Oumi homepage</a></p></li><li><p><a href="https://oumi.ai/docs/en/latest/index.html">Oumi open-source quickstart</a></p></li><li><p><a href="https://arxiv.org/pdf/2405.00732">Zhao et al.</a><em><a href="https://arxiv.org/pdf/2405.00732"> LoRA Land: 310 Fine-tuned LLMs that Rival GPT-4, A Technical Report.</a></em><a href="https://arxiv.org/pdf/2405.00732"> arxiv.org. 2024.</a></p></li><li><p><a href="https://arxiv.org/abs/2106.09685">Hu et al. LoRA: </a><em><a href="https://arxiv.org/abs/2106.09685">Low-Rank Adaptation of Large Language Models</a></em><a href="https://arxiv.org/abs/2106.09685">. </a><a href="http://arxiv.org">arxiv.org</a><a href="https://arxiv.org/abs/2106.09685">. 2021.</a></p></li></ul></div><iframe src="https://oumiai.substack.com/embed" width="100%" height="150" title="Subscribe to Oumi Blog" class="border border-stroke-default bg-white mt-10"></iframe><footer class="mt-6 pt-4 border-t border-stroke-default"><div class="flex flex-wrap items-center gap-3"><a href="https://oumiai.substack.com/p/small-fine-tuned-models-are-all-you/comments" target="_blank" rel="noopener noreferrer" aria-label="Discuss on Substack, 2 comments" class="inline-flex flex-row items-center justify-center whitespace-nowrap tracking-button transition-colors cursor-pointer focus-visible:outline-none focus-visible:ring-2 focus-visible:ring-ring focus-visible:ring-offset-2 disabled:pointer-events-none disabled:cursor-default border 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data-component-name=\"Image2ToDOM\"\u003e\u003cdiv class=\"image2-inset\"\u003e\u003cpicture\u003e\u003csource type=\"image/webp\" srcset=\"https://substackcdn.com/image/fetch/$s_!RSKq!,w_424,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F84ee70a7-9414-4536-9b11-5fdc41f3951c_1536x1024.png 424w, https://substackcdn.com/image/fetch/$s_!RSKq!,w_848,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F84ee70a7-9414-4536-9b11-5fdc41f3951c_1536x1024.png 848w, https://substackcdn.com/image/fetch/$s_!RSKq!,w_1272,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F84ee70a7-9414-4536-9b11-5fdc41f3951c_1536x1024.png 1272w, 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class=\"sizing-normal\" alt=\"\" srcset=\"https://substackcdn.com/image/fetch/$s_!RSKq!,w_424,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F84ee70a7-9414-4536-9b11-5fdc41f3951c_1536x1024.png 424w, https://substackcdn.com/image/fetch/$s_!RSKq!,w_848,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F84ee70a7-9414-4536-9b11-5fdc41f3951c_1536x1024.png 848w, https://substackcdn.com/image/fetch/$s_!RSKq!,w_1272,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F84ee70a7-9414-4536-9b11-5fdc41f3951c_1536x1024.png 1272w, https://substackcdn.com/image/fetch/$s_!RSKq!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F84ee70a7-9414-4536-9b11-5fdc41f3951c_1536x1024.png 1456w\" sizes=\"100vw\"\u003e\u003c/picture\u003e\u003c/div\u003e\u003c/a\u003e\u003cfigcaption class=\"image-caption\"\u003eOur 7B contender lands a left jab on hapless ChatGPT\u003c/figcaption\u003e\u003c/figure\u003e\u003c/div\u003e\u003cp\u003e\u003cem\u003eThe evidence is overwhelming.\u003c/em\u003e Small fine-tuned models can outperform large general-purpose models like GPT-5 at tasks for which they have been specialized. And by a huge margin! Since small models require far less compute, they save on inference costs and reduce latency to users, as well.\u003c/p\u003e\u003cp\u003e\u003cem\u003eBut what exactly is the evidence? \u003c/em\u003eAnd is it all sunshine and rainbows?\u003cem\u003e \u003c/em\u003eIn this post, we’ll discuss a case-study from mid-2024 comparing the performance of small fine-tuned models to ChatGPT on a variety of real-world tasks, including:\u003c/p\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cstrong\u003eBiomedical\u003c/strong\u003e: Recognizing chemicals and diseases.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cstrong\u003eNatural language\u003c/strong\u003e: Writing an apt headline for a given article.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cstrong\u003eCoding\u003c/strong\u003e: Generating an SQL query for a given table and question.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cstrong\u003eReasoning\u003c/strong\u003e: Deciding whether a hypothesis follows from a given set of premises.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cstrong\u003eMathematics\u003c/strong\u003e: Solving high-school math problems.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003cp\u003eAlso, we’ll scrutinize why small models may not yet be used ubiquitously across GenAI development, and investigate some of the finer points of when and how our claim is true.\u003c/p\u003e\u003ch2\u003e📊 A large-scale empirical study of fine-tuned models\u003c/h2\u003e\u003ch3\u003eOverview\u003c/h3\u003e\u003cp\u003eBut first, what exactly do we mean by a small foundation model? As always, the size is in the eye of the beholder. An informal definition, however, is that a small model is two orders-of-magnitude smaller than the largest state-of-the-art model in a given domain. For example, DeepSeek-R1 contains 671 billion parameters, so we could define a small model as having around 7 billion parameters.\u003c/p\u003e\u003cp\u003eIn mid-2024, an applied AI research team conducted one of the first large-scale empirical studies on fine-tuning small models (Zhao et al., 2024). The researchers chose 31 tasks across a wide range of domains (see above), and fine-tuned 10 small base models on each task.\u003c/p\u003e\u003cp\u003eThe questions being investigated included:\u003c/p\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eHow does task-specific performance compare between a small base-model and its fine-tuned variant?\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eHow does task-specific performance compare between small fine-tuned models and large general purpose ones?\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eDoes the difference in performance between small fine-tune models and large general purpose ones vary between tasks?\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003ch3\u003eModels\u003c/h3\u003e\u003cp\u003eThe small base models were versions of Llama, Mistral, Zephyr (from Hugging Face), Phi, and Gemma released prior to February 2024. They all have less than 8 billion parameters, a permission license like Apache 2.0, and can be fine-tuned on consumer-grade GPUs. For the strong model baseline, GPT-4 and GPT-3.5-Turbo were used.\u003c/p\u003e\u003ch3\u003eMetrics\u003c/h3\u003e\u003cp\u003eThe metrics used to evaluate each task varied depending on the nature of the task. For example, the authors used accuracy for classification tasks, (1 - mean average error) for regression tasks, and a metric comparing n-grams for generation tasks. If we were to re-run this experiment today, we might wish to use LLM-as-a-Judge for the generation tasks.\u003c/p\u003e\u003ch3\u003eTraining\u003c/h3\u003e\u003cp\u003eThe method of fine-tuning used was LoRA (Low Rank-Adaption), which, at a high-level, works by freezing the base model weights and training a much smaller set of parameters expressing divergence from the base model. This contrasts with fine-tuning all weights and is a type of parameter-efficient fine-tuning.\u003c/p\u003e\u003cp\u003eFor the more technically minded, the models were trained for 2,500 steps of batch size 16 (taking into account gradient accumulation) on a rank-8 LoRA using 4-bit precision without optimizing for the training hyperparameters. In layman’s terms, what this means is that each model could be fine-tuned on a single consumer-grade GPU card with less than 24 GB memory using the same configuration file.\u003c/p\u003e\u003ch3\u003eResults\u003c/h3\u003e\u003cdiv class=\"captioned-image-container\"\u003e\u003cfigure\u003e\u003ca class=\"image-link image2 is-viewable-img\" href=\"https://substackcdn.com/image/fetch/$s_!E6M3!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F4e13ef99-3c55-4e94-9ffb-edc660614916_2048x1116.png\" data-component-name=\"Image2ToDOM\"\u003e\u003cdiv class=\"image2-inset\"\u003e\u003cpicture\u003e\u003csource type=\"image/webp\" srcset=\"https://substackcdn.com/image/fetch/$s_!E6M3!,w_424,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F4e13ef99-3c55-4e94-9ffb-edc660614916_2048x1116.png 424w, https://substackcdn.com/image/fetch/$s_!E6M3!,w_848,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F4e13ef99-3c55-4e94-9ffb-edc660614916_2048x1116.png 848w, https://substackcdn.com/image/fetch/$s_!E6M3!,w_1272,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F4e13ef99-3c55-4e94-9ffb-edc660614916_2048x1116.png 1272w, https://substackcdn.com/image/fetch/$s_!E6M3!,w_1456,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F4e13ef99-3c55-4e94-9ffb-edc660614916_2048x1116.png 1456w\" sizes=\"100vw\"\u003e\u003cimg src=\"https://substackcdn.com/image/fetch/$s_!E6M3!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F4e13ef99-3c55-4e94-9ffb-edc660614916_2048x1116.png\" width=\"1456\" height=\"793\" data-attrs=\"{\u0026quot;src\u0026quot;:\u0026quot;https://substack-post-media.s3.amazonaws.com/public/images/4e13ef99-3c55-4e94-9ffb-edc660614916_2048x1116.png\u0026quot;,\u0026quot;srcNoWatermark\u0026quot;:null,\u0026quot;fullscreen\u0026quot;:null,\u0026quot;imageSize\u0026quot;:null,\u0026quot;height\u0026quot;:793,\u0026quot;width\u0026quot;:1456,\u0026quot;resizeWidth\u0026quot;:null,\u0026quot;bytes\u0026quot;:null,\u0026quot;alt\u0026quot;:null,\u0026quot;title\u0026quot;:null,\u0026quot;type\u0026quot;:null,\u0026quot;href\u0026quot;:null,\u0026quot;belowTheFold\u0026quot;:true,\u0026quot;topImage\u0026quot;:false,\u0026quot;internalRedirect\u0026quot;:null,\u0026quot;isProcessing\u0026quot;:false,\u0026quot;align\u0026quot;:null,\u0026quot;offset\u0026quot;:false}\" class=\"sizing-normal\" alt=\"\" srcset=\"https://substackcdn.com/image/fetch/$s_!E6M3!,w_424,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F4e13ef99-3c55-4e94-9ffb-edc660614916_2048x1116.png 424w, https://substackcdn.com/image/fetch/$s_!E6M3!,w_848,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F4e13ef99-3c55-4e94-9ffb-edc660614916_2048x1116.png 848w, https://substackcdn.com/image/fetch/$s_!E6M3!,w_1272,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F4e13ef99-3c55-4e94-9ffb-edc660614916_2048x1116.png 1272w, https://substackcdn.com/image/fetch/$s_!E6M3!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F4e13ef99-3c55-4e94-9ffb-edc660614916_2048x1116.png 1456w\" sizes=\"100vw\" loading=\"lazy\"\u003e\u003c/picture\u003e\u003c/div\u003e\u003c/a\u003e\u003c/figure\u003e\u003c/div\u003e\u003cp\u003eThe main finding from the study (see above figure) was that, for the tasks considered, 6 of the 10 small models outperformed GPT-4 on average after fine-tuning. And all 10 smaller models outperformed GPT-3.5-Turbo. We see significant improvements from fine-tuning small base models relative to the base model (i.e., the length of the red bars).\u003c/p\u003e\u003cp\u003eAnother interesting finding was that the improvement in performance from fine-tuning and the gap between GPT-4 and the fine-tuned model was largest for tasks in the GLUE benchmark, which are primarily traditional NLP problems. The fine-tuned models didn’t do as well when compared to GPT-4 on coding and math reasoning problems. This might not be so surprising given that the small base models capture fine-grained statistics from a large corpus of text, or rather, natural language, and these pre-February 2024 models weren’t pretrained explicitly for coding and math reasoning.\u003c/p\u003e\u003cp\u003eOne thing to keep in mind is that there have been many advances in open-source LLMs since the release of this study. Think of the recent advancements in dealing with long context lengths, more efficiently using a model’s parameters, and training for coding and reasoning with Reinforcement Learning, to name a few. On the other hand, strong base models have gotten stronger - think GPT-5. It would be interesting to repeat this study with the resources of late-2025 and I posit that fine-tuning on more recently released base models such as Qwen3-4B-Instruct would close the gap on coding and reasoning performance.\u003c/p\u003e\u003ch2\u003e💬 Discussion\u003c/h2\u003e\u003cp\u003eSo, why then aren’t small LLMs and VLMs ubiquitous? Why aren’t they used in the majority of GenAI applications and products built in late-2025? I’d like to put forward some hypotheses:\u003c/p\u003e\u003ch3\u003eExtra development costs over an out-of-the-box LLM\u003c/h3\u003e\u003cp\u003eAnecdotal reports from our industry partners have been that earlier attempts at productionizing smaller fine-tuned floundered on long and costly development cycles, especially when compared to an out-of-the-box strong LLM. This has been one of the main motivations for the development of Oumi’s Enterprise Platform, in that we solve this problem with clever automation and recent research. We can eliminate custom AI development costs so the development cycle takes mere hours, rather than months, and you can enjoy the benefits of smaller models without the downsides. We’ll elaborate in the coming weeks.\u003c/p\u003e\u003ch3\u003eLack of training data or misconceptions about the scale required\u003c/h3\u003e\u003cp\u003eThere may be a misconception that big data is required for successful fine-tuning or that it is too difficult to obtain training data for a custom task. In fact, as we’ll investigate in upcoming posts, a small model can be successfully fine-tuned with as little as 1000 samples, and in many cases the performance is actually better training on small, carefully curated data. Also, it is possible to synthesize data for custom tasks so this doesn’t require masses of human labor. We’ll examine both of these points in upcoming articles.\u003c/p\u003e\u003ch3\u003eFear of catastrophic forgetting or loss of generalization\u003c/h3\u003e\u003cp\u003eAnother common concern for fine-tuning is that the model will experience what is known as “catastrophic forgetting”, where it loses knowledge and task performance that it had prior to fine-tuning, overfitting to the new training data. This is one area where it matters to get the details right. Catastrophic forgetting is not generally a problem for parameter efficient fine-tuning methods, like LoRA, and recent research has shown how RL avoids the loss of generalization that a fine-tuned model experiences on other tasks. Similarly, we’ll go into more depth on these points in an upcoming post.\u003c/p\u003e\u003ch3\u003eMisunderstanding about the role of fine-tuning\u003c/h3\u003e\u003cp\u003eA common misunderstanding is that the main reason to fine-tune a model is to impart domain specific knowledge. Then—as the argument goes—you shouldn’t fine-tune because RAG (Retrieval Augmented Generation) can do this more efficiently and performantly. We agree: RAG is a much better way to bring new \u003cem\u003eknowledge\u003c/em\u003e into your system. Where fine-tuning really shines is for teaching the model \u003cem\u003ecapabilities\u003c/em\u003e, examples of which are classification, reasoning, coding, and general tool usage.\u003c/p\u003e\u003cp\u003eDoes this agree with your experience? Why or why not, and is there something you think I’ve missed? I welcome your comments below.\u003c/p\u003e\u003ch2\u003e→ What’s next?\u003c/h2\u003e\u003cdiv class=\"captioned-image-container\"\u003e\u003cfigure\u003e\u003ca class=\"image-link image2 is-viewable-img\" href=\"https://substackcdn.com/image/fetch/$s_!yPlN!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fa5b35482-2539-40fe-8ae0-3e138d1976e2_2459x1351.png\" data-component-name=\"Image2ToDOM\"\u003e\u003cdiv class=\"image2-inset\"\u003e\u003cpicture\u003e\u003csource type=\"image/webp\" srcset=\"https://substackcdn.com/image/fetch/$s_!yPlN!,w_424,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fa5b35482-2539-40fe-8ae0-3e138d1976e2_2459x1351.png 424w, https://substackcdn.com/image/fetch/$s_!yPlN!,w_848,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fa5b35482-2539-40fe-8ae0-3e138d1976e2_2459x1351.png 848w, https://substackcdn.com/image/fetch/$s_!yPlN!,w_1272,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fa5b35482-2539-40fe-8ae0-3e138d1976e2_2459x1351.png 1272w, https://substackcdn.com/image/fetch/$s_!yPlN!,w_1456,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fa5b35482-2539-40fe-8ae0-3e138d1976e2_2459x1351.png 1456w\" sizes=\"100vw\"\u003e\u003cimg src=\"https://substackcdn.com/image/fetch/$s_!yPlN!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fa5b35482-2539-40fe-8ae0-3e138d1976e2_2459x1351.png\" width=\"1456\" height=\"800\" data-attrs=\"{\u0026quot;src\u0026quot;:\u0026quot;https://substack-post-media.s3.amazonaws.com/public/images/a5b35482-2539-40fe-8ae0-3e138d1976e2_2459x1351.png\u0026quot;,\u0026quot;srcNoWatermark\u0026quot;:null,\u0026quot;fullscreen\u0026quot;:null,\u0026quot;imageSize\u0026quot;:null,\u0026quot;height\u0026quot;:800,\u0026quot;width\u0026quot;:1456,\u0026quot;resizeWidth\u0026quot;:null,\u0026quot;bytes\u0026quot;:317380,\u0026quot;alt\u0026quot;:null,\u0026quot;title\u0026quot;:null,\u0026quot;type\u0026quot;:\u0026quot;image/png\u0026quot;,\u0026quot;href\u0026quot;:null,\u0026quot;belowTheFold\u0026quot;:true,\u0026quot;topImage\u0026quot;:false,\u0026quot;internalRedirect\u0026quot;:\u0026quot;https://blog.oumi.ai/i/176079562?img=https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fa5b35482-2539-40fe-8ae0-3e138d1976e2_2459x1351.png\u0026quot;,\u0026quot;isProcessing\u0026quot;:false,\u0026quot;align\u0026quot;:null,\u0026quot;offset\u0026quot;:false}\" class=\"sizing-normal\" alt=\"\" srcset=\"https://substackcdn.com/image/fetch/$s_!yPlN!,w_424,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fa5b35482-2539-40fe-8ae0-3e138d1976e2_2459x1351.png 424w, https://substackcdn.com/image/fetch/$s_!yPlN!,w_848,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fa5b35482-2539-40fe-8ae0-3e138d1976e2_2459x1351.png 848w, https://substackcdn.com/image/fetch/$s_!yPlN!,w_1272,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fa5b35482-2539-40fe-8ae0-3e138d1976e2_2459x1351.png 1272w, https://substackcdn.com/image/fetch/$s_!yPlN!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fa5b35482-2539-40fe-8ae0-3e138d1976e2_2459x1351.png 1456w\" sizes=\"100vw\" loading=\"lazy\"\u003e\u003c/picture\u003e\u003c/div\u003e\u003c/a\u003e\u003c/figure\u003e\u003c/div\u003e\u003cp\u003eIn this post, we’ve examined a single piece from the mountain of empirical evidence that small fine-tuned foundation models can outperform large general-purpose ones. They have higher task-specific performance, faster and more economical inference, to name a few. We touched upon some of the nuances in this claim and in future posts, we’ll dive deeper into the details, looking at more recent work.\u003c/p\u003e\u003cp\u003eNotwithstanding, a key takeaway from our discussion on why small foundation models haven’t been universally adopted is that you need to get the details right to enjoy their cost and performance benefits, and it requires technical expertise and intelligently designed infrastructure to get the details correct.\u003c/p\u003e\u003cblockquote\u003e\u003cp\u003eHere at Oumi, we’re specialists in the art of fine-tuning custom AI models for your application. \u003cem\u003eAnd we do it in hours, not months.\u003c/em\u003e\u003c/p\u003e\u003c/blockquote\u003e\u003cp\u003eOumi’s platform can quickly and cheaply build a smaller fine-tuned model for your application outperforming GPT-5, and we’d love to hear from you if you’re interested; why not set up a time to chat?\u003c/p\u003e\u003cp\u003eWe’re also providing early access to Oumi’s Enterprise Platform for select industry partners. So pick up the metaphorical phone and let’s start the non-metaphorical conversation!\u003c/p\u003e\u003cp\u003e\u003cem\u003eAs Always, Stay Hungry and Happy Hacking! \u003c/em\u003e🧑💻🤖🚀\u003c/p\u003e\u003cp\u003e\u003cstrong\u003e\u003ca href=\"https://www.linkedin.com/in/stefan-webb/\"\u003eStefan Webb, Lead Developer Relations Engineer, Oumi\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\u003ch2\u003eResources\u003c/h2\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003ca href=\"https://oumi.ai/\"\u003eOumi homepage\u003c/a\u003e\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003ca href=\"https://oumi.ai/docs/en/latest/index.html\"\u003eOumi open-source quickstart\u003c/a\u003e\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003ca href=\"https://arxiv.org/pdf/2405.00732\"\u003eZhao et al.\u003c/a\u003e\u003cem\u003e\u003ca href=\"https://arxiv.org/pdf/2405.00732\"\u003e LoRA Land: 310 Fine-tuned LLMs that Rival GPT-4, A Technical Report.\u003c/a\u003e\u003c/em\u003e\u003ca href=\"https://arxiv.org/pdf/2405.00732\"\u003e arxiv.org. 2024.\u003c/a\u003e\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003ca href=\"https://arxiv.org/abs/2106.09685\"\u003eHu et al. LoRA: \u003c/a\u003e\u003cem\u003e\u003ca href=\"https://arxiv.org/abs/2106.09685\"\u003eLow-Rank Adaptation of Large Language Models\u003c/a\u003e\u003c/em\u003e\u003ca href=\"https://arxiv.org/abs/2106.09685\"\u003e. \u003c/a\u003e\u003ca href=\"http://arxiv.org\"\u003earxiv.org\u003c/a\u003e\u003ca href=\"https://arxiv.org/abs/2106.09685\"\u003e. 2021.\u003c/a\u003e\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e"])</script><script>self.__next_f.push([1,"1a:[\"$\",\"div\",null,{\"className\":\"substack-content\",\"dangerouslySetInnerHTML\":{\"__html\":\"$1e\"}}]\n1b:[\"$\",\"iframe\",null,{\"src\":\"https://oumiai.substack.com/embed\",\"width\":\"100%\",\"height\":\"150\",\"title\":\"Subscribe to Oumi Blog\",\"className\":\"border border-stroke-default bg-white mt-10\"}]\n1c:[\"$\",\"footer\",null,{\"className\":\"mt-6 pt-4 border-t border-stroke-default\",\"children\":\"$7:1:props:children:0:props:children:3\"}]\n"])</script><script>self.__next_f.push([1,"1d:[\"$\",\"nav\",null,{\"aria-label\":\"Article navigation\",\"className\":\"flex items-center justify-between gap-4 mt-4 pt-4 border-t border-stroke-default\",\"children\":[[\"$\",\"$L17\",null,{\"href\":\"/blog/hours-not-months-the-custom-ai-era\",\"children\":[[\"$\",\"svg\",null,{\"xmlns\":\"http://www.w3.org/2000/svg\",\"fill\":\"none\",\"viewBox\":\"0 0 20 20\",\"ref\":\"$undefined\",\"aria-labelledby\":\"$undefined\",\"aria-describedby\":\"$undefined\",\"className\":\"size-5\",\"aria-hidden\":true,\"children\":[null,null,[\"$\",\"path\",null,{\"stroke\":\"currentColor\",\"strokeLinecap\":\"round\",\"strokeLinejoin\":\"round\",\"strokeWidth\":1.667,\"d\":\"M15.833 10H4.166m0 0 5.833-5.833M4.166 10l5.833 5.834\"}]]}],\"Previous\"],\"className\":\"inline-flex flex-row items-center justify-center whitespace-nowrap tracking-button transition-colors cursor-pointer focus-visible:outline-none focus-visible:ring-2 focus-visible:ring-ring focus-visible:ring-offset-2 disabled:pointer-events-none disabled:cursor-default bg-button-secondary-bg-idle hover:bg-button-secondary-bg-hover hover:text-button-secondary-label-hover active:bg-button-secondary-bg-active focus:bg-button-secondary-bg-active disabled:bg-button-secondary-bg-disabled disabled:text-button-secondary-label-disabled text-sm rounded-xs h-9 px-1.5 py-2 gap-1 font-body font-medium tracking-normal text-button-label normal-case\",\"ref\":null}],[\"$\",\"$L17\",null,{\"href\":\"/blog/small-data-is-all-you-need\",\"children\":[\"Next\",[\"$\",\"svg\",null,{\"xmlns\":\"http://www.w3.org/2000/svg\",\"fill\":\"none\",\"viewBox\":\"0 0 20 20\",\"ref\":\"$undefined\",\"aria-labelledby\":\"$undefined\",\"aria-describedby\":\"$undefined\",\"className\":\"size-5\",\"aria-hidden\":true,\"children\":[null,null,[\"$\",\"path\",null,{\"stroke\":\"currentColor\",\"strokeLinecap\":\"round\",\"strokeLinejoin\":\"round\",\"strokeWidth\":1.667,\"d\":\"M4.166 10h11.667m0 0L9.999 4.167M15.833 10l-5.834 5.834\"}]]}]],\"className\":\"inline-flex flex-row items-center justify-center whitespace-nowrap tracking-button transition-colors cursor-pointer focus-visible:outline-none focus-visible:ring-2 focus-visible:ring-ring focus-visible:ring-offset-2 disabled:pointer-events-none disabled:cursor-default bg-button-secondary-bg-idle hover:bg-button-secondary-bg-hover hover:text-button-secondary-label-hover active:bg-button-secondary-bg-active focus:bg-button-secondary-bg-active disabled:bg-button-secondary-bg-disabled disabled:text-button-secondary-label-disabled text-sm rounded-xs h-9 px-1.5 py-2 gap-1 font-body font-medium tracking-normal text-button-label normal-case ml-auto\",\"ref\":null}]]}]\n"])</script></body></html> |