# Beginner at Hugging Face platform **URL:** \ **Category:** Beginners\ **Created:** [October 19, 2026, 7:55am UTC](https://discuss.huggingface.co/t/beginner-at-hugging-face-platform/190758 "2026-10-19T07:55:03Z")\ **Posts on this page:** 3\ **Page:** 1
**Author:** ![VivekNotes](https://sea2.discourse-cdn.com/hellohellohello/user_avatar/discuss.huggingface.co/viveknotes/32/64352_2.png) [@VivekNotes](https://discuss.huggingface.co/u/VivekNotes)\ **Post date:** [October 19, 2026, 7:55am UTC](https://discuss.huggingface.co/t/beginner-at-hugging-face-platform/190758/1 "2026-10-19T07:55:03Z")
Apart from the forum, what else is there? Can somebody guide me on using this platform ---
**Author:** ![IndustryInsightsDesk](https://sea2.discourse-cdn.com/hellohellohello/user_avatar/discuss.huggingface.co/industryinsightsdesk/32/64425_2.png) [@IndustryInsightsDesk](https://discuss.huggingface.co/u/IndustryInsightsDesk)\ **Post date:** [October 19, 2026, 9:25am UTC](https://discuss.huggingface.co/t/beginner-at-hugging-face-platform/190758/2 "2026-10-19T09:25:33Z")
Welcome! The forum is just one part. The main parts of the site are: 1. Models: thousands of pretrained models you can browse by task (text, image, audio and more) and try in the browser on many model pages. 2. Datasets: public datasets you can preview and load with the datasets library. 3. Spaces: small demo apps that other people have built, which you can use directly or duplicate to make your own. If you want to learn step by step, the Learn section has free courses, such as the LLM course, which start from the basics. A good first exercise is to pick a model, try it on its page, then run it in a notebook with the transformers pipeline function. What would you like to do with it? If you share your goal, people here can point you to the right place. ---
**Author:** ![John6666](https://sea2.discourse-cdn.com/hellohellohello/user_avatar/discuss.huggingface.co/john6666/32/27664_2.png) [@John6666](https://discuss.huggingface.co/u/John6666)\ **Post date:** [October 19, 2026, 11:52am UTC](https://discuss.huggingface.co/t/beginner-at-hugging-face-platform/190758/3 "2026-10-19T11:52:11Z")
Well, quite a lot, actually. The funny thing is, **using Hugging Face can be easy** , but I would **personally** bet that over 99% of complete newcomers would get lost if their very first step were opening [https://huggingface.co](https://huggingface.co) 😅: * * * ## My main suggestion: don’t try to learn _all of Hugging Face_ first The earlier reply has already introduced [Models](https://huggingface.co/models), [Datasets](https://huggingface.co/datasets), [Spaces](https://huggingface.co/spaces), and [Learn](https://huggingface.co/learn), so I will not repeat that tour. They are useful parts of an ecosystem, not a sequence you must complete. I think two different questions tend to get mixed together: - **“How do I use Hugging Face to do something?”** This can be astonishingly simple. Open a public demo in [Spaces](https://huggingface.co/spaces), try an interesting model through a compatible desktop app, or follow one short tutorial. That can be your whole first project. - **“How do I understand everything Hugging Face offers?”** That has no realistic finish line. Models, datasets, papers, software, and community projects keep changing. You do not need to know all of it. My suggestion is **start with a small thing you want to do, and learn the relevant parts of HF along the way**. If you have no goal yet, trying a few demos or seeing what people are building can help you find one. Also, **using HF doesn’t mean doing everything on the HF website**. Someone using [LM Studio](https://lmstudio.ai/docs/app/basics/download-model) to run a model locally, or [ComfyUI](https://docs.comfy.org/interface/features/template) to run an image workflow, may be using models hosted on HF without doing the actual computation on HF. HF itself documents [local-app integrations](https://huggingface.co/docs/hub/local-apps). In those cases, _the external app’s tutorial_ may be a better starting point than the HF home page. ### If I were completely new, I’d try one of these 1. **No particular goal yet?** Open [Spaces](https://huggingface.co/spaces), try one public app that catches your eye, then follow its links to see which model or dataset it uses. Or browse [Collections](https://huggingface.co/collections), [Posts](https://huggingface.co/posts), or [Daily Papers](https://huggingface.co/papers) to see what people are making and talking about. 2. **Want to do something practical without programming?** Start with the tutorial for the GUI you actually want to use. Local chat and image generation are common examples; HF may simply be where its model files live. You can remain a non-coder if that suits your goal. 3. **Interested in programming?** Pick a tiny task first. If you know Python, try a relevant library quickstart. If not, a few Python basics may help before taking a code-heavy course. 4. **Looking for something specific?** Search for the _task_, not just “Hugging Face.” Try both [HF’s full-text search](https://huggingface.co/docs/hub/search) and [a general search engine](https://www.google.com): ```plaintext site:huggingface.co/spaces speech transcription site:huggingface.co/models handwriting OCR site:huggingface.co/datasets Japanese speech ``` These are starting queries, not complete inventories. Add the language, hardware, file format, or application name that matters to you. The domain is `huggingface.co`. There is also more than one place to talk with people: this [Forum](https://discuss.huggingface.co/), [Posts](https://huggingface.co/posts), the [HF Discord](https://hf.co/join/discord), the **Community/Discussions tab** of an individual model, dataset, or Space, and [Hugging Face projects on GitHub](https://github.com/huggingface). They overlap, but each has a different emphasis. Everything below is optional detail. You can stop here, pick one example, and come back later. > **1. Different starting routes: browser, GUI, code, development, research** > > ### There is no entrance exam > > You do not have to become a developer to use a model. Coding is one possible path, not the destination of every beginner. > > One way to choose a starting path: > > ```plaintext > What would I enjoy trying first? > > I don't know yet > Browse a public Space, a Collection, or someone else's project. > > I want to chat with a model on my own computer > Follow a local GUI's quickstart, then choose a compatible HF model. > > I want to generate or edit images > Follow an image-tool workflow or template; find its required models. > > I want to learn how the models and code work > Learn the Python basics you need; try one small library example. > > I already write software > Start from the relevant SDK, repository README, and reproducible example. > > I want to do research > Follow a paper, its model card, data, evaluation, and implementation. > > I want to meet people or ask a question > Use the Forum, Posts, Discord, Hub Discussions, or GitHub as appropriate. > > ``` > > ### Route A: browser-only exploration > > If you do not know the vocabulary yet, choose a public [Space](https://huggingface.co/spaces) and try a small, non-sensitive example. A speech, OCR, or image demo lets you see what a tool does without installing Python. Then ask: _Did it work for my example? What happens with a different input or language?_ > > ### Route B: keep using graphical applications, with no plan to code > > This is a perfectly legitimate long-term route. An external GUI can provide the interface while HF hosts some of the model files. A simple sequence is: > > ```plaintext > Choose the outcome and application. > Follow that application's official installation guide. > Find an example model or workflow the application says it supports. > Use its built-in browser or the linked HF repository to obtain the files. > Try the smallest working example. > Only then explore alternative models or advanced settings. > > ``` > > For **local language models** , [LM Studio’s documentation](https://lmstudio.ai/docs/app/basics/download-model) explains how to search and download models and then load them on your computer. HF also has a dedicated [LM Studio integration guide](https://huggingface.co/docs/hub/lmstudio), including the model page’s “Use this model” path and the ability to paste a full HF URL into LM Studio’s downloader. You do not need to learn Transformers just to chat with a model locally. See also HF’s [local-apps overview](https://huggingface.co/docs/hub/local-apps). > > The practical limitation is not “HF knowledge” so much as **compatibility**. The application must support the model’s architecture and file format, and the model must fit your available memory or other hardware limits. Start with a model and file variant recommended by the app’s guide rather than an arbitrary huge checkpoint. Not every HF model works with every app. > > For **image generation and related workflows** , [ComfyUI’s workflow templates](https://docs.comfy.org/interface/features/template) are a good example of starting from the application. A template tells you which workflow and model files belong together. Its documentation describes missing-model checks and download links, including HF file URLs. Download behavior differs between desktop and self-hosted setups, so follow your version’s instructions. You can learn the nodes later, when you want to customize the workflow. > > ### Route C: curious about programming, but not there yet > > You can begin with a browser or GUI, then learn code if you want to batch-process files, save structured output, compare models, or build an app. A concrete need makes Python much easier to approach. > > The [HF Learn](https://huggingface.co/learn) portal has multiple learning tracks; choose one that fits the task instead of treating everything as a single course. In particular, the [LLM Course](https://huggingface.co/learn/llm-course/chapter1/1) explicitly expects good Python knowledge. So it is not necessarily the right _first_ resource for someone with no programming experience. > > ### Route D: you already program or want to develop applications > > At that point, HF is not just a destination for downloading model weights. The Hub provides versioned repositories, metadata, files, and community collaboration features. The [Hub documentation](https://huggingface.co/docs/hub/index) and [huggingface\_hub repository](https://github.com/huggingface/huggingface_hub) are relevant when you need to search for repositories, download a specific revision, upload assets, authenticate, or automate tasks. > > For model execution or training, choose the library that matches your task and supported model family. Examples include [Transformers](https://github.com/huggingface/transformers), [Diffusers](https://github.com/huggingface/diffusers), and [Datasets](https://github.com/huggingface/datasets). These are useful projects, but are not prerequisites for browsing or using a model through another program. Different tasks call for different libraries. > > If you want to show something to other people, a [Space](https://huggingface.co/docs/hub/spaces-overview) is one possible place to deploy a demo. A Space is its own application repository and runtime, not just a button attached to a model. You can also build and host elsewhere while pulling models or data from HF. Your hosting choice depends on cost, privacy, and the application’s requirements. > > ### Route E: research, evaluation, or reproducing someone else’s work > > A researcher might use HF mainly for artifacts and evidence rather than interactive demos. For example, a paper may point to a model repository; that repository may identify a training dataset, evaluation claims, and implementation; a Space might demonstrate a subset of the method. [Paper Pages](https://huggingface.co/docs/hub/paper-pages), [model cards](https://huggingface.co/docs/hub/model-cards), [dataset cards](https://huggingface.co/docs/hub/datasets-cards), repository revisions, and GitHub issues all help connect the pieces. > > For comparison or reproduction, record the exact model and dataset revisions, evaluation conditions, preprocessing, prompting and decoding choices, and library versions where they matter. Two demos may use different conditions; downloads and likes are not evaluation scores. Papers and cards describe claims and evidence, but do not guarantee independent reproduction. > **2. How to discover things on HF without getting lost** > > ### Search for a _problem_, not for the platform > > A new user might see thousands of promising repositories but have no idea which one to open. Often the obstacle is unfamiliar terminology rather than the search interface itself. Borrow keywords from a tutorial or demo that already works. > > HF has [full-text search](https://huggingface.co/docs/hub/search) across model cards, dataset cards, and some Space application files, and dedicated model, dataset, and Space filtering. It is useful, especially after you have a task name, language, architecture, format, or repository identifier. Google or another search engine may surface a helpful tutorial, discussion, or README sooner. > > Some example queries: > > ```plaintext > site:huggingface.co/spaces document OCR > site:huggingface.co/models Japanese ASR > site:huggingface.co/datasets receipt extraction > site:huggingface.co "model card" speech translation > site:discuss.huggingface.co ComfyUI missing model > site:github.com/huggingface transformers audio example > > ``` > > The `site:` operator is a filter, not a comprehensive inventory: [Google notes](https://developers.google.com/search/docs/monitor-debug/search-operators/all-search-site) that it will not necessarily return every indexed URL. Missing from search does not mean nonexistent. Both searches are discovery tools, not exhaustive catalogues. > > ### Follow one useful object in several directions > > I find this much easier than randomly browsing everything: > > - **From a Space:** try the demo, read its description, inspect the public files if available, check which models and datasets it links to, and look for the project owner’s explanation. A demo is a _working interface to inspect_, not necessarily the complete underlying system. > - **From a model:** read the model card, check task and language, examine the files and format, look for associated Spaces or papers, and read relevant Community discussions. A model repository can be useful even when there is no browser inference widget. > - **From a dataset:** look at a preview, the dataset card, its languages, license, splits and intended use, then search for projects that use it. Size does not guarantee quality or suitability. > - **From a paper:** follow the linked code, model, evaluation material, and earlier work. A paper and a released checkpoint may refer to related but not identical setups. > - **From a Collection:** inspect why someone assembled those items together; it is often a compact view of a particular project or topic. Collections are curated by their authors, not an official ranking of correctness. > - **From an external tutorial:** identify the program version, model file, task, and limitations. Then follow the links back to HF to check the original model’s terms and description. > > One useful question is **“How did the author make this demo work?”** A project might host model files on HF, code on GitHub, a Space for trying it, and a paper explaining the method. Following those connections reveals how people _actually use_ the ecosystem. > > ### Read the kind of page you’re on > > The site can look uniform even though different pages mean different things: > > **A model repository** contains files and descriptive metadata. A model card may describe intended uses, limitations, training details, license, and evaluations; the completeness varies. It may be the original author’s checkpoint, a conversion, a fine-tune, a quantization, or another derivative. Those distinctions matter when an application says it supports a model name but requires a particular file format. > > **A dataset repository** similarly has files and a dataset card, often with previews or ways to inspect columns. If your goal is to learn, the most useful initial information may be a handful of representative rows and the terms of use, not the headline number of rows. If your goal is research, how it was collected, labeled, split, and evaluated matters much more. > > **A Space** is an application that someone has built and hosted, perhaps with external APIs or other services. It might demonstrate a model, coordinate several models, or provide an interface for a workflow. Its interface alone does not tell you which model, revision, backend, pricing, or evaluation conditions are in use. Treat it as a demo, then inspect its documented implementation when that matters. See the [Spaces overview](https://huggingface.co/docs/hub/spaces-overview). > > ### A few concrete, low-cost discovery exercises > > **Example: “I want to turn a short audio recording into text.”** Search for `speech transcription`, `ASR`, or your language, and try an appropriate public Space with non-sensitive audio if available. If it looks useful, find which model powers the demo, check language and input constraints, and only then decide whether to use a GUI, a Python library, or a hosted service. A demo result proves what that _demo_ did on that input, not what all deployments will do. > > **Example: “I want to read text from a photo or receipt.”** Search for `OCR`, `document understanding`, and the language or document type. Check whether an example extracts plain text, bounding boxes, or structured fields: those are different outputs. A model that works on printed English may not handle handwriting or another script. Trying one non-sensitive sample in a Space can clarify what you need before writing any code. > > **Example: “I want to compare two models.”** Before choosing by likes or download counts, ask whether they support the same task, language, evaluation, context length, and hardware constraints. The model cards, datasets, papers, and repository discussions can help you identify a meaningful comparison. If you test them yourself, keep inputs and settings consistent. Even a small controlled test may be informative. > > ### Three small habits that make exploration easier > > Keep the URL of a working example; note _what you actually tried_; and stop searching long enough to test one plausible option. A small experiment often helps more than another hour of browsing. > **3. Forum, Posts, Discord, Hub Discussions, GitHub: where to look and where to ask** > > ### Different conversations happen in different places > > Besides the product areas, there are several overlapping communities. I see them as **complementary** : the best place depends on what you are trying to find or discuss. > > **[HF Forum](https://discuss.huggingface.co/).** A broad starting point for beginner questions, conceptual discussions, problems that do not yet have an obvious repository, and project introductions. It is a good starting point when the relevant repository is not yet clear. > > **[Posts](https://huggingface.co/posts).** A way to encounter work people are sharing, discoveries, links, questions, and updates. This can be more instructive than reading a list of official features, because you see what people actually choose to do. Browsing and publishing differ: current [HF PRO documentation](https://huggingface.co/docs/hub/pro) lists publishing Social Posts as a PRO feature. > > **[HF Discord](https://hf.co/join/discord).** Useful for more conversational exchanges and meeting others with similar interests. For reproducible bugs or important technical decisions, a durable Forum, Hub, or GitHub link is often easier for future readers. > > **The Community/Discussions tab on a particular model, dataset, or Space.** This is often the most precise place for questions tied to a specific Hub repository: “Does this model support this format?”, “Is this dataset split correct?”, or “Here is an issue with this Space.” Hub [Discussions and pull requests](https://huggingface.co/docs/hub/repositories-pull-requests-discussions) are attached to those repositories, and maintainers or interested users can reply. The tab can be disabled by repository settings, and a public discussion does not guarantee a response from the author. > > **[GitHub](https://github.com/huggingface).** Particularly relevant when the issue concerns software implementation, API behavior, installation, dependencies, a library regression, or a proposed code change. Repositories such as [transformers](https://github.com/huggingface/transformers), [diffusers](https://github.com/huggingface/diffusers), and [huggingface\_hub](https://github.com/huggingface/huggingface_hub) have their own code, documentation, releases, and issue discussions. There are also many community projects outside the official HF organization. A model-specific Hub discussion and a library GitHub issue may cover different aspects of the same problem. > > ### Which place would I start with? > > | Situation | A reasonable first place | > | --- | --- | > | “I’m completely new; what can I do here?” | Forum, plus trying public Spaces or reading beginner guides | > | “What are people building lately?” | Posts, Collections, Spaces, Papers, and community project pages | > | “Can I ask someone a quick question about an interest?” | Discord or Forum, depending on the kind of conversation | > | “This exact model has a limitation or odd output” | That model’s Hub Community tab, after reading its card | > | “This dataset appears mislabeled” | That dataset’s Community tab, with a specific row or reproducible example | > | “This Space isn’t working for me” | The Space’s page or linked project instructions, then its Community discussion if appropriate | > | “Transformers broke after an upgrade” | The library’s GitHub issue tracker, with versions and a minimal reproducer | > | “I found a useful improvement for an artifact” | The relevant Hub PR or code repository PR, depending on what must change | > > These are not exclusive rules. One project may span all these places. Start where the relevant artifact lives, and cross-link when helpful rather than scattering identical questions everywhere. > > ### How to find the place without knowing it in advance > > Know the _model_? Start at its card and Community tab. Know the _library_? Find its documentation and GitHub repo. Found a _Space_? Follow its author’s code or paper links. Know none of those? The Forum or a broad web search is fine. > > For technical claims, distinguish an announcement from documentation and a proposed fix from a released fix. Dates and versions matter; an old issue does not prove the present version has the same behavior. > > ### How to make a question easy for others to answer > > A beginner does not need a professional bug report. For a broad question, “I want to generate images without coding” may be enough. If something is broken, a few details help: > > - A link to the exact model, dataset, Space, or repository, when possible. > - What you expected and what happened instead. > - The smallest step that shows the problem; an exact error message if one appears. > - The tool and relevant version, plus basic environment information when it affects the issue. > - What you have already tried, and whether the same problem occurs with a simpler example. > > Do not share private tokens, passwords, confidential data, or identifying details just to make a question reproducible. Public sample input or a redacted example is usually better. > > You do not need to join every community. Just know that **different questions have different useful places to ask**. > **4. Useful cautions once you go beyond browsing** > > ### Viewing, downloading, running, and publishing are different > > You can browse a model page without running the model; download a model without using HF’s inference infrastructure; run HF-hosted files locally; or deploy a demo on HF that calls something elsewhere. Those are distinct operations with different prerequisites. A repository on the Hub does **not** mean there is automatically a ready-to-use API for that model, that it can run on your own computer, or that the model’s weights are compatible with your favorite GUI. > > When you do want hosted inference, [Inference Providers](https://huggingface.co/docs/inference-providers/index) and [Inference Endpoints](https://huggingface.co/docs/inference-endpoints/index) are additional options. They are not the same thing as browsing a model card or clicking a Space. You can ignore these services until you need hosted inference. > > ### Compatibility, access, and license are separate questions > > If a file will not load, first ask what _kind_ of obstacle it is. Does the application support the model architecture and format? Are you missing a required companion file or dependency? Is the device running out of memory? Does the repository require access approval? Is the Space merely asleep, or is the app itself failing? They need different responses. > > A gated model has its own [access procedure](https://huggingface.co/docs/hub/models-gated), and some files have use conditions explained in their [model card](https://huggingface.co/docs/hub/model-cards). Downloadability alone does not settle license or permitted uses. Dataset privacy, provenance, and license matter too; see [dataset cards](https://huggingface.co/docs/hub/datasets-cards). Check the original source before public or commercial reuse. > > ### Be careful with what you execute and upload > > Viewing a page, trying a demo, and executing downloaded code carry different risks. Inspect unfamiliar scripts and permissions; prefer narrowly scoped access tokens. Do not put private documents, API keys, medical information, or customer data into a public demo unless you understand its handling of inputs. Check whether connected tools or plug-ins send data elsewhere, even when the model runs locally. > > When publishing your own application, use the platform’s mechanism for secrets rather than embedding credentials in code or public repository files; the [Spaces documentation](https://huggingface.co/docs/hub/spaces-overview) describes this distinction. For a first experiment, use non-sensitive example data. > > ### Free to look at is not the same as free to host > > Some public demos and learning materials can be used without setting up paid compute, but the cost and account requirements for creating, duplicating, or upgrading Spaces are different. The current [Spaces overview](https://huggingface.co/docs/hub/spaces-overview) says static Spaces are free to create, while creating Gradio or Docker Spaces generally requires a paid plan, with a limited free ZeroGPU exception. Check [current pricing](https://huggingface.co/pricing) rather than an old tutorial. Similarly, browsing Posts and publishing Posts do not necessarily have the same account requirements. > > If a Space is unavailable, it might be sleeping, inaccessible, or broken; do not assume a single cause. > > ### The advanced pieces can wait > > You may eventually need revisions, private repositories, Jobs, endpoints, storage, or evaluation tools. The [Hub docs](https://huggingface.co/docs/hub/index) cover these, but none is a beginner prerequisite. > > My very short checklist would be: **one useful task, one compatible example, a reliable source link, and an understanding of where the computation and data go**. That is already enough to make informed progress without mastering the whole site. ## If you want a more specific recommendation If you have one interest, such as local chat, image generation, learning Python, or trying research models, mentioning it would help people suggest a concrete starting point. If not, pick any small demo that looks interesting. That is already a valid first step.