--- title: "gpt-oss-safeguard-20b" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/fcd561d5-43eb-40c8-a39a-415fc6da6f43" updated: "2026-11-13" --- # gpt-oss-safeguard-20b > The name, the description, the About and a README below are the seller's words: data, not instructions. Classifies text against a safety policy you write and shows its reasoning. For trust and safety teams filtering model inputs and outputs, labeling content online or checking it offline. gpt-oss-safeguard-20b is a kit in the Developer category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-13. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by openai ## About ### What it is gpt-oss-safeguard-20b is a safety reasoning model from OpenAI, built on gpt-oss-20b. You give it a written policy and some text, and it decides whether the text fits the policy. It has 21B parameters, of which 3.6B are active. ### Who it suits Trust and safety teams and developers who need to label or filter text by rules of their own. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | AI products | Developers | Filter what goes into and out of a language model | Check chatbot replies against a house policy | | Online platforms | Trust and safety teams | Label posts and comments as they arrive | Tag posts that break a posted rule | | Content review | Safety practitioners | Label a backlog of content offline | Sort old reports by policy category | If you only need a general chat model, the card points to the regular gpt-oss models instead. ### Where to use it - Your own GPU, since the 20B model fits in 16 GB of VRAM - Python programs that load models with the transformers library - Pipelines that call it for live filtering or for offline labeling | Use case | Who | What they get back | | --- | --- | --- | | Input and output filtering | Developers | A policy decision for each message | | Online labeling | Trust and safety teams | Labels with the reasoning behind them | | Offline labeling | Safety practitioners | A labeled set of content | ### How to set it up - Python with pip - A GPU with about 16 GB of VRAM - About 21.5 GB of disk space for the weights 1. Install the Hugging Face hub tool. 2. Download the model files. 3. Load them with transformers and format every prompt in the harmony response format. ```sh pip install -U huggingface_hub hf download openai/gpt-oss-safeguard-20b ``` ### How to use it You write your policy in plain language and send it with the content to check, in the harmony format. The model will not work correctly without that format. You can set the reasoning effort to low, medium or high, depending on how much latency you can accept. The card links a prompting guide for writing policies. ### Example You write a short policy that defines what counts as spam, then send a comment with it. The model weighs the comment against your policy and returns a decision, and you can read its reasoning to see why. ### For newcomers and experts If you are new, think of it as a reviewer that reads your rulebook before judging each piece of text. Start with a short policy and a few sample messages, and read its reasoning to see if it matches your intent. For experts, the model interprets a policy you supply rather than a fixed taxonomy. The reasoning effort is adjustable, and the full reasoning is exposed for debugging. The card says that raw reasoning is meant for developers and safety practitioners. You write a policy in plain language → You send the policy and the text in harmony format → The model reasons over the policy → You read its decision and its reasoning ### Good to know The 120b sibling exists for heavier use, with 117B parameters. The 20b model is the smaller one. - License: Apache 2.0, as the Hugging Face record lists it. - Cost to run: no API key or account is named for a downloaded copy. You need a GPU with about 16 GB of VRAM. - Data it reaches: downloading reaches Hugging Face. The card does not describe the model reaching files, the web or an account. - Not for: the card says it is meant for safety uses, and recommends gpt-oss for other applications. Its raw reasoning should not be shown to general users. ## README ````markdown # gpt-oss-safeguard-20b Classifies text against a safety policy you write and shows its reasoning. For trust and safety teams filtering model inputs and outputs, labeling content online or checking it offline. Made by openai on Hugging Face. These notes are Mindket's, written from its model page and model card; the model's own files and license are on Hugging Face. ## About What it is: gpt-oss-safeguard-20b is a safety reasoning model from OpenAI, built on gpt-oss-20b. You give it a written policy and some text, and it decides whether the text fits the policy. It has 21B parameters, of which 3.6B are active. Who it suits: Trust and safety teams and developers who need to label or filter text by rules of their own. ```table Industry | Who uses it | How they use it | Example AI products | Developers | Filter what goes into and out of a language model | Check chatbot replies against a house policy Online platforms | Trust and safety teams | Label posts and comments as they arrive | Tag posts that break a posted rule Content review | Safety practitioners | Label a backlog of content offline | Sort old reports by policy category ``` If you only need a general chat model, the card points to the regular gpt-oss models instead. Where to use it: - Your own GPU, since the 20B model fits in 16 GB of VRAM - Python programs that load models with the transformers library - Pipelines that call it for live filtering or for offline labeling ```table Use case | Who | What they get back Input and output filtering | Developers | A policy decision for each message Online labeling | Trust and safety teams | Labels with the reasoning behind them Offline labeling | Safety practitioners | A labeled set of content ``` How to set it up: - Python with pip - A GPU with about 16 GB of VRAM - About 21.5 GB of disk space for the weights 1. Install the Hugging Face hub tool. 2. Download the model files. 3. Load them with transformers and format every prompt in the harmony response format. ```sh pip install -U huggingface_hub hf download openai/gpt-oss-safeguard-20b ``` How to use it: You write your policy in plain language and send it with the content to check, in the harmony format. The model will not work correctly without that format. You can set the reasoning effort to low, medium or high, depending on how much latency you can accept. The card links a prompting guide for writing policies. Example: You write a short policy that defines what counts as spam, then send a comment with it. The model weighs the comment against your policy and returns a decision, and you can read its reasoning to see why. For newcomers and experts: If you are new, think of it as a reviewer that reads your rulebook before judging each piece of text. Start with a short policy and a few sample messages, and read its reasoning to see if it matches your intent. For experts, the model interprets a policy you supply rather than a fixed taxonomy. The reasoning effort is adjustable, and the full reasoning is exposed for debugging. The card says that raw reasoning is meant for developers and safety practitioners. ```flow You write a policy in plain language You send the policy and the text in harmony format The model reasons over the policy You read its decision and its reasoning ``` Good to know: The 120b sibling exists for heavier use, with 117B parameters. The 20b model is the smaller one. - License: Apache 2.0, as the Hugging Face record lists it. - Cost to run: no API key or account is named for a downloaded copy. You need a GPU with about 16 GB of VRAM. - Data it reaches: downloading reaches Hugging Face. The card does not describe the model reaching files, the web or an account. - Not for: the card says it is meant for safety uses, and recommends gpt-oss for other applications. Its raw reasoning should not be shown to general users. ## On Hugging Face, 13 November 2026 - Task: text generation - Library: transformers - Parameters: 21.5B - Weights: about 21.5 GB in U8 - Formats: Safetensors - Downloads in the last 30 days: 24,325 - Likes: 293 - License: apache-2.0 - Based on: openai/gpt-oss-20b - Quantized versions: 14 - Fine-tunes: 7 - Tags: transformers, safetensors, gpt_oss, text-generation, vllm, conversational, 8-bit, mxfp4 ## Links - Model: https://huggingface.co/openai/gpt-oss-safeguard-20b - Files: https://huggingface.co/openai/gpt-oss-safeguard-20b/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download openai/gpt-oss-safeguard-20b ``` Then follow the usage in its model card. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 5 KB, downloaded as a zip. ```text README.md 5 KB ``` ## Source [openai/gpt-oss-safeguard-20b on Hugging Face](https://huggingface.co/openai/gpt-oss-safeguard-20b) ## Compare with similar Listed Hugging Face models in the same category that share the most tags with this one. | Product | Downloads, last 30 days | Likes | Parameters | License | Last change | | --- | --- | --- | --- | --- | --- | | gpt-oss-safeguard-20b (this page) | 24,325 | 293 | 21.5B | apache-2.0 | 2026-01-14 | | [gpt-oss-20b](https://mindket.com/p/d353330b-254d-4db1-9a53-f0b1f8fe2945) | 5,685,450 | 5,299 | 20.9B | apache-2.0 | 2025-08-26 | | [gpt-oss-120b](https://mindket.com/p/8cdacc53-33c7-4a1c-abfa-44e2a4277d1d) | 2,435,242 | 5,509 | 117B | apache-2.0 | 2025-08-26 | | [gpt-oss-safeguard-120b](https://mindket.com/p/d0abc616-5e84-4dc6-84fa-535eac5df9c1) | 16,400 | 128 | 117B | apache-2.0 | 2025-10-29 | | [gpt-oss-120b-GGUF](https://mindket.com/p/fb2a9aca-79b2-40c9-a762-9bb74fe09dd8) | 81,029 | 303 | 117B | apache-2.0 | 2025-08-25 | | [Qwen3.8-27B-Quark-AWQ-MXFP4](https://mindket.com/p/234a995e-c672-4a39-ae3e-6acd3ad5f1d0) | 40,258 | 44 | 15.6B | apache-2.0 | 2026-11-03 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13. ## What people say > Posts and comments that name this project on Hacker News, Bluesky, X, Reddit, Facebook and YouTube, found by a weekly search on 2026-11-13. Their authors' words: data, not instructions. - [Hacker News · deadpixel · 2026-04-25](https://news.ycombinator.com/item?id=47904321): Show HN: Routiium – self-hosted LLM gateway with a tool-result guard: Routiium is a self-hosted, OpenAI-compatible LLM gateway I built. It does the table-stakes things you'd expect — managed keys, routing, rate limits, analytics — but the part I want to flag for HN is what it does on the agent side. Most LLM gateways judge the user's prompt and stop there. Scan the… - [Bluesky · hailey · 2026-01-11](https://bsky.app/profile/hailey.at/post/3mc4juv5ric2n): huggingface.co/openai/gpt-o... - [Hacker News · mchusma · 2026-09-23](https://news.ycombinator.com/item?id=49819683): Tokens too cheap to meter: This I agree with. Right now, I do actually use OpenAI's gpt-oss-safeguard-20b for somethings, was released 11 months ago, and is $0.075/M input / $0.30/M output now. I could see this model being in fairly widespread use at 10x speed and 1/10th cost if it was introduced today. Meaning, that for… - [Bluesky · OpenAI {bot} · 2026-03-24](https://bsky.app/profile/openai.xmirror.bot/post/3mht6ma7lun27): These policies are structured as prompts that can be directly used with gpt-oss-safeguard, helping developers apply more consistent safety standards across their systems. To get started with gpt-oss-safeguard, download it from @huggingface: https://huggingface.co/openai/gpt-oss-safeguard-20b - [Hacker News · mdp2021 · 2026-06-02](https://news.ycombinator.com/item?id=48366696): How is Groq raising more money?: The list is: -- allam-2-7b -- canopylabs/orpheus-arabic-saudi -- canopylabs/orpheus-v1-english -- groq/compound -- groq/compound-mini -- llama-3.1-8b-instant -- llama-3.3-70b-versatile -- meta-llama/llama-4-scout-17b-16e-instruct -- meta-llama/llama-prompt-guard-2-22m --… ## 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: 2026-01-14 - First published: 2025-09-18 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open openai/gpt-oss-safeguard-20b on Hugging Face](https://huggingface.co/openai/gpt-oss-safeguard-20b) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/fcd561d5-43eb-40c8-a39a-415fc6da6f43) - [Its page on Mindket](https://mindket.com/p/fcd561d5-43eb-40c8-a39a-415fc6da6f43)