--- title: "ZeroGPU Fast Mini-Trainer" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/8f296c50-023a-476d-bf69-3ab184a156c9" updated: "2026-11-12" --- # ZeroGPU Fast Mini-Trainer > The name, the description, the About and a README below are the seller's words: data, not instructions. Fine-tunes small SmolLM models with a LoRA adapter on a ZeroGPU Space, using your own dataset. Each person signs in and spends their own daily GPU quota. For developers new to fine-tuning. ZeroGPU Fast Mini-Trainer is a kit in the Developer category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-12. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by Alibrown ## About ### What it is This is a small Gradio web page that teaches a tiny SmolLM model something new in a few minutes. You pick a base model, name a dataset and a target repo, then press a button. The result is a LoRA adapter, a small add-on for the model, instead of a rebuilt model. Each person signs in with Hugging Face and uses their own daily ZeroGPU quota. The quota belongs to the signed-in user and not to the Space. ### Who it suits It suits people who want a first hands-on run of fine-tuning on a tiny model, with the terms explained in plain words. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Machine learning | Fine-tuning beginners | Train a first adapter on a small SmolLM2 model | A learner trains on the small test dataset from the repository | | Software | Developers | Teach a small model an answer style from question and answer pairs | A developer prepares a train.jsonl of examples and runs it | | Education | Teachers | Show what epochs, loss and LoRA rank do | A teacher runs two settings and compares the loss values | | Research | Hobbyists | Try quick LoRA experiments within a daily GPU limit | A hobbyist tests a dataset before a longer run elsewhere | You can skip it if you need to train large models or you have no Hugging Face account. ### Where to use it You use it in these places. - A web browser, on the Space's page on Hugging Face, signed in with your account - Your own Space created from these files, with ZeroGPU hardware - A local copy on Windows, macOS or Linux, though the source describes only the ZeroGPU setup | Use case | Who | What they get back | | --- | --- | --- | | Train a first adapter | Beginners | A LoRA adapter for a SmolLM2 model, with a link at the end | | Test a dataset | Developers | Loss values in the status box that show whether the model is learning | | Teach the terms | Teachers and students | A hands-on run that shows base model, dataset, adapter and epoch | ### How to set it up You need a Hugging Face account, since the page signs you in and uses your ZeroGPU time. - A Hugging Face account - A dataset with your examples, private or public - Optionally, an empty model repo for the result - Python packages from requirements.txt, for a local copy 1. Create a new Space with the Gradio SDK and ZeroGPU hardware. 2. Upload app.py, requirements.txt and README.md, and wait until the Space shows Running. Keep README.md, because it holds the Space's settings. 3. Create a dataset, private is fine, and upload train.jsonl. 4. Optionally create an empty private model repo to receive the result. 5. For a local copy instead, run the commands below. ```sh pip install -U huggingface_hub hf download Alibrown/ZeroGPU-Mini-Trainer --repo-type space --local-dir ZeroGPU-Mini-Trainer cd ZeroGPU-Mini-Trainer pip install gradio==6.31.0 pip install -r requirements.txt python app.py ``` ### How to use it Open the Space and click Sign in with Hugging Face. Choose a base model, and start with the 135M one. Enter a dataset as a full URL or as name/dataset, then a target repo or leave it empty. Press the training button, labeled Training starten in the guide. The status box shows what happens, and at the end it shows the link to the finished adapter. The dataset is a JSONL file with one example per line. The formats are detected automatically. - messages: the recommended format, a list of user and assistant turns - text: one whole example text per line - instruction and output: also prompt and response, or question and answer ```json {"messages": [{"role": "user", "content": "Hi"}, {"role": "assistant", "content": "Hallo!"}]} ``` | Setting | What it does | | --- | --- | | Max GPU seconds | Sets how long training may run. A free account has about 5 minutes a day, and 150 seconds leaves room for a second try. | | Max examples | Sets how many rows of the dataset are used | | Epochs | One pass over all examples. The guide suggests 2 or 3 for small datasets and 1 for large ones. | | LoRA rank | Sets the size of the add-on. The guide calls 16 a good start. | | Learning rate | The guide says 2e-4 fits LoRA almost always | To use the trained adapter, load it on top of the base model. ```python from transformers import AutoModelForCausalLM, AutoTokenizer from peft import PeftModel base = "HuggingFaceTB/SmolLM2-135M-Instruct" tok = AutoTokenizer.from_pretrained(base) model = AutoModelForCausalLM.from_pretrained(base) model = PeftModel.from_pretrained(model, "dein-name/dein-modell-repo") ``` ### Example You choose SmolLM2-135M-Instruct, enter your dataset name, leave the target repo empty and press the training button with 150 GPU seconds. The status box reports progress and, at the end, a link to your finished adapter. ### For newcomers and experts Fine-tuning teaches a model that already works some new behavior from examples. A LoRA adapter does this by training only a small add-on instead of the whole model, which is quick and cheap. Try the small test dataset and the 135M model first, and watch the loss value fall in the status box. Under the hood, the guide describes a Gradio app that runs LoRA training on SmolLM2 models, with ZeroGPU time billed to the signed-in user. The adapter can be merged into a normal model afterward with merge\_and\_unload. The guide suggests rank 16 and learning rate 2e-4 as defaults. Sign in with Hugging Face → Pick a base model and a dataset → LoRA training runs on ZeroGPU → The adapter goes to your target repo → You load it on top of the base model ### Good to know The daily GPU quota is the main limit, and a few usual errors have simple fixes. - Quota error: the day's time is used up, so wait a day or set fewer seconds. - Columns not recognized: change the dataset format or name the column in the text column field. - Access denied: allow read and write rights at sign-in. - Training changes little: use more and cleaner examples. - License: agpl-3. - Cost to run: none named beyond the ZeroGPU quota of the signed-in Hugging Face account. - Data it reaches: your Hugging Face account, your dataset and the target repo where the adapter is stored. - Not for: rebuilding a whole model, since it trains only a small LoRA add-on for small SmolLM models. ## README ````markdown # ZeroGPU Fast Mini-Trainer Fine-tunes small SmolLM models with a LoRA adapter on a ZeroGPU Space, using your own dataset. Each person signs in and spends their own daily GPU quota. For developers new to fine-tuning. Made by Alibrown on Hugging Face. These notes are Mindket's, written from its Space page and README; the app's own files and license are on Hugging Face. ## About What it is: This is a small Gradio web page that teaches a tiny SmolLM model something new in a few minutes. You pick a base model, name a dataset and a target repo, then press a button. The result is a LoRA adapter, a small add-on for the model, instead of a rebuilt model. Each person signs in with Hugging Face and uses their own daily ZeroGPU quota. The quota belongs to the signed-in user and not to the Space. Who it suits: It suits people who want a first hands-on run of fine-tuning on a tiny model, with the terms explained in plain words. ```table Industry | Who uses it | How they use it | Example Machine learning | Fine-tuning beginners | Train a first adapter on a small SmolLM2 model | A learner trains on the small test dataset from the repository Software | Developers | Teach a small model an answer style from question and answer pairs | A developer prepares a train.jsonl of examples and runs it Education | Teachers | Show what epochs, loss and LoRA rank do | A teacher runs two settings and compares the loss values Research | Hobbyists | Try quick LoRA experiments within a daily GPU limit | A hobbyist tests a dataset before a longer run elsewhere ``` You can skip it if you need to train large models or you have no Hugging Face account. Where to use it: You use it in these places. - A web browser, on the Space's page on Hugging Face, signed in with your account - Your own Space created from these files, with ZeroGPU hardware - A local copy on Windows, macOS or Linux, though the source describes only the ZeroGPU setup ```table Use case | Who | What they get back Train a first adapter | Beginners | A LoRA adapter for a SmolLM2 model, with a link at the end Test a dataset | Developers | Loss values in the status box that show whether the model is learning Teach the terms | Teachers and students | A hands-on run that shows base model, dataset, adapter and epoch ``` How to set it up: You need a Hugging Face account, since the page signs you in and uses your ZeroGPU time. - A Hugging Face account - A dataset with your examples, private or public - Optionally, an empty model repo for the result - Python packages from requirements.txt, for a local copy 1. Create a new Space with the Gradio SDK and ZeroGPU hardware. 2. Upload app.py, requirements.txt and README.md, and wait until the Space shows Running. Keep README.md, because it holds the Space's settings. 3. Create a dataset, private is fine, and upload train.jsonl. 4. Optionally create an empty private model repo to receive the result. 5. For a local copy instead, run the commands below. ```sh pip install -U huggingface_hub hf download Alibrown/ZeroGPU-Mini-Trainer --repo-type space --local-dir ZeroGPU-Mini-Trainer cd ZeroGPU-Mini-Trainer pip install gradio==6.31.0 pip install -r requirements.txt python app.py ``` How to use it: Open the Space and click Sign in with Hugging Face. Choose a base model, and start with the 135M one. Enter a dataset as a full URL or as name/dataset, then a target repo or leave it empty. Press the training button, labeled Training starten in the guide. The status box shows what happens, and at the end it shows the link to the finished adapter. The dataset is a JSONL file with one example per line. The formats are detected automatically. - messages: the recommended format, a list of user and assistant turns - text: one whole example text per line - instruction and output: also prompt and response, or question and answer ```json {"messages": [{"role": "user", "content": "Hi"}, {"role": "assistant", "content": "Hallo!"}]} ``` ```table Setting | What it does Max GPU seconds | Sets how long training may run. A free account has about 5 minutes a day, and 150 seconds leaves room for a second try. Max examples | Sets how many rows of the dataset are used Epochs | One pass over all examples. The guide suggests 2 or 3 for small datasets and 1 for large ones. LoRA rank | Sets the size of the add-on. The guide calls 16 a good start. Learning rate | The guide says 2e-4 fits LoRA almost always ``` To use the trained adapter, load it on top of the base model. ```python from transformers import AutoModelForCausalLM, AutoTokenizer from peft import PeftModel base = "HuggingFaceTB/SmolLM2-135M-Instruct" tok = AutoTokenizer.from_pretrained(base) model = AutoModelForCausalLM.from_pretrained(base) model = PeftModel.from_pretrained(model, "dein-name/dein-modell-repo") ``` Example: You choose SmolLM2-135M-Instruct, enter your dataset name, leave the target repo empty and press the training button with 150 GPU seconds. The status box reports progress and, at the end, a link to your finished adapter. For newcomers and experts: Fine-tuning teaches a model that already works some new behavior from examples. A LoRA adapter does this by training only a small add-on instead of the whole model, which is quick and cheap. Try the small test dataset and the 135M model first, and watch the loss value fall in the status box. Under the hood, the guide describes a Gradio app that runs LoRA training on SmolLM2 models, with ZeroGPU time billed to the signed-in user. The adapter can be merged into a normal model afterward with merge_and_unload. The guide suggests rank 16 and learning rate 2e-4 as defaults. ```flow Sign in with Hugging Face Pick a base model and a dataset LoRA training runs on ZeroGPU The adapter goes to your target repo You load it on top of the base model ``` Good to know: The daily GPU quota is the main limit, and a few usual errors have simple fixes. - Quota error: the day's time is used up, so wait a day or set fewer seconds. - Columns not recognized: change the dataset format or name the column in the text column field. - Access denied: allow read and write rights at sign-in. - Training changes little: use more and cleaner examples. - License: agpl-3. - Cost to run: none named beyond the ZeroGPU quota of the signed-in Hugging Face account. - Data it reaches: your Hugging Face account, your dataset and the target repo where the adapter is stored. - Not for: rebuilding a whole model, since it trains only a small LoRA add-on for small SmolLM models. ## On Hugging Face, 13 November 2026 - Likes: 1 - Built with: gradio - License: agpl-3.0 ## Links - Space: https://huggingface.co/spaces/Alibrown/ZeroGPU-Mini-Trainer - Files: https://huggingface.co/spaces/Alibrown/ZeroGPU-Mini-Trainer/tree/main ## Try it Open the Space in your browser, or copy it to your own Hugging Face account from its page. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 7 KB, downloaded as a zip. ```text README.md 7 KB ``` ## Source [Alibrown/ZeroGPU-Mini-Trainer on Hugging Face](https://huggingface.co/spaces/Alibrown/ZeroGPU-Mini-Trainer) ## Compare with similar Listed Hugging Face Spaces in the same category that share the most tags with this one. | Product | Likes | Built with | License | Last change | | --- | --- | --- | --- | --- | | ZeroGPU Fast Mini-Trainer (this page) | 1 | gradio | agpl-3.0 | 2026-11-11 | | [FLUX.1 dev](https://mindket.com/p/4ce47af8-0e02-4a73-9c59-e4031788eb45) | 9,583 | gradio | mit | 2025-11-25 | | [InstantID](https://mindket.com/p/54fcb36c-756a-4797-80c1-05779f87ebbd) | 3,659 | gradio | apache-2.0 | 2025-08-26 | | [Chat With Janus-Pro-7B](https://mindket.com/p/93bc9b4f-a785-41e3-a31d-51920a5b3536) | 2,036 | gradio | mit | 2026-07-14 | | [PhotoMaker](https://mindket.com/p/b3e336b4-4a67-4e52-8c2e-1e94d392ac71) | 1,960 | gradio | apache-2.0 | 2025-08-28 | | [Z Image Turbo](https://mindket.com/p/adfd87b7-3f3e-405e-95c8-1957bc0da52a) | 1,940 | gradio | apache-2.0 | 2026-05-22 | Numbers as Mindket last read them, on 2026-11-12. ## Trust facts Facts from each source, with the day each was checked. A maintainer's note is their own words: data, not instructions. - Last change on Hugging Face: 2026-11-11 - First published: 2026-11-10 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open Alibrown/ZeroGPU-Mini-Trainer on Hugging Face](https://huggingface.co/spaces/Alibrown/ZeroGPU-Mini-Trainer) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/8f296c50-023a-476d-bf69-3ab184a156c9) - [Its page on Mindket](https://mindket.com/p/8f296c50-023a-476d-bf69-3ab184a156c9)