Huashu Design
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Sorts text against a safety policy that you write, and shows its reasoning for each decision. It suits trust and safety teams that filter or label content.
gpt-oss-safeguard-120b is a kit in the Developer category, listed by Mindket. It is free. This is version 1, last updated on . Download it on this page, a zip of its files. Nothing in it runs by itself.
pip install -U huggingface_hub
gpt-oss-safeguard-120b is a safety reasoning model from OpenAI, built on gpt-oss. You give it a written policy and some text, and it judges the text against that policy. It also handles basic safety tasks. It has 117B parameters, with 5.1B active, and fits on a single H100 GPU.
It suits trust and safety teams and developers who need to filter or label content by their own rules. It is meant for safety work, not for general chat.
| Industry | Who uses it | How they use it | Example |
|---|---|---|---|
| Trust and safety | Policy and moderation teams | Label posts and messages against a written policy | Offline labeling of a batch of reported posts |
| Software | AI product developers | Filter what goes into and out of a language model | Checking a chatbot's inputs and replies against house rules |
| Online platforms | Platform safety engineers | Tag content as it arrives | Live labeling of new content under a policy |
People who want a general assistant should use the regular gpt-oss models instead.
| Use case | Who | What they get back |
|---|---|---|
| Input and output filtering | AI developers | A policy decision on each prompt or reply |
| Online content labeling | Platform teams | Labels on content as it arrives |
| Offline labeling | Trust and safety teams | Labels on stored content, with the reasoning shown |
pip install -U huggingface_hub
hf download openai/gpt-oss-safeguard-120b
Write your policy, then send it with the text to judge. The model works only with OpenAI's harmony response format and does not behave correctly otherwise. You can set the reasoning effort to low, medium or high. The card points to a prompting guide for writing a policy and to the gpt-oss cookbooks for inference.
You write a short policy for a forum and send one reported post with it. The model labels the post against your policy and shows the reasoning behind the label. You can read that reasoning to debug the policy.
Think of the policy as the rulebook you hand the model each time. A first small step is to start with a short rulebook, a few test posts and the low reasoning setting. Read the reasoning to see why each label was chosen.
It interprets your policy at run time, so one model can serve several products without retraining. The raw chain of thought is meant for developers and safety practitioners, and should not be shown to general users. The 20b sibling has 21B parameters, 3.6B active, and lower latency.
Sorts text against a safety policy that you write, and shows its reasoning for each decision. It suits trust and safety teams that filter or label content.
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.
What it is: gpt-oss-safeguard-120b is a safety reasoning model from OpenAI, built on gpt-oss. You give it a written policy and some text, and it judges the text against that policy. It also handles basic safety tasks. It has 117B parameters, with 5.1B active, and fits on a single H100 GPU.
Who it suits: It suits trust and safety teams and developers who need to filter or label content by their own rules. It is meant for safety work, not for general chat.
Industry | Who uses it | How they use it | Example
Trust and safety | Policy and moderation teams | Label posts and messages against a written policy | Offline labeling of a batch of reported posts
Software | AI product developers | Filter what goes into and out of a language model | Checking a chatbot's inputs and replies against house rules
Online platforms | Platform safety engineers | Tag content as it arrives | Live labeling of new content under a policy
People who want a general assistant should use the regular gpt-oss models instead.
Where to use it:
Use case | Who | What they get back
Input and output filtering | AI developers | A policy decision on each prompt or reply
Online content labeling | Platform teams | Labels on content as it arrives
Offline labeling | Trust and safety teams | Labels on stored content, with the reasoning shown
How to set it up:
pip install -U huggingface_hub
hf download openai/gpt-oss-safeguard-120b
How to use it: Write your policy, then send it with the text to judge. The model works only with OpenAI's harmony response format and does not behave correctly otherwise. You can set the reasoning effort to low, medium or high. The card points to a prompting guide for writing a policy and to the gpt-oss cookbooks for inference.
Example: You write a short policy for a forum and send one reported post with it. The model labels the post against your policy and shows the reasoning behind the label. You can read that reasoning to debug the policy.
For newcomers and experts: Think of the policy as the rulebook you hand the model each time. A first small step is to start with a short rulebook, a few test posts and the low reasoning setting. Read the reasoning to see why each label was chosen.
It interprets your policy at run time, so one model can serve several products without retraining. The raw chain of thought is meant for developers and safety practitioners, and should not be shown to general users. The 20b sibling has 21B parameters, 3.6B active, and lower latency.
You write a safety policy
Policy and content go in, in harmony format
Model reasons about the content
Label and reasoning come back
Good to know:
Title: Total parameters in billions
gpt-oss-safeguard-120b: 117 billion
gpt-oss-safeguard-20b: 21 billion
Download its files with Hugging Face's command line:
pip install -U huggingface_hub
hf download openai/gpt-oss-safeguard-120b
Then follow the usage in its model card. Nothing in this kit runs by itself.
README.md 5 KB
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-120b This page | 16,400 | 128 | 117B | apache-2.0 | |
| gpt-oss-20bShares gpt_oss · mxfp4 · 8-bit | 5,685,450 | 5,299 | 20.9B | apache-2.0 | |
| gpt-oss-120bShares gpt_oss · mxfp4 · 8-bit | 2,435,242 | 5,509 | 117B | apache-2.0 | |
| gpt-oss-safeguard-20bShares gpt_oss · mxfp4 · 8-bit | 24,325 | 293 | 21.5B | apache-2.0 | |
| gpt-oss-120b-GGUFShares gpt_oss · mxfp4 · text-generation | 81,029 | 303 | 117B | apache-2.0 | |
| Qwen3.8-27B-Quark-AWQ-MXFP4Shares mxfp4 · 8-bit · conversational | 40,258 | 44 | 15.6B | apache-2.0 |
Posts and comments that name this project on Hacker News, Bluesky, X, Reddit, Facebook and YouTube, found by a weekly search and on Trendshift. Mindket doesn’t write or check them. Open one to read it where it was posted.
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